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Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS? To explore the answer, we give a thorough review of data representations in CSS for both text and network. Specifically, we summarize existing representations into two schemes, namely symbol-based and embedding-based representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.


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From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

Show Author's information Huimin Chen1Cheng Yang2Xuanming Zhang3Zhiyuan Liu3( )Maosong Sun3Jianbin Jin1( )
School of Journalism and Communication, Tsinghua University, Beijing 100084, China
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Abstract

Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS? To explore the answer, we give a thorough review of data representations in CSS for both text and network. Specifically, we summarize existing representations into two schemes, namely symbol-based and embedding-based representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

Keywords:

Computational Social Science (CSS), symbol-based representation, embedding-based representation, social network
Received: 14 April 2021 Revised: 26 June 2021 Accepted: 02 July 2021 Published: 23 August 2021 Issue date: June 2021
References(461)
1

D. M. J. Lazer, A. Pentland, D. J. Watts, S. Aral, S. Athey, N. Contractor, D. Freelon, S. Gonzalez-Bailon, G. King, H. Margetts, et al., Computational social science: Obstacles and opportunities, Science, vol. 369, no. 6507, pp. 1060–1062, 2020.

2

J. Zhang, W. Wang, F. Xia, Y. R. Lin, and H. H. Tong, Data-driven computational social science: A survey, Big Data Res., vol. 21, p. 100145, 2020.

3

D. Lazer, A. Pentland, L. Adamic, S. Aral, A. L. Barabási, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, et al., Computational social science, Science, vol. 323, no. 5915, pp. 721–723, 2009.

4

M. Nadhom and P. Loskot, Survey of public data sources on the Internet usage and other Internet statistics, Data Brief, vol. 18, pp. 1914–1929, 2018.

5

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798–1828, 2013.

6

Z. S. Harris, Distributional structure, WORD, vol. 10, no. 2&3, pp. 146–162, 1954.

DOI
7

C. E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, 1948.

8

G. Salton, E. A Fox, and H. Wu, Extended Boolean information retrieval, Commun. ACM, vol. 26, no. 11, pp. 1022–1036, 1983.

9
J. W. Pennebaker, M. E. Francis, and R. J. Booth, Linguistic Inquiry and Word Count: LIWC 2001. Mahway, NJ, USA: Lawrence Erlbaum Associates, 2001.
10

P. S. Dodds, E. M. Clark, S. Desu, M. R. Frank, A. J. Reagan, J. R. Williams, L. Mitchell, K. D. Harris, I. M. Kloumann, J. P. Bagrow, et al., Human language reveals a universal positivity bias, Proc. Natl. Acad. Sci. USA, vol. 112, no. 8, pp. 2389–2394, 2015.

11

A. Lenci, Distributional models of word meaning, Ann. Rev. Linguist., vol. 4, pp. 151–171, 2018.

12

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, Indexing by latent semantic analysis, J. Am. Soc. Inf. Sci., vol. 41, no. 6, pp. 391–407, 1990.

DOI
13
J. Pennington, R. Socher, and C. D. Manning, Glove: Global vectors for word representation, in Proc. 2014 Conf. on Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1532−1543.https://doi.org/10.3115/v1/D14-1162
DOI
14
M. Sahlgren, An introduction to random indexing, in Proc. Methods and Applications of Semantic Indexing Workshop at the 7th Int. Conf. on Terminology and Knowledge Engineering, doi: 10.1111/j.1749-6632.1996.tb21128.x.
15
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, in Proc. 27th Ann. Conf. on Neural Information Processing Systems 2013, Lake Tahoe, NV, USA, 2013, pp. 3111−3119.
16
F. Morin and Y. Bengio, Hierarchical probabilistic neural network language model, in Proc. 10th Int. Workshop on Artificial Intelligence and Statistics, Barbados, 2005, pp. 246−252.
17
A. Mnih and Y. W. Teh, A fast and simple algorithm for training neural probabilistic language models, in Proc. 29th Int. Conf. on Machine Learning, Edinburgh, UK, 2012, pp. 1751−1759.
18

O. Levy, Y. Goldberg, and I. Dagan, Improving distributional similarity with lessons learned from word Embeddings, Trans. Assoc. Comput. Linguistics, vol. 3, pp. 211–225, 2015.

19

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent Dirichlet allocation, J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.

20

M. E. Roberts, B. M. Stewart, D. Tingley, C. Lucas, J. Leder-Luis, S. K. Gadarian, B. Albertson, and D. G. Rand, Structural topic models for open-ended survey responses, Am. J. Polit. Sci., vol. 58, no. 4, pp. 1064–1082, 2014.

21

A. Baddeley, Working memory, Science, vol. 255, no. 5044, pp. 556–559, 1992.

22
W. James, F Burkhardt, F. Bowers, and I. K. Skrupskelis, The Principles of Psychology. London, UK: Macmillan, 1890.https://doi.org/10.1037/10538-000
DOI
23
N. Kalchbrenner, E. Grefenstette, and P. Blunsom, A convolutional neural network for modelling sentences, in Proc. 52nd Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA, 2014, pp. 655−665.https://doi.org/10.3115/v1/P14-1062
DOI
24
T. Mikolov, M. Karafiát, L. Burget, J. Cernocký, and S. Khudanpur, Recurrent neural network based language model, in Proc. 11th Ann. Conf. of the Int. Speech Communication Association, Makuhari, Japan, 2010, pp. 1045−1048.
25
K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation, in Proc. 2014 Conf. on Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1724−1734.https://doi.org/10.3115/v1/D14-1179
DOI
26

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.

27
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, in Proc. Ann. Conf. on Neural Information Processing Systems 2017, Long Beach, CA, USA, 2017, pp. 5998−6008.
28
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, in Proc. Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA, 2019, pp. 4171−4186.
29
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, et al., Languagemodels are few-shot learners, in Proc. of 34th Annual Conference on Neural Information Processing Systems, https://www.researchgate.net/publication/341724146_Language_Models_are_Few-Shot_Learners, 2020.
30
B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online learning of social representations, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York City, NY, USA, 2014, pp. 701-710.https://doi.org/10.1145/2623330.2623732
DOI
31
M. Belkin and P. Niyogi, Laplacian Eigenmaps and spectral techniques for embedding and clustering, in Proc. Neural Information Processing Systems: Natural and Synthetic, Vancouver, Canada, 2001, pp. 585-591.
32

F. Fouss, A. Pirotte, J. M. Renders, and M. Saerens, Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation, IEEE Trans. Knowl. Data Eng., vol. 19, no. 3, pp. 355–369, 2007.

33
R. Andersen, F. R. K. Chung, and K. J. Lang, Local graph partitioning using Pagerank vectors, in Proc. 47th Ann. IEEE Symp. on Foundations of Computer Science, Berkeley, CA, USA, 2006, pp. 475−486.https://doi.org/10.1109/FOCS.2006.44
DOI
34
A. Grover and J. Leskovec, Node2Vec: Scalable feature learning for networks, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 855-864.https://doi.org/10.1145/2939672.2939754
DOI
35
J. Tang, M. Qu, M. Z. Wang, M. Zhang, J. Yan, and Q. Z. Mei, LINE: Large-scale information network embedding, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 1067-1077.https://doi.org/10.1145/2736277.2741093
DOI
36
T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, in Proc. 5th Int. Conf. on Learning Representations, Toulon, France, 2017.
37

C. Yang, Ontogeny and phylogeny of language, Proc. Natl. Acad. Sci. USA, vol. 110, no. 16, pp. 6324–6327, 2013.

38
J. B. Michel, Y. K. Shen, A. P. Aiden, A. Veres, M. K. Gray, The Google Books Team, J. P. Pickett, D. Hoiberg, D. Clancy, P. Norvig, et al., Quantitative analysis of culture using millions of digitized books, Science, vol. 331, no. 6014, pp. 176−182, 2011.https://doi.org/10.1126/science.1199644
DOI
39

E. E. Bruch and M. E. J. Newman, Aspirational pursuit of mates in online dating markets, Sci. Adv., vol. 4, no. 8, p. eaap9815, 2018.

40

A. Lupia, S. Soroka, and A. Beatty, What does congress want from the National Science Foundation? A content analysis of remarks from 1995 to 2018 Sci. Adv., vol. 6, no. 33, p. eaaz6300, 2020.

41

K. Sheshadri and M. P. Singh, The public and legislative impact of hyperconcentrated topic news, Sci. Adv., vol. 5, no. 8, p. eaat8296, 2019.

42

S. A. Golder and M. W. Macy, Diurnal and seasonal mood vary with work, sleep, and Daylength across diverse cultures, Science, vol. 333, no. 6051, pp. 1878–1881, 2011.

43

M. Alanyali, H. S. Moat, and T. Preis, Quantifying the relationship between financial news and the stock market, Sci. Rep., vol. 3, no. 1, p. 3578, 2013.

44

A. G. Huth, W. A. de Heer, T. L. Griffiths, F. E. Theunissen, and J. L. Gallant, Natural speech reveals the semantic maps that tile human cerebral cortex, Nature, vol. 532, no. 7600, pp. 453–458, 2016.

45

M. Stella, E. Ferrara, and M. De Domenico, Bots increase exposure to negative and inflammatory content in online social systems, Proc. Natl. Acad. Sci. USA, vol. 115, no. 49, pp. 12435–12440, 2018.

46

C. Ramiro, M. Srinivasan, B. C. Malt, and Y. Xu, Algorithms in the historical emergence of word senses, Proc. Natl. Acad. Sci. USA, vol. 115, no. 10, pp. 2323–2328, 2018.

47

J. C. Jackson, J. Watts, T. R. Henry, J. M. List, R. Forkel, P. J. Mucha, S. J. Greenhill, R. D. Gray, and K. A. Lindquist, Emotion semantics show both cultural variation and universal structure, Science, vol. 366, no. 6472, pp. 1517–1522, 2019.

48

A. Rule, J. P. Cointet, and P. S. Bearman, Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790-2014, Proc. Natl. Acad. Sci. USA, vol. 112, no. 35, pp. 10837–10844, 2015.

49

A. Bovet, F. Morone, and H. A. Makse, Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald trump, Sci. Rep., vol. 8, no. 1, p. 8673, 2018.

50

D. T. Citron and P. Ginsparg, Patterns of text reuse in a scientific corpus, Proc. Natl. Acad. Sci. USA, vol. 112, no. 1, pp. 25–30, 2015.

51

J. C. Eichstaedt, R. J. Smith, R. M. Merchant, L. H. Ungar, P. Crutchley, D. Preoţuc-Pietro, D. A. Asch, and H. A. Schwartz, Facebook language predicts depression in medical records, Proc. Natl. Acad. Sci. USA, vol. 115, no. 44, pp. 11203–11208, 2018.

52

J. Green, J. Edgerton, D. Naftel, K. Shoub, and S. J. Cranmer, Elusive consensus: Polarization in elite communication on the COVID-19 pandemic, Sci. Adv., vol. 6, no. 28, p. eabc2717, 2020.

53

E. Bakshy, S. Messing, and L. A. Adamic, Exposure to ideologically diverse news and opinion on Facebook, Science, vol. 348, no. 6239, pp. 1130–1132, 2015.

54

S. T. Piantadosi, H. Tily, and E. Gibson, Word lengths are optimized for efficient communication, Proc. Natl. Acad. Sci. USA, vol. 108, no. 9, pp. 3526–3529, 2011.

55

R. Futrell, K. Mahowald, and E. Gibson, Large-scale evidence of dependency length minimization in 37 languages, Proc. Natl. Acad. Sci. USA, vol. 112, no. 33, pp. 10336–10341, 2015.

56

K. N. Jordan, J. Sterling, J. W. Pennebaker, and R. L. Boyd, Examining long-term trends in politics and culture through language of political leaders and cultural institutions, Proc. Natl. Acad. Sci. USA, vol. 116, no. 9, pp. 3476–3481, 2019.

57

M. R. Frank, L. Mitchell, P. S. Dodds, and C. M. Danforth, Happiness and the patterns of life: A study of Geolocated tweets, Sci. Rep., vol. 3, no. 1, p. 2625, 2013.

58

Y. Kryvasheyeu, H. H. Chen, N. Obradovich, E. Moro, P. V. Hentenryck, J. Fowler, and M. Cebrian, Rapid assessment of disaster damage using social media activity, Sci. Adv., vol. 2, no. 3, p. e1500779, 2016.

59

S. Klingenstein, T. Hitchcock, and S. DeDeo, The civilizing process in London’s Old Bailey, Proc. Natl. Acad. Sci. USA, vol. 111, no. 26, pp. 9419–9424, 2014.

60

C. Catalini, N. Lacetera, and A. Oettl, The incidence and role of negative citations in science, Proc. Natl. Acad. Sci. USA, vol. 112, no. 45, pp. 13823–13826, 2015.

61

R. L. Boyd, K. G. Blackburn, and J. W. Pennebaker, The narrative arc: Revealing core narrative structures through text analysis, Sci. Adv., vol. 6, no. 32, p. eaba2196, 2020.

62

J. M. Hughes, N. J. Foti, D. C. Krakauer, and D. N. Rockmore, Quantitative patterns of stylistic influence in the evolution of literature, Proc. Natl. Acad. Sci. USA, vol. 109, no. 20, pp. 7682–7686, 2012.

63

A. D. I. Kramer, J. E. Guillory, and J. T. Hancock, Experimental evidence of massive-scale emotional contagion through social networks, Proc. Natl. Acad. Sci. USA, vol. 111, no. 24, pp. 8788–8790, 2014.

64

W. J. Brady, J. A. Wills, J. T. Jost, J. A. Tucker, and J. J. Van Bavel, Emotion shapes the diffusion of moralized content in social networks, Proc. Natl. Acad. Sci. USA, vol. 114, no. 28, pp. 7313–7318, 2017.

65

N. M. Jones, R. R. Thompson, C. D. Schetter, and R. C. Silver, Distress and rumor exposure on social media during a campus lockdown, Proc. Natl. Acad. Sci. USA, vol. 114, no. 44, pp. 11663–11668, 2017.

66

M. Del Vicario, G. Vivaldo, A. Bessi, F. Zollo, A. Scala, G. Caldarelli, and W. Quattrociocchi, Echo chambers: Emotional contagion and group polarization on facebook, Sci. Rep., vol. 6, no. 1, p. 37825, 2016.

67

M. Alizadeh, J. N. Shapiro, C. Buntain, and J. A. Tucker, Content-based features predict social media influence operations, Sci. Adv., vol. 6, no. 30, p. eabb5824, 2020.

68

N. Garg, L. Schiebinger, D. Jurafsky, and J. Zou, Word embeddings quantify 100 years of gender and ethnic stereotypes, Proc. Natl. Acad. Sci. USA, vol. 115, no. 16, pp. E3635–E3644, 2018.

69

A. Caliskan, J. J. Bryson, and A. Narayanan, Semantics derived automatically from language corpora contain human-like biases, Science, vol. 356, no. 6334, pp. 183–186, 2017.

70

E. Sivak and I. Smirnov, Parents mention sons more often than daughters on social media, Proc. Natl. Acad. Sci. USA, vol. 116, no. 6, pp. 2039–2041, 2019.

71

A. Gerow, Y. N. Hu, J. Boyd-Graber, D. M. Blei, and J. A. Evans, Measuring discursive influence across scholarship, Proc. Natl. Acad. Sci. USA, vol. 115, no. 13, pp. 3308–3313, 2018.

72

E. Bokányi, D. Kondor, L. Dobos, T. Sebök, J. Stéger, I. Csabai, and G. Vattay, Race, religion and the city: Twitter word frequency patterns reveal dominant demographic dimensions in the United States, Palgrave Commun., vol. 2, no. 1, p. 16010, 2016.

73

J. Farrell, Network structure and influence of the climate change counter-movement, Nat. Climate Change, vol. 6, no. 4, pp. 370–374, 2016.

74

B. C. Roy, M. C. Frank, P. DeCamp, M. Miller, and D. Roy, Predicting the birth of a spoken word, Proc. Natl. Acad. Sci. USA, vol. 112, no. 41, pp. 12663–12668, 2015.

75

C. Curme, T. Preis, H. E. Stanley, and H. S. Moat, Quantifying the semantics of search behavior before stock market moves, Proc. Natl. Acad. Sci. USA, vol. 111, no. 32, pp. 11600–11605, 2014.

76

J. Farrell, Corporate funding and ideological polarization about climate change, Proc. Natl. Acad. Sci. USA, vol. 113, no. 1, pp. 92–97, 2016.

77

K. Jaidka, S. Giorgi, H. A. Schwartz, M. L. Kern, L. H. Ungar, and J. C. Eichstaedt, Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods, Proc. Natl. Acad. Sci. USA, vol. 117, no. 19, pp. 10165–10171, 2020.

78

E. Fetaya, Y. Lifshitz, E. Aaron, and S. Gordin, Restoration of fragmentary Babylonian texts using recurrent neural networks, Proc. Natl. Acad. Sci. USA, vol. 117, no. 37, pp. 22743–22751, 2020.

79

M. Hahn, D. Jurafsky, and R. Futrell, Universals of word order reflect optimization of grammars for efficient communication, Proc. Natl. Acad. Sci. USA, vol. 117, no. 5, pp. 2347–2353, 2020.

80

M. Mooijman, J. Hoover, Y. Lin, H. Ji, and M. Dehghani, Moralization in social networks and the emergence of violence during protests, Nat. Hum. Behav., vol. 2, no. 6, pp. 389–396, 2018.

81

N. Grinberg, K. Joseph, L. Friedland, B. Swire-Thompson, and D. Lazer, Fake news on Twitter during the 2016 U.S. presidential election, Science, vol. 363, no. 6425, pp. 374–378, 2019.

82

M. X. Li, Z. Q. Jiang, W. J. Xie, S. Miccichè, M. Tumminello, W. X. Zhou, and R. N. Mantegna, A comparative analysis of the statistical properties of large mobile phone calling networks, Sci. Rep., vol. 4, no. 1, p. 5132, 2014.

83

K. M. Turetsky, V. Purdie-Greenaway, J. E. Cook, J. P. Curley, and G. L. Cohen, A psychological intervention strengthens students’ peer social networks and promotes persistence in STEM, Sci. Adv., vol. 6, no. 45, p. eaba9221, 2020.

84

C. L. Apicella, F. W. Marlowe, J. H. Fowler, and N. A. Christakis, Social networks and cooperation in hunter-gatherers, Nature, vol. 481, no. 7382, pp. 497–501, 2012.

85

J. D. Boardman, B. W. Domingue, and J. M. Fletcher, How social and genetic factors predict friendship networks, Proc. Natl. Acad. Sci. USA, vol. 109, no. 43, pp. 17377–17381, 2012.

86

A. Clauset, S. Arbesman, and D. B. Larremore, Systematic inequality and hierarchy in faculty hiring networks, Sci. Adv., vol. 1, no. 1, p. e1400005, 2015.

87

C. M. Trujillo and T. M. Long, Document co- citation analysis to enhance transdisciplinary research, Sci. Adv., vol. 4, no. 1, p. e1701130, 2018.

88

A. Wesolowski, N. Eagle, A. J. Tatem, D. L. Smith, A. M. Noor, R. W. Snow, and C. O. Buckee, Quantifying the impact of human mobility on malaria, Science, vol. 338, no. 6104, pp. 267–270, 2012.

89

H. H. Jo, J. Saramäki, R. I. M. Dunbar, and K. Kaski, Spatial patterns of close relationships across the lifespan, Sci. Rep., vol. 4, no. 1, p. 6988, 2014.

90

C. Parkinson, A. M. Kleinbaum, and T. Wheatley, Similar neural responses predict friendship, Nat. Commun., vol. 9, no. 1, p. 332, 2018.

91

L. F. Wu, D. S. Wang, and J. A. Evans, Large teams develop and small teams disrupt science and technology, Nature, vol. 566, no. 7744, pp. 378–382, 2019.

92

P. S. Park, J. E. Blumenstock, and M. W. Macy, The strength of long-range ties in population-scale social networks, Science, vol. 362, no. 6421, pp. 1410–1413, 2018.

93

D. Garcia, Leaking privacy and shadow profiles in online social networks, Sci. Adv., vol. 3, no. 8, p. e1701172, 2017.

94

M. Schich, C. M. Song, Y. Y. Ahn, A. Mirsky, M. Martino, A. L. Barabási, and D. Helbing, A network framework of cultural history, Science, vol. 345, no. 6196, pp. 558–562, 2014.

95

P. Manrique, Z. F. Cao, A. Gabriel, J. Horgan, P. Gill, H. Qi, E. M. Restrepo, D. Johnson, S. Wuchty, C. M. Song, et al., Women’s connectivity in extreme networks, Sci. Adv., vol. 2, no. 6, p. e1501742, 2016.

96

L. Reino, R. Figueira, P. Beja, M. B. Araújo, C. Capinha, and D. Strubbe, Networks of global bird invasion altered by regional trade ban, Sci. Adv., vol. 3, no. 11, p. e1700783, 2017.

97

J. P. Hart, J. Birch, and C. G. St-Pierre, Effects of population dispersal on regional signaling networks: An example from northern Iroquoia, Sci. Adv., vol. 3, no. 8, p. e1700497, 2017.

98

S. P. Fraiberger, R. Sinatra, M. Resch, C. Riedl, and A. L. Barabási, Quantifying reputation and success in art, Science, vol. 362, no. 6416, pp. 825–829, 2018.

99

S. Pei, L. Muchnik, J. S. J. Andrade Jr, Z. M. Zheng, and H. A. Makse, Searching for superspreaders of information in real-world social media, Sci. Rep., vol. 4, no. 1, p. 5547, 2014.

100

M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan, Hiding individuals and communities in a social network, Nat. Hum. Behav., vol. 2, no. 2, pp. 139–147, 2018.

101

M. M. Dankulov, R. Melnik, and B. Tadić, The dynamics of meaningful social interactions and the emergence of collective knowledge, Sci. Rep., vol. 5, no. 1, p. 12197, 2015.

102

A. Asikainen, G. Iñiguez, J. Ureña-Carrión, K. Kaski, and M. Kivelä, Cumulative effects of triadic closure and homophily in social networks, Sci. Adv., vol. 6, no. 19, p. eaax7310, 2020.

103

B. Charoenwong, A. Kwan, and V. Pursiainen, Social connections with COVID-19-affected areas increase compliance with mobility restrictions, Sci. Adv., vol. 6, no. 47, p. eabc3054, 2020.

104

S. Aral and D. Walker, Identifying influential and susceptible members of social networks, Science, vol. 337, no. 6092, pp. 337–341, 2012.

105

Y. H. Eom and H. H. Jo, Generalized friendship paradox in complex networks: The case of scientific collaboration, Sci. Rep., vol. 4, no. 1, p. 4603, 2014.

106

A. A. Ganin, M. Kitsak, D. Marchese, J. M. Keisler, T. Seager, and I. Linkov, Resilience and efficiency in transportation networks, Sci. Adv., vol. 3, no. 12, p. e1701079, 2017.

107

F. A. Massucci, J. Wheeler, R. Beltrán-Debón, J. Joven, M. Sales-Pardo, and R. Guimerà, Inferring propagation paths for sparsely observed perturbations on complex networks, Sci. Adv., vol. 2, no. 10, p. e1501638, 2016.

108

X. Teng, S. Pei, F. Morone, and H. A. Makse, Collective influence of multiple spreaders evaluated by tracing real information flow in large-scale social networks, Sci. Rep., vol. 6, no. 1, p. 36043, 2016.

109

M. Medo, M. S. Mariani, A. Zeng, and Y. C. Zhang, Identification and impact of discoverers in online social systems, Sci. Rep., vol. 6, no. 1, p. 34218, 2016.

110

L. Kovanen, K. Kaski, J. Kertész, and J. Saramäki, Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences, Proc. Natl. Acad. Sci. USA, vol. 110, no. 45, pp. 18070–18075, 2013.

111

P. Expert, T. S. Evans, V. D. Blondel, and R. Lambiotte, Uncovering space-independent communities in spatial networks, Proc. Natl. Acad. Sci. USA, vol. 108, no. 19, pp. 7663–7668, 2011.

112
S. H. Yang, B. Long, A. Smola, S. H. Yang, B. Long, A. Smola, N. Sadagopan, Z. H. Zheng, and H. Zha, Like like alike: Joint friendship and interest propagation in social networks, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 537−546.https://doi.org/10.1145/1963405.1963481
DOI
113
M. Sachan, D. Contractor, M. Sachan, D. Contractor, T. A. Faruquie, and L. V. Subramaniam, Using content and interactions for discovering communities in social networks, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 331−340.https://doi.org/10.1145/2187836.2187882
DOI
114

M. Kosinski, D. Stillwell, and T. Graepel, Private traits and attributes are predictable from digital records of human behavior, Proc. Natl. Acad. Sci. USA, vol. 110, no. 15, pp. 5802–5805, 2013.

115
G. Wang, Y. C. Zhao, X. X. Shi, and P. S. Yu, Magnet community identification on social networks, in Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 588−596.https://doi.org/10.1145/2339530.2339627
DOI
116
D. Q. Yang, B. Q. Qu, J. Yang, and P. Cudre-Mauroux, Revisiting user mobility and social relationships in LBSNs: A hypergraph embedding approach, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 2147−2157.https://doi.org/10.1145/3308558.3313635
DOI
117
C. Zhang, K. Y. Zhang, Q. Yuan, H. R. Peng, Y. Zheng, T. Hanratty, S. W. Wang, and J. W. Han, Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning, in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 361−370.https://doi.org/10.1145/3038912.3052601
DOI
118
W. Q. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. L. Tang, and D. W. Yin, Graph neural networks for social recommendation, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 417−426.https://doi.org/10.1145/3308558.3313488
DOI
119
Q. T. Wu, H. R. Zhang, X. F. Gao, P. He, P. Weng, H. Gao, and G. H. Chen, Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 2091−2102.
120

C. W. J. Granger, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, vol. 37, no. 3, pp. 424–438, 1969.

121

Q. S. Zhang and S. C. Zhu, Visual interpretability for deep learning: A survey, Front. Inf. Technol. Electr. Eng., vol. 19, no. 1, pp. 27–39, 2018.

122

Y. Belinkov and J. Glass, Analysis methods in neural language processing: A survey, Trans. Assoc. Comput. Linguistics, vol. 7, pp. 49–72, 2019.

123
M. D, Zeiler and R. Fergus, Visualizing and understanding convolutional networks, in Proc. 13th European Conf. on Computer Vision, Zurich, Switzerland, 2014, pp. 818−833.https://doi.org/10.1007/978-3-319-10590-1_53
DOI
124
Y. H. Liu, M. Ott, N. Goyal, J. F. Du, M. Joshi, D. Q. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, RoBERta: A robustly optimized BERT pretraining approach, arXiv preprint arXiv: 1907.11692, 2019.
125

J. Zhou, G. Q. Cui, S. D. Hu, Z. Y. Zhang, C. Yang, Z. Y. Liu, L. F. Wang, C. C. Li, and M. S. Sun, Graph neural networks: A review of methods and applications, AI Open, vol. 1, pp. 57–81, 2020.

126
L. F. Wu, Y. Chen, H. Ji, and Y. Y. Li, Deep learning on graphs for natural language processing, in Proc. 2021 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials, Seattle, WA, USA, 2021, pp. 11−14.
127
R. Ying, R. N. He, K. F. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, Graph convolutional neural networks for web-scale recommender systems, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 974-983.https://doi.org/10.1145/3219819.3219890
DOI
128

A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, et al., Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion, vol. 58, pp. 82–115, 2020.

129
C. Boididou, S. Papadopoulos, Y. Kompatsiaris, S. Schifferes, and N. Newman, Challenges of computational verification in social multimedia, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 743−748.https://doi.org/10.1145/2567948.2579323
DOI
130
Z. Zhao, P. Resnick, and Q. Z. Mei, Enquiring minds: Early detection of rumors in social media from enquiry posts, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 1395−1405.https://doi.org/10.1145/2736277.2741637
DOI
131
S. Kumar, R. West, and J. Leskovec, Disinformation on the web: Impact, characteristics, and detection of Wikipedia hoaxes, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 591−602.https://doi.org/10.1145/2872427.2883085
DOI
132
K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum, Where the truth lies: Explaining the credibility of emerging claims on the web and social media, in Proc. 26th Int. Conf. on World Wide Web Companion, Perth, Australia, 2017, pp. 1003−1012.https://doi.org/10.1145/3041021.3055133
DOI
133
F. Yang, Y. Liu, X. H. Yu, and M. Yang, Automatic detection of rumor on Sina Weibo, in Proc. ACM SIGKDD Workshop on Mining Data Semantics, New York, NY, USA, 2012, p. 13.https://doi.org/10.1145/2350190.2350203
DOI
134
G. Bhatt, A. Sharma, S. Sharma, A. Sharma, A. Nagpal, B. Raman, and A. Mittal, Combining neural, statistical and external features for fake news stance identification, in Proc. Proc.Web Conf. 2018, Lyon, France, 2018, pp. 1353−1357.https://doi.org/10.1145/3184558.3191577
DOI
135
A. Gupta, H. Lamba, P. Kumaraguru, and A. Joshi, Faking sandy: Characterizing and identifying fake images on twitter during hurricane sandy, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 729−736.https://doi.org/10.1145/2487788.2488033
DOI
136
L. Flekova, O. Ferschke, and I. Gurevych, What makes a good biography?: Multidimensional quality analysis based on Wikipedia article feedback data, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 855−866.https://doi.org/10.1145/2566486.2567972
DOI
137
P. Badjatiya, S. Gupta, M. Gupta, and V. Varma, Deep learning for hate speech detection in tweets, in Proc. 26th Int. Conf. on World Wide Web Companion, Perth, Australia, 2017, pp. 759−760.https://doi.org/10.1145/3041021.3054223
DOI
138
Z. Savvas, B. Bradlyn, E. De Cristofaro, H. Kwak, M. Sirivianos, G. Stringhini, and J. Blackburn, What is gab: A bastion of free speech or an alt-right echo chamber, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 1007−1014.
139
C. Nobata, J. R. Tetreault, A. Thomas, Y. Mehdad, and Y. Chang, Abusive language detection in online user content, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 145−153.https://doi.org/10.1145/2872427.2883062
DOI
140
D. Chatzakou, N. Kourtellis, J. Blackburn, E. De Cristofaro, G. Stringhini, and A. Vakali, Measuring #Gamergate: A tale of hate, sexism, and bullying, in Proc. 26th Int. Conf. on World Wide Web Companion, Perth, Australia, 2017, pp. 1285−1290.https://doi.org/10.1145/3041021.3053890
DOI
141
C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer, BotOrNot: A system to evaluate social bots, in Proc. 25th Int. Conf. Companion on World Wide Web, Montreal, Canada, 2016, pp. 273−274.https://doi.org/10.1145/2872518.2889302
DOI
142
S. Kumar, J. Cheng, J. Leskovec, and V. S. Subrahmanian, An army of me: Sockpuppets in online discussion communities, in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 857−866.https://doi.org/10.1145/3038912.3052677
DOI
143
R. S. Portnoff, S. Afroz, G. Durrett, J. K. Kummerfeld, T. Berg-Kirkpatrick, D. McCoy, K. Levchenko, and V. Paxson, Tools for automated analysis of cybercriminal markets, in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 657−666.https://doi.org/10.1145/3038912.3052600
DOI
144
O. Goga, H. Lei, S. H. K. Parthasarathi, G. Friedland, R. Sommer, and R. Teixeira, Exploiting innocuous activity for correlating users across sites, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 447−458.https://doi.org/10.1145/2488388.2488428
DOI
145
P. Jain, P. Kumaraguru, and A. Joshi, @i seek ‘fb. me’ identifying users across multiple online social networks, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 1259−1268.https://doi.org/10.1145/2487788.2488160
DOI
146
M. Imran, S. Elbassuoni, C. Castillo, F. Diaz, and P. Meier, Practical extraction of disaster-relevant information from social media, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 1021−1024.https://doi.org/10.1145/2487788.2488109
DOI
147
M. Imran, C. Castillo, J. Lucas, P. Meier, and S. Vieweg, AIDR: Artificial intelligence for disaster response, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 159−162.https://doi.org/10.1145/2567948.2577034
DOI
148
S. Cresci, M. Tesconi, A. Cimino, and F. Dell’Orletta, A linguistically-driven approach to cross-event damage assessment of natural disasters from social media messages, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 1195−1200.https://doi.org/10.1145/2740908.2741722
DOI
149
P. Bramsen, M. Escobar-Molano, A. Patel, and R. Alonso, Extracting social power relationships from natural language, in Proc. 49th Ann. Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, Portland, OR, USA, 2011, pp. 773−782.
150
L. Yang, T. Sun, M. Zhang, and Q. Z. Mei, We know what @you #tag: Does the dual role affect hashtag adoption? in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 261−270.https://doi.org/10.1145/2187836.2187872
DOI
151
J. Lehmann, B. Gonçalves, J. J. Ramasco, and C. Cattuto, Dynamical classes of collective attention in twitter, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 251−260.https://doi.org/10.1145/2187836.2187871
DOI
152
B. Fu, J. L. Lin, L. Li, C. Faloutsos, J. I. Hong, and N. M. Sadeh, Why people hate your app: Making sense of user feedback in a mobile app store, in Proc. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 1276−1284.https://doi.org/10.1145/2487575.2488202
DOI
153
D. Hovy, A. Johannsen, and A. Søgaard, User review sites as a resource for large-scale sociolinguistic studies, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 452−461.https://doi.org/10.1145/2736277.2741141
DOI
154
C. Danescu-Niculescu-Mizil, M. Sudhof, D. Jurafsky, J. Leskovec, and C. Potts, A computational approach to politeness with application to social factors, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 250−259.
155
C. Danescu-Niculescu-Mizil, J. Cheng, J. Kleinberg, and L. Lee, You had me at hello: How phrasing affects memorability, in Proc. 50th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jeju Island, Republic of Korea, 2012, pp. 892−901.
156
C. H. Tan, L. Lee, and B. Pang, The effect of wording on message propagation: Topic- and author- controlled natural experiments on Twitter, in Proc. 52nd Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA, 2014, pp. 175-185.https://doi.org/10.3115/v1/P14-1017
DOI
157
C. Hube and B. Fetahu, Detecting biased statements in Wikipedia, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 1779−1786.https://doi.org/10.1145/3184558.3191640
DOI
158
X. T. Chen, M. D. Sykora, T. W. Jackson, and S. Elayan, What about mood swings: Identifying depression on twitter with temporal measures of emotions, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 1653−1660.https://doi.org/10.1145/3184558.3191624
DOI
159
J. P. Chang, J. Cheng, and C. Danescu-Niculescu-Mizil, Don’t let me be misunderstood: Comparing intentions and perceptions in online discussions, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 2066−2077.https://doi.org/10.1145/3366423.3380273
DOI
160

M. L Kern, P. X. McCarthy, D. Chakrabarty, and M. A. Rizoiu, Social media-predicted personality traits and values can help match people to their ideal jobs, Proc. Natl. Acad. Sci. USA, vol. 116, no. 52, pp. 26459–26464, 2019.

161
Y. Ikawa, M. Enoki, and M. Tatsubori, Location inference using microblog messages, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 687−690.https://doi.org/10.1145/2187980.2188181
DOI
162
K. M. Ryoo and S. Moon, Inferring twitter user locations with 10 km accuracy, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 643−648.https://doi.org/10.1145/2567948.2579236
DOI
163
B. Wing and J. Baldridge, Simple supervised document geolocation with geodesic grids, in Proc. 49th Ann. Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA, 2011, pp. 955−964.
164
J. Kim, M. Cha, and T. Sandholm, Socroutes: Safe routes based on tweet sentiments, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 179−182.https://doi.org/10.1145/2567948.2577023
DOI
165
D. Preoţiuc-Pietro, Y. Liu, D. Hopkins, and L. Ungar, Beyond binary labels: Political ideology prediction of twitter users, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 729−740.https://doi.org/10.18653/v1/P17-1068
DOI
166
C. Burfoot, S. Bird, and T. Baldwin, Collective classification of congressional floor-debate transcripts, in Proc. 49th Ann. Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA, 2011, pp. 1506-1515.
167
M. Jenders, G. Kasneci, and F. Naumann, Analyzing and predicting viral tweets, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 657-664.https://doi.org/10.1145/2487788.2488017
DOI
168
I. Arous, J. Yang, M. Khayati, and P. Cudré-Mauroux, OpenCrowd: A human-AI collaborative approach for finding social influencers via open-ended answers aggregation, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 1851−1862.https://doi.org/10.1145/3366423.3380254
DOI
169
B. Y. Xie, R. J. Passonneau, L. Wu, and G. G. Creamer, Semantic frames to predict stock price movement, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 873−883.
170
A. Agarwal, B. Y. Xie, I. Vovsha, O. Rambow, and R. Passonneau, Sentiment analysis of twitter data, in Proc. Workshop on Languages in Social Media, Stroudsburg, PA, USA, 2011, pp. 30-38.
171

M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, Sentiment strength detection in short informal text, J. Am. Soc. Inf. Sci. Technol., vol. 61, no. 12, pp. 2544–2558, 2010.

172
S. Ghosh and M. S. Desarkar, Class specific TF-IDF boosting for short-text classification: Application to short-texts generated during disasters, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 1629−1637.https://doi.org/10.1145/3184558.3191621
DOI
173

S. A Mehr, M. Singh, D. Knox, D. M. Ketter, D. Pickens-Jones, S. Atwood, C. Lucas, N. Jacoby, A. A. Egner, E. J. Hopkins, et al., Universality and diversity in human song, Science, vol. 366, no. 6468, p. eaax0868, 2019.

174
P. Singer, F. Lemmerich, R. West, L. Zia, E. Wulczyn, M. Strohmaier, and J. Leskovec, Why we read Wikipedia? in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 1591−1600.https://doi.org/10.1145/3038912.3052716
DOI
175
J. Weerasinghe, B. Flanigan, A. J. Stein, D. McCoy, and R. Greenstadt, The pod people: Understanding manipulation of social media popularity via reciprocity abuse, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 1874−1884.https://doi.org/10.1145/3366423.3380256
DOI
176
W. Y. Wang, E. Mayfield, S. Naidu, and J. Dittmar, Historical analysis of legal opinions with a sparse mixed-effects latent variable model, in Proc. 50th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jeju Island, Republic of Korea, 2012, pp. 740−749.
177
J. Ma, W. Gao, and K. F. Wong, Detect rumor and stance jointly by neural multi-task learning, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 585−593.https://doi.org/10.1145/3184558.3188729
DOI
178
D. Khattar, J. S. Goud, M. Gupta, and V. Varma, MVAE: Multimodal variational autoencoder for fake news detection, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 2915−2921.https://doi.org/10.1145/3308558.3313552
DOI
179
J. Ma, W. Gao, and K. F. Wong, Detect rumors on twitter by promoting information campaigns with generative adversarial learning, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 3049−3055.https://doi.org/10.1145/3308558.3313741
DOI
180
Q. Zhang, A. Lipani, S. S. Liang, and E. Yilmaz, Reply-aided detection of misinformation via bayesian deep learning, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 2333−2343.https://doi.org/10.1145/3308558.3313718
DOI
181
H. Almerekhi, H. Kwak, J. Salminen, and B. J. Jansen, Are these comments triggering? Predicting triggers of toxicity in online discussions, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 3033−3040.https://doi.org/10.1145/3366423.3380074
DOI
182
Z. J. Wang, S. A. Hale, D. I. Adelani, P. A. Grabowicz, T. Hartmann, F. Flöck, and D. Jurgens, Demographic inference and representative population estimates from multilingual social media data, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 2056−2067.https://doi.org/10.1145/3308558.3313684
DOI
183
S. Wilson and R. Mihalcea, Predicting human activities from user-generated content, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2572−2582.https://doi.org/10.18653/v1/P19-1245
DOI
184
J. Q. Pan, R. Bhardwaj, W. Lu, H. L. Chieu, X. H. Pan, and N. Y. Puay, Twitter homophily: Network based prediction of user’s occupation, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2633−2638.https://doi.org/10.18653/v1/P19-1252
DOI
185
A. Ahmed, L. J. Hong, and A. J. Smola, Hierarchical geographical modeling of user locations from social media posts, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 25−36.https://doi.org/10.1145/2488388.2488392
DOI
186
Y. Miura, M. Taniguchi, T. Taniguchi, and T. Ohkuma, Unifying text, metadata, and user network representations with a neural network for Geolocation prediction, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 1260−1272.https://doi.org/10.18653/v1/P17-1116
DOI
187
Q. Yuan, G. Cong, Z. Y. Ma, A. X. Sun, and N. Magnenat-Thalmann, Who, where, when and what: Discover spatio-temporal topics for twitter users, in Proc. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 605−613.https://doi.org/10.1145/2487575.2487576
DOI
188
L. J. Hong, A. Ahmed, S. Gurumurthy, A. J. Smola, and K. Tsioutsiouliklis, Discovering geographical topics in the twitter stream, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 769−778.https://doi.org/10.1145/2187836.2187940
DOI
189
C. Zhang, K. Y. Zhang, Q. Yuan, L. M. Zhang, T. Hanratty, and J. W. Han, Gmove: Group-level mobility modeling using geo-tagged social media, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1305−1314.https://doi.org/10.1145/2939672.2939793
DOI
190
C. Zhang, L. Y. Liu, D. M. Lei, Q. Yuan, H. L. Zhuang, T. Hanratty, and J. W. Han, Triovecevent: Embedding-based online local event detection in geo-tagged tweet streams, in Proc. 23rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 595−604.https://doi.org/10.1145/3097983.3098027
DOI
191
C. Li and D. Goldwasser, Encoding social information with graph convolutional networks forpolitical perspective detection in news media, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2594-2604.https://doi.org/10.18653/v1/P19-1247
DOI
192
P. Stefanov, K. Darwish, A. Atanasov, and P. Nakov, Predicting the topical stance and political leaning of media using tweets, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 527−537.https://doi.org/10.18653/v1/2020.acl-main.50
DOI
193
T. H. Nguyen and K. Shirai, Topic modeling based sentiment analysis on social media for stock market prediction, in Proc. 53rd Ann. Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, pp. 1354−1364.https://doi.org/10.3115/v1/P15-1131
DOI
194
B. O’Connor, B. M. Stewart, and N. A. Smith, Learning to extract international relations from political context, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 1094−1104.
195
O. Tsur, D. Calacci, and D. Lazer, A frame of mind: Using statistical models for detection of framing and agenda setting campaigns, in Proc. 53rd Ann. Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, pp. 1629−1638.https://doi.org/10.3115/v1/P15-1157
DOI
196
G. Da San Martino, S. Shaar, Y. F. Zhang, S. Yu, A. Barrón-Cedeño, and P. Nakov, Prta: A system to support the analysis of propaganda techniques in the news, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics: System Demonstrations, Seattle, WA, USA, 2020, pp. 287−293.https://doi.org/10.18653/v1/2020.acl-demos.32
DOI
197
M. Silva, L. S. de Oliveira, A. Andreou, P. O. S. V. de Melo, O. Goga, and F. Benevenuto, Facebook ads monitor: An independent auditing system for political ads on facebook, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 224−234.https://doi.org/10.1145/3366423.3380109
DOI
198
B. Cao, L. Zheng, C. W. Zhang, P. S. Yu, A. Piscitello, J. Zulueta, O. Ajilore, K. Ryan, and A. D. Leow, DeepMood: Modeling mobile phone typing dynamics for mood detection, in Proc. 23rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 747−755.https://doi.org/10.1145/3097983.3098086
DOI
199
M. Gaur, A. Alambo, J. P. Sain, U. Kursuncu, K. Thirunarayan, R. Kavuluru, A. P. Sheth, R. S. Welton, and J. Pathak, Knowledge-aware assessment of severity of suicide risk for early intervention, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 514−525.https://doi.org/10.1145/3308558.3313698
DOI
200
W. H. Yu, M. X. Yu, T. Zhao, and M. Jiang, Identifying referential intention with heterogeneous contexts, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 962−972.
201

D. Susan, Latent semantic analysis, Annual Review of Information Science and Technology, vol. 38, no. 1, pp. 188–230, 2004.

202
J. Y. Jiang, X. Sun, W. Wang, and S. Young, Enhancing air quality prediction with social media and natural language processing, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2627−2632.https://doi.org/10.18653/v1/P19-1251
DOI
203
L. Y. Yang, T. L. J. Ng, B. Smyth, and R. Dong, HTML: Hierarchical transformer-based multi-task learning for volatility prediction, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 441-451.https://doi.org/10.1145/3366423.3380128
DOI
204
Y. M. Xu and S. B. Cohen, Stock movement prediction from tweets and historical prices, in Proc. 56th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, pp. 1970-1979.
205
G. M. Liu, T. T. Nguyen, G. Zhao, W. Zha, J. B. Yang, J. N. Cao, M. Wu, P. L. Zhao, and W. Chen, Repeat buyer prediction for E- commerce, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 155-164.https://doi.org/10.1145/2939672.2939674
DOI
206
S. Chakraborty, A. Venkataraman, S. Jagabathula, and L. Subramanian, Predicting socio-economic indicators using news events, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1455-1464.https://doi.org/10.1145/2939672.2939817
DOI
207
G. Doyle, D. Yurovsky, and M. C. Frank, A robust framework for estimating linguistic alignment in twitter conversations, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 637-648.https://doi.org/10.1145/2872427.2883091
DOI
208
V. Kulkarni, R. Al-Rfou, B. Perozzi, and S. Skiena, Statistically significant detection of linguistic change, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 625-635.https://doi.org/10.1145/2736277.2741627
DOI
209
H. Gonen, G. Jawahar, D. Seddah, and Y. Goldberg, Simple, interpretable and stable method for detecting words with usage change across corpora, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 538-555.https://doi.org/10.18653/v1/2020.acl-main.51
DOI
210
J. Tang, S. Wu, B. Gao, and Y. Wan, Topic-level social network search, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 769-772.https://doi.org/10.1145/2020408.2020532
DOI
211
D. M. Romero, B. Meeder, and J. M. Kleinberg, Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 695-704.https://doi.org/10.1145/1963405.1963503
DOI
212
B. Meeder, B. Karrer, A. Sayedi, R. Ravi, C. Borgs, and J. T. Chayes, We know who you followed last summer: Inferring social link creation times in twitter, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 517-526.https://doi.org/10.1145/1963405.1963479
DOI
213
S. M. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts, Who says what to whom on twitter, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 705-714.
214

K. Lewis, M. Gonzalez, and J. Kaufman, Social selection and peer influence in an online social network, Proc. Natl. Acad. Sci. USA, vol. 109, no. 1, pp. 68–72, 2012.

215

A. L. Schmidt, F. Zollo, M. Del Vicario, A. Bessi, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, Anatomy of news consumption on Facebook, Proc. Natl. Acad. Sci. USA, vol. 114, no. 12, pp. 3035–3039, 2017.

216

D. Eckles, R. F. Kizilcec, and E. Bakshy, Estimating peer effects in networks with peer encouragement designs, Proc. Natl. Acad. Sci. USA, vol. 113, no. 27, pp. 7316–7322, 2016.

217
I. Kayes, N. Kourtellis, D. Quercia, A. Iamnitchi, and F. Bonchi, The social world of content abusers in community question answering, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 570-580.https://doi.org/10.1145/2736277.2741674
DOI
218
J. Z. Qiu, Y. X. Li, J. Tang, Z. Lu, H. Ye, B. Chen, Q. Yang, and J. E. Hopcroft, The lifecycle and cascade of wechat social messaging groups, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 311-320.https://doi.org/10.1145/2872427.2882979
DOI
219

K. Lewis, The limits of racial prejudice, Proc. Natl. Acad. Sci. USA, vol. 110, no. 47, pp. 18814–18819, 2013.

220

L. Glowacki, A. Isakov, R. W. Wrangham, R. McDermott, J. H. Fowler, and N. A. Christakis, Formation of raiding parties for intergroup violence is mediated by social network structure, Proc. Natl. Acad. Sci. USA, vol. 113, no. 43, pp. 12114–12119, 2016.

221

D. Braha, Patterns of ties in problem-solving networks and their dynamic properties, Sci. Rep., vol. 10, no. 1, p. 18137, 2020.

222

R. Dakin and T. B. Ryder, Reciprocity and behavioral heterogeneity govern the stability of social networks, Proc. Natl. Acad. Sci. USA, vol. 117, no. 6, pp. 2993–2999, 2020.

223
M. Gupte, P. Shankar, J. Li, S. Muthukrishnan, and L. Iftode, Finding hierarchy in directed online social networks, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 557-566.https://doi.org/10.1145/1963405.1963484
DOI
224
X. Liu and K. Aberer, Soco: A social network aided context-aware recommender system, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 781-802.https://doi.org/10.1145/2488388.2488457
DOI
225

M. Del Vicario, A. Bessi, F. Zollo, F. Petroni, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, The spreading of misinformation online, Proc. Natl. Acad. Sci. USA, vol. 113, no. 3, pp. 554–559, 2016.

226

J. Lulewicz, The social networks and structural variation of Mississippian sociopolitics in the southeastern United States, Proc. Natl. Acad. Sci. USA, vol. 116, no. 14, pp. 6707–6712, 2019.

227

J. H. Fowler, J. E. Settle, and N. A. Christakis, Correlated genotypes in friendship networks, Proc. Natl. Acad. Sci. USA, vol. 108, no. 5, pp. 1993–1997, 2011.

228

A. I. Roberts and S. G. B. Roberts, Wild chimpanzees modify modality of gestures according to the strength of social bonds and personal network size, Sci. Rep., vol. 6, no. 1, p. 33864, 2016.

229

K. Hilger, M. Ekman, C. J. Fiebach, and U. Basten, Intelligence is associated with the modular structure of intrinsic brain networks, Sci. Rep., vol. 7, no. 1, p. 16088, 2017.

230

H. Youn, L. Sutton, E. Smith, C. Moore, J. F. Wilkins, I. Maddieson, W. Croft, and T. Bhattacharya, On the universal structure of human lexical semantics, Proc. Natl. Acad. Sci. USA, vol. 113, no. 7, pp. 1766–1771, 2016.

231

A. E. Sizemore, E. A. Karuza, C. Giusti, and D. S. Bassett, Knowledge gaps in the early growth of semantic feature networks, Nat. Hum. Behav., vol. 2, no. 9, pp. 682–692, 2018.

232

S. Ronen, B. Gonçalves, K. Z. Hu, A. Vespignani, S. Pinker, and C. A. Hidalgo, Links that speak: The global language network and its association with global fame, Proc. Natl. Acad. Sci. USA, vol. 111, no. 52, pp. E5616–E5622, 2014.

233

L. Taruffi, C. Pehrs, S. Skouras, and S. Koelsch, Effects of sad and happy music on mind-wandering and the default mode network, Sci. Rep., vol. 7, no. 1, p. 14396, 2017.

234

R. Schmälzle, M. B. O’Donnell, J. O. Garcia, C. N. Cascio, J. Bayer, D. S. Bassett, J. M. Vettel, and E. B. Falk, Brain connectivity dynamics during social interaction reflect social network structure, Proc. Natl. Acad. Sci. USA, vol. 114, no. 20, pp. 5153–5158, 2017.

235

S. A. Morelli, D. C. Ong, R. Makati, M. O. Jackson, and J. Zaki, Empathy and well-being correlate with centrality in different social networks, Proc. Natl. Acad. Sci. USA, vol. 114, no. 37, pp. 9843–9847, 2017.

236

H. Kim, S. Kwak, J. Kim, Y. Youm, and J. Chey, Social network position moderates the relationship between late-life depressive symptoms and memory differently in men and women, Sci. Rep., vol. 9, no. 1, p. 6142, 2019.

237

T. Ito, The influence of networks of general trust on willingness to communicate in English for Japanese people, Sci. Rep., vol. 10, no. 1, p. 19939, 2020.

238
J. Bao, T. F. He, S. J. Ruan, Y. H. Li, and Y. Zheng, Planning bike lanes based on sharing-bikes’ trajectories, in Proc. 23rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 1377-1386.https://doi.org/10.1145/3097983.3098056
DOI
239

C. Barrington-Leigh and A. Millard-Ball, Global trends toward urban street-network sprawl, Proc. Natl. Acad. Sci. USA, vol. 117, no. 4, pp. 1941–1950, 2020.

240

N. Yadav, S. Chatterjee, and A. R. Ganguly, Resilience of urban transport network-of-networks under intense flood hazards exacerbated by targeted attacks, Sci. Rep., vol. 10, no. 1, p. 10350, 2020.

241

G. Bonaccorsi, F. Pierri, M. Cinelli, A. Flori, A. Galeazzi, F. Porcelli, A. L. Schmidt, C. M. Valensise, A. Scala, W. Quattrociocchi, et al., Economic and social consequences of human mobility restrictions under COVID-19, Proc. Natl. Acad. Sci. USA, vol. 117, no. 27, pp. 15530–15535, 2020.

242

P. Santi, G. Resta, M. Szell, S. Sobolevsky, S. H. Strogatz, and C. Ratti, Quantifying the benefits of vehicle pooling with shareability networks, Proc. Natl. Acad. Sci. USA, vol. 111, no. 37, pp. 13290–13294, 2014.

243

M. M. Vazifeh, P. Santi, G. Resta, S. H. Strogatz, and C. Ratti, Addressing the minimum fleet problem in on-demand urban mobility, Nature, vol. 557, no. 7706, pp. 534–538, 2018.

244
J. M. Liu, L. L. Sun, W. W. Chen, and H. Xiong, Rebalancing bike sharing systems: A multi-source data smart optimization, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1005-1014.https://doi.org/10.1145/2939672.2939776
DOI
245

A. P. Riascos and J. L. Mateos, Networks and long-range mobility in cities: A study of more than one billion taxi trips in New York City, Sci. Rep., vol. 10, no. 1, p. 4022, 2020.

246

A. Karduni, A. Kermanshah, and S. Derrible, A protocol to convert spatial polyline data to network formats and applications to world urban road networks, Sci. Data, vol. 3, no. 1, p. 160046, 2016.

247

R. Kujala, C. Weckström, R. K. Darst, M. N, Mladenović, and J. Saramäki, A collection of public transport network data sets for 25 cities, Sci. Data, vol. 5, no. 1, p. 180089, 2018.

248
D. S. Wang, D. Pedreschi, C. M. Song, F. Giannotti, and A. L. Barabási, Human mobility, social ties, and link prediction, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 1100−1108.https://doi.org/10.1145/2020408.2020581
DOI
249
E. Cho, S. A. Myers, and J. Leskovec, Friendship and mobility: User movement in location-based social networks, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 1082-1090.https://doi.org/10.1145/2020408.2020579
DOI
250
R. Li, S. J. Wang, H. B. Deng, R. Wang, and K. C. C. Chang, Towards social user profiling: Unified and discriminative influence model for inferring home locations, in Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 1023-1031.https://doi.org/10.1145/2339530.2339692
DOI
251
D. N. Yang, C. Y. Shen, W. C. Lee, and M. S. Chen, On socio-spatial group query for location-based social networks, in Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 949-957.https://doi.org/10.1145/2339530.2339679
DOI
252

L. J. Sun, K. W. Axhausen, D. H. Lee, and X. F. Huang, Understanding metropolitan patterns of daily encounters, Proc. Natl. Acad. Sci. USA, vol. 110, no. 34, pp. 13774–13779, 2013.

253

V. Sekara, A. Stopczynski, and S. Lehmann, Fundamental structures of dynamic social networks, Proc. Natl. Acad. Sci. USA, vol. 113, no. 36, pp. 9977–9982, 2016.

254

A. Halu, A. Scala, A. Khiyami, and M. C. González, Data-driven modeling of solar-powered urban microgrids, Sci. Adv., vol. 2, no. 1, p. e1500700, 2016.

255

S. Battiston, G. Caldarelli, R. M. May, T. Roukny, and J. E. Stiglitz, The price of complexity in financial networks, Proc. Natl. Acad. Sci. USA, vol. 113, no. 36, pp. 10031–10036, 2016.

256

M. Bardoscia, S. Battiston, F. Caccioli, and G. Caldarelli, Pathways towards instability in financial networks, Nat. Commun., vol. 8, no. 1, p. 14416, 2017.

257

L. L. Porfirio, D. Newth, J. J. Finnigan, and Y. Y. Cai, Economic shifts in agricultural production and trade due to climate change, Palgrave Commun., vol. 4, no. 1, p. 111, 2018.

258

Z. M. Ren, A. Zeng, and Y. C. Zhang, Bridging nestedness and economic complexity in multilayer world trade networks, Humanit. Soc. Sci. Commun., vol. 7, no. 1, p. 156, 2020.

259

K. A. Anderson, Skill networks and measures of complex human capital, Proc. Natl. Acad. Sci. USA, vol. 114, no. 48, pp. 12720–12724, 2017.

260

C. A. Bail, L. P. Argyle, T. W. Brown, J. P. Bumpus, H. H. Chen, M. B. F. Hunzaker, J. Lee, M. Mann, F. Merhout, and A. Volfovsky, Exposure to opposing views on social media can increase political polarization, Proc. Natl. Acad. Sci. USA, vol. 115, no. 37, pp. 9216–9221, 2018.

261
S. Padó, A. Blessing, N. Blokker, E. Dayanik, S. Haunss, and J. Kuhn, Who sides with whom? Towards computational construction of discourse networks for political debates, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2841−2847.https://doi.org/10.18653/v1/P19-1273
DOI
262
K. Johnson, D. Jin, and D. Goldwasser, Leveraging behavioral and social information for weakly supervised collective classification of political discourse on twitter, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 741−752.https://doi.org/10.18653/v1/P17-1069
DOI
263

A. Bovet and H. A. Makse, Influence of fake news in Twitter during the 2016 US presidential election, Nat. Commun., vol. 10, no. 1, p. 7, 2019.

264
S. Volkova, G. Coppersmith, and B. Van Durme, Inferring user political preferences from streaming communications, in Proc. 52nd Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA, 2014, pp. 186−196.https://doi.org/10.3115/v1/P14-1018
DOI
265

M. L. Barnes, J. Lynham, K. Kalberg, and P. S. Leung, Social networks and environmental outcomes, Proc. Natl. Acad. Sci. USA, vol. 113, no. 23, pp. 6466–6471, 2016.

266

R. Cámara-Leret, M. A. Fortuna, and J. Bascompte, Indigenous knowledge networks in the face of global change, Proc. Natl. Acad. Sci. USA, vol. 116, no. 20, pp. 9913–9918, 2019.

267
Y. Zheng, F. R. Liu, and H. P. Hsieh, U-air: When urban air quality inference meets big data, in Proc. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 1436−1444.https://doi.org/10.1145/2487575.2488188
DOI
268
H. P. Hsieh, S. D. Lin, and Y. Zheng, Inferring air quality for station location recommendation based on urban big data, in Proc. 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 437−446.https://doi.org/10.1145/2783258.2783344
DOI
269
A. Guille and H. Hacid, A predictive model for the temporal dynamics of information diffusion in online social networks, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 1145-1152.https://doi.org/10.1145/2187980.2188254
DOI
270

J. Gómez-Gardeñes, L. Lotero, S. N. Taraskin, and F. J. Pérez-Reche, Explosive contagion in networks, Sci. Rep., vol. 6, no. 1, p. 19767, 2016.

271

D. Y. Zhang, Y. Wang, and Z. X. Zhang, Identifying and quantifying potential super-spreaders in social networks, Sci. Rep., vol. 9, no. 1, p. 14811, 2019.

272

L. Gao, C. M. Song, Z. Y. Gao, A. L. Barabási, J. P. Bagrow, and D. S. Wang, Quantifying information flow during emergencies, Sci. Rep., vol. 4, no. 1, p. 3997, 2014.

273

W. Quattrociocchi, G. Caldarelli, and A. Scala, Opinion dynamics on interacting networks: Media competition and social influence, Sci. Rep., vol. 4, no. 1, p. 4938, 2014.

274

C. C. Shao, G. L. Ciampaglia, O. Varol, K. C. Yang, A. Flammini, and F. Menczer, The spread of low-credibility content by social bots, Nat. Commun., vol. 9, no. 1, p. 4787, 2018.

275

S. Vosoughi, D. Roy, and S. Aral, The spread of true and false news online, Science, vol. 359, no. 6380, pp. 1146–1151, 2018.

276

A. Lima, M. De Domenico, V. Pejovic, and M. Musolesi, Disease containment strategies based on mobility and information dissemination, Sci. Rep., vol. 5, no. 1, p. 10650, 2015.

277

S. J. Luo, F. Morone, C. Sarraute, M. Travizano, and H. A. Makse, Inferring personal economic status from social network location, Nat. Commun., vol. 8, no. 1, p. 15227, 2017.

278

C. J. Gomez and D. M. J. Lazer, Clustering knowledge and dispersing abilities enhances collective problem solving in a network, Nat. Commun., vol. 10, no. 1, p. 5146, 2019.

279
X. F. Tang, Y. Z. Liu, N. Shah, X. L. Shi, P. Mitra, and S. H. Wang, Knowing your FATE: Friendship, action and temporal explanations for user engagement prediction on social apps, in Proc. 26th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Virtual Event, CA, USA, 2020, pp. 2269−2279.https://doi.org/10.1145/3394486.3403276
DOI
280
C. Li, J. Q. Ma, X. X. Guo, and Q. Z. Mei, DeepCas: An end-to-end predictor of information cascades, in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 577−586.https://doi.org/10.1145/3038912.3052643
DOI
281
J. Z. Qiu, J. Tang, H. Ma, Y. X. Dong, K. S. Wang, and J. Tang, Deepinf: Social influence prediction with deep learning, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 2110−2119.https://doi.org/10.1145/3219819.3220077
DOI
282
Y. J. Lu and C. T. Li, GCAN: Graph-aware co-attention networks for explainable fake news detection on social media, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 505−514.
283
L. Zhong, J. Cao, Q. Sheng, J. B. Guo, and Z. Wang, Integrating semantic and structural information with graph convolutional network for controversy detection, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 515−526.https://doi.org/10.18653/v1/2020.acl-main.49
DOI
284
X. Y. Wang, Y. Ma, Y. Q. Wang, W. Jin, X. Wang, J. L. Tang, C. Y. Jia, and J. Yu, Traffic flow prediction via spatial temporal graph neural network, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 1082−1092.https://doi.org/10.1145/3366423.3380186
DOI
285
D. X. Deng, C. Shahabi, U. Demiryurek, L. H. Zhu, R. Yu, and Y. Liu, Latent space model for road networks to predict time-varying traffic, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1525−1534.https://doi.org/10.1145/2939672.2939860
DOI
286
Y. G. Li, K. Fu, Z. Wang, C. Shahabi, J. P. Ye, and Y. Liu, Multi-task representation learning for travel time estimation, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 1695−1704.
287
Y. Sun, H. S. Zhu, F. Z. Zhuang, J. J. Gu, and Q. He, Exploring the urban region-of-interest through the analysis of online map search queries, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 2269−2278.https://doi.org/10.1145/3219819.3220009
DOI
288
Z. Y. Pan, Y. X. Liang, W. F. Wang, Y. Yu, Y. Zheng, and J. B. Zhang, Urban traffic prediction from spatio- temporal data using deep meta learning, in Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 1720−1730.https://doi.org/10.1145/3292500.3330884
DOI
289
J. Yuan, Y. Zheng, and X. Xie, Discovering regions of different functions in a city using human mobility and POIs, in Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 186−194.https://doi.org/10.1145/2339530.2339561
DOI
290
P. Y. Wang, Y. J. Fu, J. W. Zhang, P. F. Wang, Y. Zheng, and C. C. Aggarwal, You are how you drive: Peer and temporal-aware representation learning for driving behavior analysis, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 2457−2466.https://doi.org/10.1145/3219819.3219985
DOI
291
Q. W. Zhong, Y. Liu, X. Ao, B. B. Hu, J. H. Feng, J. Y. Tang, and Q. He, Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network, in Proc. Web Conf. 2020, Taipei, China, 2020, pp. 785−795.https://doi.org/10.1145/3366423.3380159
DOI
292
J. B. Shang, Y. Zheng, W. Z. Tong, E. Chang, and Y. Yu, Inferring gas consumption and pollution emission of vehicles throughout a city, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 1027−1036.https://doi.org/10.1145/2623330.2623653
DOI
293
H. Lakkaraju and J. Ajmera, Attention prediction on social media brand pages, in Proc. 20th ACM Int. Conf. on Information and Knowledge Management, Glasgow, UK, 2011, pp. 2157−2160.https://doi.org/10.1145/2063576.2063915
DOI
294
S. Volkova and J. Y. Jang, Misleading or falsification: Inferring deceptive strategies and types in online news and social media, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 575−583.https://doi.org/10.1145/3184558.3188728
DOI
295
W. B. Tesfay, P. Hofmann, T. Nakamura, S. Kiyomoto, and J. M. Serna, I read but don’t agree: Privacy policy benchmarking using machine learning and the EU GDPR, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 163−166.https://doi.org/10.1145/3184558.3186969
DOI
296
R. Parimi and D. Caragea, Predicting friendship links in social networks using a topic modeling approach, in Proc. 15th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Shenzhen, China, 2011, pp. 75−86.https://doi.org/10.1007/978-3-642-20847-8_7
DOI
297
N. Ramakrishnan, P. Butler, S. Muthiah, N. Self, R. P. Khandpur, P. Saraf, W. Wang, J. Cadena, A. Vullikanti, G. Korkmaz, et al., `Beating the news' with EMBERS: Forecasting civil unrest using open source indicators, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York City, NY, USA, 2014, pp. 1799−1808.https://doi.org/10.1145/2623330.2623373
DOI
298
F. Chen and D. B. Neill, Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 1166−1175.https://doi.org/10.1145/2623330.2623619
DOI
299
K. Q. Li, W. Lu, S. Bhagat, L. V. S. Lakshmanan, and C. Yu, On social event organization, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 1206−1215.
300
S. Rayana and L. Akoglu, Collective opinion spam detection: Bridging review networks and metadata, in Proc. 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 985−994.https://doi.org/10.1145/2783258.2783370
DOI
301
O. Goga, P. Loiseau, R. Sommer, R. Teixeira, and K. P. Gummadi, On the reliability of profile matching across large online social networks, in Proc. 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 1799−1808.https://doi.org/10.1145/2783258.2788601
DOI
302
M. Madaio, S. T. Chen, O. L. Haimson, W. W. Zhang, X. Cheng, M. Hinds-Aldrich, D. H. Chau, and B Dilkina, Firebird: Predicting fire risk and prioritizing fire inspections in Atlanta, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 185−194.https://doi.org/10.1145/2939672.2939682
DOI
303
B. Shi, W. Lam, L. D. Bing, and Y. Q. Xu, Detecting common discussion topics across culture from news reader comments, in Proc. 54th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, 2016, pp. 676−685.https://doi.org/10.18653/v1/P16-1064
DOI
304
S. Bergsma and B. Van Durme, Using conceptual class attributes to characterize social media users, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 710−720.
305
Z. Kozareva, Multilingual affect polarity and valence prediction in metaphor-rich texts, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 682−691.
306
S. Rosenthal and K. McKeown, Age prediction in blogs: A study of style, content, and online behavior in pre- and post-social media generations, in Proc. 49th Ann. Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA, 2011, pp. 763−772.
307
S. Park, K. S. Lee, and J. Song, Contrasting opposing views of news articles on contentious issues, in Proc. 49th Ann. Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA, 2011, pp. 340−349.
308
C. Castillo, M. Mendoza, and B. Poblete, Information credibility on twitter, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 675−684.https://doi.org/10.1145/1963405.1963500
DOI
309
J. J. Lin, R. Snow, and W. Morgan, Smoothing techniques for adaptive online language models: Topic tracking in tweet streams, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 422−429.https://doi.org/10.1145/2020408.2020476
DOI
310
M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, A Stylometric inquiry into hyperpartisan and fake news, in Proc. 56th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, pp. 231−240.https://doi.org/10.18653/v1/P18-1022
DOI
311
J. Ma, W. Gao, and K. F. Wong, Detect rumors in microblog posts using propagation structure via kernel learning, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 708−717.https://doi.org/10.18653/v1/P17-1066
DOI
312
N. Hassan, F. Arslan, C. K. Li, and M. Tremayne, Toward automated fact- checking: Detecting check-worthy factual claims by ClaimBuster, in Proc. 23rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 1803−1812.https://doi.org/10.1145/3097983.3098131
DOI
313
Y. X. Dong, Y. Yang, J. Tang, Y. Yang, and N. V. Chawla, Inferring user demographics and social strategies in mobile social networks, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 15−24.https://doi.org/10.1145/2623330.2623703
DOI
314
B. Hooi, H. A. Song, A. Beutel, N. Shah, K. Shin, and C. Faloutsos, Fraudar: Bounding graph fraud in the face of camouflage, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 895−904.https://doi.org/10.1145/2939672.2939747
DOI
315
C. Budak, D. Agrawal, and A. El Abbadi, Limiting the spread of misinformation in social networks, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 665-674.https://doi.org/10.1145/1963405.1963499
DOI
316
J. Ratkiewicz, M. D. Conover, M. R. Meiss, B. Gonçalves, S. Patil, A. Flammini, and F. Menczer, Truthy: Mapping the spread of astroturf in microblog streams, in Proc. 20th Int. Conf. Companion on World Wide Web, Hyderabad, India, 2011, pp. 249−252.https://doi.org/10.1145/1963192.1963301
DOI
317
M. Jamali, G. Haffari, and M. Ester, Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns, in Proc. 20th Int. Conf. on World Wide Web, Hyderabad, India, 2011, pp. 527−536.https://doi.org/10.1145/1963405.1963480
DOI
318
S. Ghosh, B. Viswanath, F. Kooti, N. K. Sharma, G. Korlam, F. Benevenuto, N. Ganguly, and P. K. Gummadi, Understanding and combating link farming in the twitter social network, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 61−70.https://doi.org/10.1145/2187836.2187846
DOI
319
C. Yang, R. C. Harkreader, J. L. Zhang, S. Shin, and G. F. Gu, Analyzing spammers' social networks for fun and profit: A case study of cyber criminal ecosystem on twitter, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 71−80.https://doi.org/10.1145/2187836.2187847
DOI
320
G. Ver Steeg and A. Galstyan, Information transfer in social media, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 509−518.https://doi.org/10.1145/2187836.2187906
DOI
321
A. Beutel, W. H. Xu, V. Guruswami, C. Palow, and C. Faloutsos, CopyCatch: Stopping group attacks by spotting lockstep behavior in social networks, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 119−130.https://doi.org/10.1145/2488388.2488400
DOI
322
T. C. Lou and J. Tang, Mining structural hole spanners through information diffusion in social networks, in Proc. 22nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 825−836.https://doi.org/10.1145/2488388.2488461
DOI
323
J. Cheng, L. A. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec, Can cascades be predicted? in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 925−936.https://doi.org/10.1145/2566486.2567997
DOI
324
S. A. Myers, A. Sharma, P. Gupta, and J. J. Lin, Information network or social network?: The structure of the Twitter follow graph, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 493−498.https://doi.org/10.1145/2567948.2576939
DOI
325
C. Buntain and J. Golbeck, Identifying social roles in Reddit using network structure, in Proc. 23rd Int. Conf. on World Wide Web, Seoul, Republic of Korea, 2014, pp. 615-620.https://doi.org/10.1145/2567948.2579231
DOI
326
U. Pavalanathan and M. De Choudhury, Identity management and mental health discourse in social media, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 315−321.https://doi.org/10.1145/2740908.2743049
DOI
327
P. Singer, D. Helic, A. Hotho, and M. Strohmaier, HypTrails: A bayesian approach for comparing hypotheses about human trails on the web, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 1003−1013.https://doi.org/10.1145/2736277.2741080
DOI
328
M. Yin, M. L. Gray, S. Suri, and J. W. Vaughan, The communication network within the crowd, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 1293−1303.https://doi.org/10.1145/2872427.2883036
DOI
329
J. Su, A. Sharma, and S. Goel, The effect of recommendations on network structure, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 1157−1167.https://doi.org/10.1145/2872427.2883040
DOI
330
D. M. Romero, B. Uzzi, and J. Kleinberg, Social networks under stress, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 9−20.https://doi.org/10.1145/2872427.2883063
DOI
331
Á. García-Recuero, Discouraging abusive behavior in privacy-preserving online social networking applications, in Proc. 25th Int. Conf. Companion on World Wide Web, Montreal, Canada, 2016, pp. 305−309.https://doi.org/10.1145/2872518.2888600
DOI
332
Y. X. Li, O. Martinez, X. Chen, Y. Li, and J. E. Hopcroft, In a world that counts: Clustering and detecting fake social engagement at scale, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 111−120.
333
G. Resende, P. F. Melo, H. Sousa, J. Messias, M. Vasconcelos, J. M. Almeida, and F. Benevenuto, (Mis) information dissemination in WhatsApp: Gathering, analyzing and countermeasures, in Proc. World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 818−828.https://doi.org/10.1145/3308558.3313688
DOI
334

N. E. Friedkin, A. V. Proskurnikov, R. Tempo, and S. E. Parsegov, Network science on belief system dynamics under logic constraints, Science, vol. 354, no. 6310, pp. 321–326, 2016.

335

S. Mukherjee, D. M. Romero, B. Jones, and B. Uzzi, The nearly universal link between the age of past knowledge and tomorrow's breakthroughs in science and technology: The hotspot, Sci. Adv., vol. 3, no. 4, p. e1601315, 2017.

336
X. Jin, C. Wang, J. B. Luo, X. Yu, and J. W. Han, LikeMiner: A system for mining the power of 'like' in social media networks, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 753−756.https://doi.org/10.1145/2020408.2020528
DOI
337
P. Papadimitriou, H. Garcia-Molina, P. Krishnamurthy, R. A. Lewis, and D. H. Reiley, Display advertising impact: Search lift and social influence, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 1019−1027.https://doi.org/10.1145/2020408.2020572
DOI
338
C. T. Li and S. D. Lin, Social flocks: A crowd simulation framework for social network generation, community detection, and collective behavior modeling, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 765−768.
339
Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos, Rise and fall patterns of information diffusion: Model and implications, in Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 6−14.https://doi.org/10.1145/2339530.2339537
DOI
340

D. G. Rand, S. Arbesman, and N. A. Christakis, Dynamic social networks promote cooperation in experiments with humans, Proc. Natl. Acad. Sci. USA, vol. 108, no. 48, pp. 19193–19198, 2011.

341

G. Facchetti, G. Iacono, and C. Altafini, Computing global structural balance in large-scale signed social networks, Proc. Natl. Acad. Sci. USA, vol. 108, no. 52, pp. 20953–20958, 2011.

342

C. P. Roca and D. Helbing, Emergence of social cohesion in a model society of greedy, mobile individuals, Proc. Natl. Acad. Sci. USA, vol. 108, no. 28, pp. 11370–11374, 2011.

343

L. Dall’Asta, M. Marsili, and P. Pin, Collaboration in social networks, Proc. Natl. Acad. Sci. USA, vol. 109, no. 12, pp. 4395–4400, 2012.

344

B. J. Mills, J. J. Clark, M. A. Peeples, W. R. Haas Jr, J. M. Roberts Jr, J. B. Hill, D. L. Huntley, L. Borck, R. L. Breiger, A. Clauset, et al., Transformation of social networks in the late pre-Hispanic US Southwest, Proc. Natl. Acad. Sci. USA, vol. 110, no. 15, pp. 5785–5790, 2013.

345

Z. Q. Jiang, W. J. Xie, M. X. Li, B. Podobnik, W. X. Zhou, and H. E. Stanley, Calling patterns in human communication dynamics, Proc. Natl. Acad. Sci. USA, vol. 110, no. 5, pp. 1600–1605, 2013.

346

A. Rutherford, M. Cebrian, S. Dsouza, E. Moro, A. Pentland, and I. Rahwan, Limits of social mobilization, Proc. Natl. Acad. Sci. USA, vol. 110, no. 16, pp. 6281–6286, 2013.

347

J. Saramäki, E. A. Leicht, E. López, S. G. B. Roberts, F. Reed-Tsochas, and R. I. M. Dunbar, Persistence of social signatures in human communication, Proc. Natl. Acad. Sci. USA, vol. 111, no. 3, pp. 942–947, 2014.

348

A. Rzhetsky, J. G. Foster, I. T. Foster, and J. A. Evans, Choosing experiments to accelerate collective discovery, Proc. Natl. Acad. Sci. USA, vol. 112, no. 47, pp. 14569–14574, 2015.

349

A. M. Petersen, Quantifying the impact of weak, strong, and super ties in scientific careers, Proc. Natl. Acad. Sci. USA, vol. 112, no. 34, pp. E4671–E4680, 2015.

350

E. L. Paluck, H. Shepherd, and P. M. Aronow, Changing climates of conflict: A social network experiment in 56 schools, Proc. Natl. Acad. Sci. USA, vol. 113, no. 3, pp. 566–571, 2016.

351

A. Coman, I. Momennejad, R. D. Drach, and A. Geana, Mnemonic convergence in social networks: The emergent properties of cognition at a collective level, Proc. Natl. Acad. Sci. USA, vol. 113, no. 29, pp. 8171–8176, 2016.

352

X. Han, S. N. Cao, Z. S. Shen, B. Y. Zhang, W. X. Wang, R. Cressman, and H. E. Stanley, Emergence of communities and diversity in social networks, Proc. Natl. Acad. Sci. USA, vol. 114, no. 11, pp. 2887–2891, 2017.

353

D. Guilbeault, J. Becker, and D. Centola, Social learning and partisan bias in the interpretation of climate trends, Proc. Natl. Acad. Sci. USA, vol. 115, no. 39, pp. 9714–9719, 2018.

354

I. Tamarit, J. A. Cuesta, R. I. M. Dunbar, and A. Sánchez, Cognitive resource allocation determines the organization of personal networks, Proc. Natl. Acad. Sci. USA, vol. 115, no. 33, pp. 8316–8321, 2018.

355

C. Stadtfeld, A. Vörös, T. Elmer, Z. Boda, and I. J. Raabe, Integration in emerging social networks explains academic failure and success, Proc. Natl. Acad. Sci. USA, vol. 116, no. 3, pp. 792–797, 2019.

356

Y. Yang, N. V. Chawla, and B. Uzzi, A network’s gender composition and communication pattern predict women’s leadership success, Proc. Natl. Acad. Sci. USA, vol. 116, no. 6, pp. 2033–2038, 2019.

357

D. R. Lo Sardo, S. Thurner, J. Sorger, G. Duftschmid, G. Endel, and P. Klimek, Quantification of the resilience of primary care networks by stress testing the health care system, Proc. Natl. Acad. Sci. USA, vol. 116, no. 48, pp. 23930–23935, 2019.

358

A. Almaatouq, A. Noriega-Campero, A. Alotaibi, P. M. Krafft, M. Moussaid, and A. Pentland, Adaptive social networks promote the wisdom of crowds, Proc. Natl. Acad. Sci. USA, vol. 117, no. 21, pp. 11379–11386, 2020.

359

N. Rovira-Asenjo, T. Gumí, M. Sales-Pardo, and R. Guimerà, Predicting future conflict between team-members with parameter-free models of social networks, Sci. Rep., vol. 3, no. 1, p. 1999, 2013.

360

M. H. Li, H. L. Zou, S. G. Guan, X. F. Gong, K. Li, Z. R. Di, and C. H. Lai, A coevolving model based on preferential triadic closure for social media networks, Sci. Rep., vol. 3, no. 1, p. 2512, 2013.

361

W. Wang, Q. H. Liu, S. M. Cai, M. Tang, L. A. Braunstein, and H. E. Stanley, Suppressing disease spreading by using information diffusion on multiplex networks, Sci. Rep., vol. 6, no. 1, p. 29259, 2016.

362

S. Aral and C. Nicolaides, Exercise contagion in a global social network, Nat. Commun., vol. 8, no. 1, p. 14753, 2017.

363

M. Del Vicario, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, Modeling confirmation bias and polarization, Sci. Rep., vol. 7, no. 1, p. 40391, 2017.

364

C. Parkinson, A. M. Kleinbaum, and T. Wheatley, Spontaneous neural encoding of social network position, Nat. Hum. Behav., vol. 1, no. 5, p. 0072, 2017.

365

F. Battiston, V. Nicosia, V. Latora, and M. S. Miguel, Layered social influence promotes multiculturality in the Axelrod model, Sci. Rep., vol. 7, no. 1, p. 1809, 2017.

366

C. Shen, C. Chu, H. Guo, L. Shi, and J. Y. Duan, Coevolution of vertex weights resolves social dilemma in spatial networks, Sci. Rep., vol. 7, no. 1, p. 15213, 2017.

367

Y. E. Wu, S. H. Chang, Z. P. Zhang, and Z. H. Deng, Impact of social reward on the evolution of the cooperation behavior in complex networks, Sci. Rep., vol. 7, no. 1, p. 41076, 2017.

368

K. M. Altenburger and J. Ugander, Monophily in social networks introduces similarity among friends-of-friends, Nat. Hum. Behav., vol. 2, no. 4, pp. 284–290, 2018.

369

I. Iacopini, G. Petri, A. Barrat, and V. Latora, Simplicial models of social contagion, Nat. Commun., vol. 10, no. 1, p. 2485, 2019.

370

N. F. Johnson, R. Leahy, N. J. Restrepo, N. Velasquez, M. Zheng, P. Manrique, P. Devkota, and S. Wuchty, Hidden resilience and adaptive dynamics of the global online hate ecology, Nature, vol. 573, no. 7773, pp. 261–265, 2019.

371

E. Lee, F. Karimi, C. Wagner, H. H. Jo, M. Strohmaier, and M. Galesic, Homophily and minority-group size explain perception biases in social networks, Nat. Hum. Behav., vol. 3, no. 10, pp. 1078–1087, 2019.

372

R. Schuchard, A. Crooks, A. Stefanidis, and A. Croitoru, Bots fired: Examining social bot evidence in online mass shooting conversations, Palgrave Commun., vol. 5, no. 1, p. 158, 2019.

373

Z. Q. Zhu, C. Gao, Y. M. Zhang, H. N. Li, J. Xu, Y. L. Zan, and Z. Li, Cooperation and competition among information on social networks, Sci. Rep., vol. 10, no. 1, p. 12160, 2020.

374

T. David-Barrett, Herding friends in similarity-based architecture of social networks, Sci. Rep., vol. 10, no. 1, p. 4859, 2020.

375

C. Pomeroy, R. M. Bond, P. J. Mucha, and S. J. Cranmer, Dynamics of social network emergence explain network evolution, Sci. Rep., vol. 10, no. 1, p. 21876, 2020.

376

G. Pickard, W. Pan, I. Rahwan, M. Cebrian, R. Crane, A. Madan, and A. Pentland, Time-critical social mobilization, Science, vol. 334, no. 6055, pp. 509–512, 2011.

377

H. P. Young, The dynamics of social innovation, Proc. Natl. Acad. Sci. USA, vol. 108, no. S4, pp. 21285–21291, 2011.

378

N. S. Contractor and L. A. DeChurch, Integrating social networks and human social motives to achieve social influence at scale, Proc. Natl. Acad. Sci. USA, vol. 111, no. S4, pp. 13650–13657, 2014.

379

J. Becker, D. Brackbill, and D. Centola, Network dynamics of social influence in the wisdom of crowds, Proc. Natl. Acad. Sci. USA, vol. 114, no. 26, pp. E5070–E5076, 2017.

380

J. S. Sayles and J. A. Baggio, Social-ecological network analysis of scale mismatches in estuary watershed restoration, Proc. Natl. Acad. Sci. USA, vol. 114, no. 10, pp. E1776–E1785, 2017.

381

Y. Q. Hu, S. G. Ji, Y. L. Jin, L. Feng, H. E. Stanley, and S. Havlin, Local structure can identify and quantify influential global spreaders in large scale social networks, Proc. Natl. Acad. Sci. USA, vol. 115, no. 29, pp. 7468–7472, 2018.

382

Y. F. Ma and B. Uzzi, Scientific prize network predicts who pushes the boundaries of science, Proc. Natl. Acad. Sci. USA, vol. 115, no. 50, pp. 12608–12615, 2018.

383

T. Wei, M. H. Li, C. S. Wu, X. Y. Yan, Y. Fan, Z. R. Di, and J. S. Wu, Do scientists trace hot topics? Sci. Rep., vol. 3, no. 1, p. 2207, 2013.

384

A. Ehlert, M. Kindschi, R. Algesheimer, and H. Rauhut, Human social preferences cluster and spread in the field, Proc. Natl. Acad. Sci. USA, vol. 117, no. 37, pp. 22787–22792, 2020.

385

F. Gargiulo and T. Carletti, Driving forces of researchers mobility, Sci. Rep., vol. 4, no. 1, p. 4860, 2014.

386
P. H. C. Guerra, A. Veloso, W. Meira Jr, and V. A. F. Almeida, From bias to opinion: A transfer-learning approach to real-time sentiment analysis, in Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011, pp. 150-158.
387

Y. Y. Wu, M. Kosinski, and D. Stillwell, Computer-based personality judgments are more accurate than those made by humans, Proc. Natl. Acad. Sci. USA, vol. 112, no. 4, pp. 1036–1040, 2015.

388

F. Schlosser, B. F. Maier, O. Jack, D. Hinrichs, A. Zachariae, and D. Brockmann, COVID-19 lockdown induces disease-mitigating structural changes in mobility networks, Proc. Natl. Acad. Sci. USA, vol. 117, no. 52, pp. 32883–32890, 2020.

389

A. D. Henry, P. Prałat, and C. Q. Zhang, Emergence of segregation in evolving social networks, Proc. Natl. Acad. Sci. USA, vol. 108, no. 21, pp. 8605–8610, 2011.

390

W. Mason and D. J. Watts, Collaborative learning in networks, Proc. Natl. Acad. Sci. USA, vol. 109, no. 3, pp. 764–769, 2012.

391

J. Wang, S. Suri, and D. J. Watts, Cooperation and assortativity with dynamic partner updating, Proc. Natl. Acad. Sci. USA, vol. 109, no. 36, pp. 14363–14368, 2012.

392

P. Dandekar, A. Goel, and D. T. Lee, Biased assimilation, homophily, and the dynamics of polarization, Proc. Natl. Acad. Sci. USA, vol. 110, no. 15, pp. 5791–5796, 2013.

393

A. Varga, Shorter distances between papers over time are due to more cross-field references and increased citation rate to higher-impact papers, Proc. Natl. Acad. Sci. USA, vol. 116, no. 44, pp. 22094–22099, 2019.

394

J. Becker, E. Porter, and D. Centola, The wisdom of partisan crowds, Proc. Natl. Acad. Sci. USA, vol. 116, no. 22, pp. 10717–10722, 2019.

395

L. Lucchini, L. Alessandretti, B. Lepri, A. Gallo, and A. Baronchelli, From code to market: Network of developers and correlated returns of cryptocurrencies, Sci. Adv., vol. 6, no. 51, p. eabd2204, 2020.

396

H. Shirado and N. A. Christakis, Locally noisy autonomous agents improve global human coordination in network experiments, Nature, vol. 545, no. 7654, pp. 370–374, 2017.

397

A. J. Stewart, M. Mosleh, M. Diakonova, A. A. Arechar, D. G. Rand, and J. B. Plotkin, Information gerrymandering and undemocratic decisions, Nature, vol. 573, no. 7772, pp. 117–121, 2019.

398

L. Muchnik, S. Pei, L. C. Parra, S. D. S. Reis, J. S. Andrade Jr, S. Havlin, and H. A. Makse, Origins of power-law degree distribution in the heterogeneity of human activity in social networks, Sci. Rep., vol. 3, no. 1, p. 1783, 2013.

399

Z. Wang, C. Y. Xia, S. Meloni, C. S. Zhou, and Y. Moreno, Impact of social punishment on cooperative behavior in complex networks, Sci. Rep., vol. 3, no. 1, p. 3055, 2013.

400

S. F. Lu, G. Z. Jin, B. Uzzi, and B. Jones, The retraction penalty: Evidence from the web of science, Sci. Rep., vol. 3, no. 1, p. 3146, 2013.

401

P. Singh, S. Sreenivasan, B. K. Szymanski, and G. Korniss, Threshold-limited spreading in social networks with multiple initiators, Sci. Rep., vol. 3, no. 1, p. 2330, 2013.

402

R. W. Wilkins, D. A. Hodges, P. J. Laurienti, M. Steen, and J. H. Burdette, Network science and the effects of music preference on functional brain connectivity: From Beethoven to Eminem, Sci. Rep., vol. 4, no. 1, p. 6130, 2014.

403

D. A. Gianetto and B. Heydari, Network modularity is essential for evolution of cooperation under uncertainty, Sci. Rep., vol. 5, no. 1, p. 9340, 2015.

404

J. A. Cuesta, C. Gracia-Lázaro, A. Ferrer, Y. Moreno, and A. Sánchez, Reputation drives cooperative behaviour and network formation in human groups, Sci. Rep., vol. 5, no. 1, p. 7843, 2015.

405

M. Ramos, J. Shao, S. D. S. Reis, C. Anteneodo, J. S. Andrade, S. Havlin, and H. A. Makse, How does public opinion become extreme? Sci. Rep., vol. 5, no. 1, p. 10032, 2015.

406

G. L. Yang, T. P. Benko, M. Cavaliere, J. C. Huang, and M. Perc, Identification of influential invaders in evolutionary populations, Sci. Rep., vol. 9, no. 1, p. 7305, 2019.

407
R. Zafarani and H. Liu, Connecting users across social media sites: A behavioral-modeling approach, in Proc. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 41−49.https://doi.org/10.1145/2487575.2487648
DOI
408
A. Mukherjee, A. Kumar, B. Liu, J. H. Wang, M. Hsu, M. Castellanos, and R. Ghosh, Spotting opinion spammers using behavioral footprints, in Proc. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 632−640.https://doi.org/10.1145/2487575.2487580
DOI
409
P. Lucey, D. Oliver, P. Carr, J. Roth, and I. A. Matthews, Assessing team strategy using spatiotemporal data, in Proc. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Chicago, IL, USA, 2013, pp. 1366−1374.https://doi.org/10.1145/2487575.2488191
DOI
410
A. F. Costa, Y. Yamaguchi, A. J. M. Traina, C. Traina Jr, and C. Faloutsos, RSC: Mining and modeling temporal activity in social media, in Proc. 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 269−278.
411
X. Mu, F. D. Zhu, E. P. Lim, J. Xiao, J. Z. Wang, and Z. H. Zhou, User identity linkage by latent user space modelling, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1775−1784.https://doi.org/10.1145/2939672.2939849
DOI
412
Y. Q. Wang, F. L. Ma, Z. W. Jin, Y. Yuan, G. X. Xun, K. Jha, L. Su, and J. Gao, EANN: Event adversarial neural networks for multi-modal fake news detection, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 849−857.https://doi.org/10.1145/3219819.3219903
DOI
413
R. H. Jiang, X. Song, D. Huang, X. Y. Song, T. Q. Xia, Z. K. Cai, Z. N. Wang, K. S. Kim, and R. Shibasaki, DeepUrbanEvent: A system for predicting citywide crowd dynamics at big events, in Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, 2114−2122.https://doi.org/10.1145/3292500.3330654
DOI
414
D. Z. Ding, M. Zhang, X. D. Pan, M. Yang, and X. N. He, Modeling extreme events in time series prediction, in Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 1114−1122.https://doi.org/10.1145/3292500.3330896
DOI
415
F. Zarrinkalam, H. Fani, and E. Bagheri, Social user interest mining: Methods and applications, in Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 3235−3236.https://doi.org/10.1145/3292500.3332279
DOI
416
J. Q. Zhang, B. Bai, Y. Lin, J. Liang, K. Bai, and F. Wang, General-purpose user Embeddings based on mobile app usage, in Proc. 26th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Virtual Event, CA, USA, 2020, pp. 2831−2840.https://doi.org/10.1145/3394486.3403334
DOI
417
S. Dutta, S. Masud, S. Chakrabarti, and T. Chakraborty, Deep exogenous and endogenous influence combination for social chatter intensity prediction, in Proc. 26th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Virtual Event, CA, USA, 2020, pp. 1999−2008.https://doi.org/10.1145/3394486.3403251
DOI
418
J. T. Ye and S. Skiena, The secret lives of names?: Name embeddings from social media, in Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 3000−3008.
419
R. Baly, G. Karadzhov, J. S. An, H. Kwak, Y. Dinkov, A. Ali, J. R. Glass, and P. Nakov, What was written vs. who read it: News media profiling using text analysis and social media context, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 3364−3374.https://doi.org/10.18653/v1/2020.acl-main.308
DOI
420
L. W. Wu, Y. Rao, Y. Q. Zhao, H. Liang, and A. Nazir, DTCA: Decision tree-based co-attention networks for explainable claim verification, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 1024−1035.
421
S. Bansal, V. Garimella, A. Suhane, J. Patro, and A. Mukherjee, Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of Humour, sarcasm and hate speech detection, in Proc. 58th Ann. Meeting of the Association for Computational Linguistics, Seattle, WA, USA, 2020, pp. 1018−1023.https://doi.org/10.18653/v1/2020.acl-main.96
DOI
422
A. G. Chowdhury, R. Sawhney, R. R. Shah, and D. Mahata, YouToo? Detection of personal recollections of sexual harassment on social media, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2527−2537.https://doi.org/10.18653/v1/P19-1241
DOI
423
S. Oprea and W. Magdy, Exploring author context for detecting intended vs. perceived sarcasm, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2854−2859.https://doi.org/10.18653/v1/P19-1275
DOI
424
J. Ma, W. Gao, S. Joty, and K. F. Wong, Sentence-level evidence embedding for claim verification with hierarchical attention networks, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2561−2571.https://doi.org/10.18653/v1/P19-1244
DOI
425
M. T. Wan, R. Misra, N. Nakashole, and J. McAuley, Fine-grained spoiler detection from large-scale review corpora, in Proc. 57th Ann. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 2605−2610.https://doi.org/10.18653/v1/P19-1248
DOI
426
J. Ma, W. Gao, and K. F. Wong, Rumor detection on twitter with tree-structured recursive neural networks, in Proc. 56th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, pp. 1980−1989.https://doi.org/10.18653/v1/P18-1184
DOI
427
A. Mishra, K. Dey, and P. Bhattacharyya, Learning cognitive features from gaze data for sentiment and sarcasm classification using convolutional neural network, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 377−387.https://doi.org/10.18653/v1/P17-1035
DOI
428
U. Pavalanathan, J. Fitzpatrick, S. Kiesling, and J. Eisenstein, A multidimensional lexicon for interpersonal stancetaking, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 884−895.https://doi.org/10.18653/v1/P17-1082
DOI
429
A. Sasaki, K. Hanawa, N. Okazaki, and K. Inui, Other topics you may also agree or disagree: Modeling inter-topic preferences using tweets and matrix factorization, in Proc. 55th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, pp. 398−408.https://doi.org/10.18653/v1/P17-1037
DOI
430
S. Volkova and Y. Bachrach, Inferring perceived demographics from user emotional tone and user-environment emotional contrast, in Proc. 54th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, 2016, pp. 1567−1578.https://doi.org/10.18653/v1/P16-1148
DOI
431
D. Preoţiuc-Pietro, V. Lampos, and N. Aletras, An analysis of the user occupational class through Twitter content, in Proc. 53rd Ann. Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, pp. 1754−1764.https://doi.org/10.3115/v1/P15-1169
DOI
432
J. W. Li, M. Ott, C. Cardie, and E. Hovy, Towards a general rule for identifying deceptive opinion spam, in Proc. 52nd Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA, 2014, pp. 1566−1576.
433
M. Yancheva and F. Rudzicz, Automatic detection of deception in child-produced speech using syntactic complexity features, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 944−953.
434
Q. M. Diao, J. Jiang, F. D. Zhu, and E. P. Lim, Finding bursty topics from microblogs, in Proc. 50th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jeju Island, Republic of Korea, 2012, pp. 536−544.
435
N. Djuric, J. Zhou, R. Morris, M. Grbovic, V. Radosavljevic, and N. Bhamidipati, Hate speech detection with comment embeddings, in Proc. 24th Int. Conf. on World Wide Web, Florence, Italy, 2015, pp. 29−30.https://doi.org/10.1145/2740908.2742760
DOI
436
K. H. Lim, K. E. Lee, D. Kendal, L. Rashidi, E. Naghizade, S. Winter, and M. Vasardani, The grass is greener on the other side: Understanding the effects of green spaces on Twitter user sentiments, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 275−282.https://doi.org/10.1145/3184558.3186337
DOI
437
K. Johnson and D. Goldwasser, Classification of moral foundations in Microblog political discourse, in Proc. 56th Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, pp. 720−730.https://doi.org/10.18653/v1/P18-1067
DOI
438
V. Lampos, D. Preoţiuc-Pietro, and T. Cohn, A user-centric model of voting intention from social media, in Proc. 51st Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, pp. 993−1003.
439
A. Badawy, K. Lerman, and E. Ferrara, Who falls for online political manipulation? in Proc. 2019 World Wide Web Conf., San Francisco, CA, USA, 2019, pp. 162−168.https://doi.org/10.1145/3308560.3316494
DOI
440
V. A. Nguyen, J. Boyd-Graber, P. Resnik, and K. Miler, Tea party in the house: A hierarchical ideal point topic model and its application to republican legislators in the 112th congress, in Proc. 53rd Ann. Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, pp. 1438−1448.https://doi.org/10.3115/v1/P15-1139
DOI
441
M. Iyyer, P. Enns, J. Boyd-Graber, and P. Resnik, Political ideology detection using recursive neural networks, in Proc. 52nd Ann. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA, 2014, pp. 1113−1122.https://doi.org/10.3115/v1/P14-1105
DOI
442

E. Atalay, A. Hortaçsu, J. Roberts, and C. Syverson, Network structure of production, Proc. Natl. Acad. Sci. USA, vol. 108, no. 13, pp. 5199–5202, 2011.

443

N. Arinaminpathy, S. Kapadia, and R. M. May, Size and complexity in model financial systems, Proc. Natl. Acad. Sci. USA, vol. 109, no. 45, pp. 18338–18343, 2012.

444

A. G. Haldane and R. M. May, Systemic risk in banking ecosystems, Nature, vol. 469, no. 7330, pp. 351–355, 2011.

445

F. Pozzi, T. Di Matteo, and T. Aste, Spread of risk across financial markets: Better to invest in the peripheries, Sci. Rep., vol. 3, no. 1, p. 1665, 2013.

446

T. Squartini, I. Van Lelyveld, and D. Garlaschelli, Early-warning signals of topological collapse in interbank networks, Sci. Rep., vol. 3, no. 1, p. 3357, 2013.

447

S. Thurner and S. Poledna, DebtRank-transparency: Controlling systemic risk in financial networks, Sci. Rep., vol. 3, no. 1, p. 1888, 2013.

448

G. Cimini, T. Squartini, D. Garlaschelli, and A. Gabrielli, Systemic risk analysis on reconstructed economic and financial networks, Sci. Rep., vol. 5, no. 1, p. 15758, 2015.

449

G. L. Ciampaglia, A. Flammini, and F. Menczer, The production of information in the attention economy, Sci. Rep., vol. 5, no. 1, p. 9452, 2015.

450

O. Filip, K. Janda, L. Kristoufek, and D. Zilberman, Dynamics and evolution of the role of biofuels in global commodity and financial markets, Nat. Energy, vol. 1, no. 12, p. 16169, 2016.

451

J. García-Algarra, M. L. Mouronte-López, and J. Galeano, A stochastic generative model of the world trade network, Sci. Rep., vol. 9, no. 1, p. 18539, 2019.

452

J. T. Kao, J. Y. Wu, L. Bergen, and N. D. Goodman, Nonliteral understanding of number words, Proc. Natl. Acad. Sci. USA, vol. 111, no. 33, pp. 12002–12007, 2014.

453

A. Bouchard-Côté, D. Hall, T. L. Griffiths, and D. Klein, Automated reconstruction of ancient languages using probabilistic models of sound change, Proc. Natl. Acad. Sci. USA, vol. 110, no. 11, pp. 4224–4229, 2013.

454

P. Deville, C. Linard, S. Martin, M. Gilbert, F. R. Stevens, A. E. Gaughan, V. D. Blondel, and A. J. Tatem, Dynamic population mapping using mobile phone data, Proc. Natl. Acad. Sci. USA, vol. 111, no. 45, pp. 15888–15893, 2014.

455

L. M. A. Bettencourt, The origins of scaling in cities, Science, vol. 340, no. 6139, pp. 1438–1441, 2013.

456

M. Barthelemy, P. Bordin, H. Berestycki, and M. Gribaudi, Self-organization versus top-down planning in the evolution of a city, Sci. Rep., vol. 3, no. 1, p. 2153, 2013.

457

D. Q. Li, Y. N. Jiang, R. Kang, and S. Havlin, Spatial correlation analysis of cascading failures: Congestions and blackouts, Sci. Rep., vol. 4, no. 1, p. 5381, 2014.

458

Z. Su, L. X. Li, H. P. Peng, J. Kurths, J. H. Xiao, and Y. X. Yang, Robustness of interrelated traffic networks to cascading failures, Sci. Rep., vol. 4, no. 1, p. 5413, 2014.

459
Y. L. Wang, Y. Zheng, and Y. X, Xue, Travel time estimation of a path using sparse trajectories, in Proc. 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York City, NY, USA, 2014, pp. 25−34.https://doi.org/10.1145/2623330.2623656
DOI
460

R. I. McDonald, P. Green, D. Balk, B. M. Fekete, C. Revenga, M. Todd, and M. Montgomery, Urban growth, climate change, and freshwater availability, Proc. Natl. Acad. Sci. USA, vol. 108, no. 15, pp. 6312–6317, 2011.

461

C. Dalin, M. Konar, N. Hanasaki, A. Rinaldo, and I. Rodriguez-Iturbe, Evolution of the global virtual water trade network, Proc. Natl. Acad. Sci. USA, vol. 109, no. 16, pp. 5989–5994, 2012.

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Publication history

Received: 14 April 2021
Revised: 26 June 2021
Accepted: 02 July 2021
Published: 23 August 2021
Issue date: June 2021

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