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Social media has more than three billion users sharing events, comments, and feelings throughout the world. It serves as a critical information source with large volumes, high velocity, and a wide variety of data. The previous studies on information spreading, relationship analyzing, and individual modeling, etc., have been heavily conducted to explore the tremendous social and commercial values of social media data. This survey studies the previous literature and the existing applications from a practical perspective. We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques, such as topic analysis, time series analysis, sentiment analysis, and network analysis. After that, we present the impacts of such applications in three different areas, including disaster management, healthcare, and business. Finally, we list existing challenges and suggest promising future research directions in terms of data privacy, 5G wireless network, and multilingual support.


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Survey on Data Analysis in Social Media: A Practical Application Aspect

Show Author's information Qixuan HouMeng Han( )Zhipeng Cai( )
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
Data-driven Intelligence Research (DIR) Lab of College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA 30060, USA
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

Abstract

Social media has more than three billion users sharing events, comments, and feelings throughout the world. It serves as a critical information source with large volumes, high velocity, and a wide variety of data. The previous studies on information spreading, relationship analyzing, and individual modeling, etc., have been heavily conducted to explore the tremendous social and commercial values of social media data. This survey studies the previous literature and the existing applications from a practical perspective. We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques, such as topic analysis, time series analysis, sentiment analysis, and network analysis. After that, we present the impacts of such applications in three different areas, including disaster management, healthcare, and business. Finally, we list existing challenges and suggest promising future research directions in terms of data privacy, 5G wireless network, and multilingual support.

Keywords:

social media, topic analysis, time series analysis, sentiment analysis, network analysis, disaster management, bio-surveillance, business intelligence
Received: 21 May 2020 Accepted: 08 June 2020 Published: 16 November 2020 Issue date: December 2020
References(122)
[1]
A. M. Kaplan and M. Haenlein, Users of the world, unite! The challenges and opportunities of social media, Business Horizons, vol. 53, no. 1, pp. 59-68, 2010.
[2]
P. N. Howard and M. R. Parks, Social media and political change: Capacity, constraint, and consequence, Journal of Communication, vol. 62, no. 2, pp. 359-362, 2012.
[3]
C. T. Carr and R. A. Hayes, Social media: Defining, developing, and divining, Atlantic Journal of Communication, vol. 23, no. 1, pp. 46-65, 2015.
[4]
Data never sleeps 5.0, , 2020.
[5]
E. Ortiz-Ospina, The rise of social media, , 2019.
[6]
S. Stieglitz, M. Mirbabaie, B. Ross, and C. Neuberger, Social media analytics—Challenges in topic discovery, data collection, and data preparation, International Journal of Information Management, vol. 39, pp. 156-168, 2018.
[7]
I. Moalla, A. Nabli, L. Bouzguenda, and M. Hammami, Data warehouse design approaches from social media: Review and comparison, Social Network Analysis and Mining, vol. 7, no. 1, p. 5, 2017.
[8]
J. W. Cao, K. Basoglu, H. Sheng, and P. B. Lowry, A systematic review of social networking research in information systems, Communications of the Association for Information Systems, vol. 36, no. 1, 2015.
[9]
S. Karimi, J. Yin, and C. Paris, Classifying microblogs for disasters, in Proc. 18th Australasian Document Computing Symp., Brisbane, Australia, 2013, pp. 26-33.
[10]
T. Sakaki, M. Okazaki, and Y. Matsuo, Earthquake shakes twitter users: Real-time event detection by social sensors, in Proc. 19th Int. Conf. World Wide Web, Raleigh, NC, USA, 2010, pp. 851-860.
[11]
J. K. Harris, R. Mansour, B. Choucair, J. Olson, C. Nissen, and J. Bhatt, Health department use of social media to identify foodborne illness, Chicago, IL, USA, 2013-2014, Morbidity and Mortality Weekly Report, vol. 63, no. 32, pp. 681-685, 2014.
[12]
E. O. Nsoesie, S. A. Kluberg, and J. S. Brownstein, Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports, Preventive Medicine, vol. 67, pp. 264-269, 2014.
[13]
M. Han, M. Y. Yan, Z. P. Cai, and Y. S. Li, An exploration of broader influence maximization in timeliness networks with opportunistic selection, Journal of Network and Computer Applications, vol. 63, pp. 39-49, 2016.
[14]
P. P. Xu, Y. C. Wu, E. X. Wei, T. Q. Peng, S. X. Liu, J. J. H. Zhu, and H. M. Qu, Visual analysis of topic competition on social media, IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2012-2021, 2013.
[15]
W. W. Dou, X. Y. Wang, D. Skau, W. Ribarsky, and M. X. Zhou, LeadLine: Interactive visual analysis of text data through event identification and exploration, presented at 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), Seattle, WA, USA, 2012, pp. 93-102.
[16]
S. M. Chen, X. R. Yuan, Z. H. Wang, C. Guo, J. Liang, Z. C. Wang, X. L. Zhang, and J. W. Zhang, Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data, IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 270-279, 2016.
[17]
A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller, Twitinfo: Aggregating and visualizing microblogs for event exploration, in Proc. SIGCHI Conf. Human Factors in Computing Systems, Vancouver, Canada, 2011, pp. 227-236.
[18]
M. Cataldi, L. Di Caro, and C. Schifanella, Emerging topic detection on twitter based on temporal and social terms evaluation, in Proc. 10th Int. Workshop on Multimedia Data Mining, Washington, DC, USA, 2010, pp. 1-10.
[19]
S. Sizov, GeoFolk: Latent spatial semantics in web 2.0 social media, in Proc. 3rd ACM Int.l Conf. Web Search and Data Mining, New York, NY, USA, 2010, pp. 281-290.
[20]
S. Y. Song, Q. D. Li, and N. Zheng, A spatio-temporal framework for related topic search in micro-blogging, presented at International Conference on Active Media Technology, A. An, P. Lingras, S. Petty, and R. Huang, eds. Berlin, Germany: Springer, 2010, pp. 63-73.
[21]
S. C. Han and B. H. Kang, Identifying the relevance of social issues to a target, presented at 2012 IEEE 19th Int. Conf. Web Services, Honolulu, HI, USA, 2012, pp. 666-667.
[22]
S. Y. Song, Q. D. Li, and X. L. Zheng, Detecting popular topics in micro-blogging based on a user interest-based model, presented at 2012 Int. Joint Conf. Neural Networks (IJCNN), Brisbane, Australia, 2012, pp. 1-8.
[23]
B. Hu, Z. Song, and M. Ester, User features and social networks for topic modeling in online social media, presented at 2012 IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, Istanbul, Turkey, 2012, pp. 202-209.
[24]
K. Y. Kamath, J. Caverlee, K. Lee, and Z. Y. Cheng, Spatio-temporal dynamics of online memes: A study of geo-tagged tweets, in Proc. 22nd Int. Conf. World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 667-678.
[25]
Z. Q. Ma, W. W. Dou, X. Y. Wang, and S. Akella, Tag- latent dirichlet allocation: Understanding hashtags and their relationships, presented at 2013 IEEE/WIC/ACM Int. Joint Conf. Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Atlanta, GA, USA, 2013, pp. 260-267.
[26]
P. Bogdanov, M. Busch, J. Moehlis, A. K. Singh, and B. K. Szymanski, The social media genome: Modeling individual topic-specific behavior in social media, in Proc. 2013 IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, Niagara, Canada, 2013, pp. 236-242.
[27]
G. Jang and S. H. Myaeng, Analysis of spatially oriented topic versatility over time on social media, in Proc. 2015 IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, Paris, France, 2015, pp. 573-578.
[28]
F. Z. Yao, K. C. C. Chang, and R. H. Campbell, Ushio: Analyzing news media and public trends in twitter, in Proc. 2015 IEEE/ACM 8th Int. Conf. Utility and Cloud Computing (UCC), Limassol, Cyprus, 2015, pp. 424-429.
[29]
S. S. Qian, T. Z. Zhang, C. S. Xu, and J. Shao, Multi-modal event topic model for social event analysis, IEEE Transactions on Multimedia, vol. 18, no. 2, pp. 233-246, 2016.
[30]
A. Musaev and Q. X. Hou, Gathering high quality information on landslides from twitter by relevance ranking of users and tweets, presented at 2016 IEEE 2nd Int. Conf. Collaboration and Internet Computing(CIC), Pittsburgh, PA, USA, 2016, pp. 276-284.
[31]
V. A. Rohani, S. Shayaa, and G. Babanejaddehaki, Topic modeling for social media content: A practical approach, presented at 2016 3rd Int. Conf. Computer and Information Sciences (ICCOINS), Kuala Lumpur, Malaysia, 2016, pp. 397-402.
[32]
A. Argyrou, S. Giannoulakis, and N. Tsapatsoulis, Topic modelling on Instagram hashtags: An alternative way to automatic image annotation? presented at 2018 13th Int. Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Zaragoza, Spain, 2018, pp. 61-67.
[33]
A. Ejaz, S. K. Fatima, Q. N. Rajput, and S. A. Khoja, Analyzing news from electronic media and topics discussed on social media using ontology, presented at 2018 5th Int. Conf. Social Networks Analysis, Management and Security (SNAMS), Valencia, Spain, 2018, pp. 349-354.
[34]
T. C. Fu, D. C. M. Sze, P. K. C. Leung, K. Y. Hung, and F. L. Chung, Analysis and visualization of time series data from consumer-generated media and news archives, presented at 2007 IEEE/WIC/ACM Int. Conf. Web Intelligence and Intelligent Agent Technology-Workshops, Silicon Valley, CA, USA, 2007, pp. 259-262.
[35]
M. Mathioudakis and N. Koudas, Twittermonitor: Trend detection over the twitter stream, in Proc. 2010 ACM SIGMOD Int. Conf. Management of Data, Indianapolis, IN, USA, 2010, pp. 1155-1158.
[36]
J. Yang and J. Leskovec, Patterns of temporal variation in online media, in Proc. 4th ACM Int. Conf. Web Search and Data Mining, Hong Kong, China, 2011, pp. 177-186.
[37]
W. X. Zhao, B. H. Shu, J. Jiang, Y. Song, H. F. Yan, and X. M. Li, Identifying event-related bursts via social media activities, in Proc. 2012 Joint Conf. Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, 2012, pp. 1466-1477.
[38]
J. Chae, D. Thom, H. Bosch, Y. Jang, R. Maciejewski, D. S. Ebert, and T. Ertl, Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition, presented at 2012 IEEE Conf. Visual Analytics Science and Technology (VAST), Seattle, WA, USA, 2012, pp. 143-152.
[39]
H. I. Ahn and W. S. Spangler, Sales prediction with social media analysis, presented at 2014 Annu. SRII Global Conf., San Jose, CA, USA, 2014, pp. 213-222.
[40]
K. Kucher, A. Kerren, C. Paradis, and M. Sahlgren, Visual analysis of stance markers in online social media, presented at 2014 IEEE Conf. Visual Analytics Science and Technology (VAST), Paris, France, 2014, pp. 259-260.
[41]
P. Healy, G. Hunt, S. Kilroy, T. Lynn, J. P. Morrison, and S. Venkatagiri, Evaluation of peak detection algorithms for social media event detection, presented at 2015 10th Int. Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Trento, Italy, 2015, pp. 1-9.
[42]
Y. Tsuboi, A. Jatowt, and K. Tanaka, Product purchase prediction based on time series data analysis in social media, presented at 2015 IEEE/WIC/ACM Int. Conf. Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, 2015, pp. 219-224.
[43]
K. Nusratullah, S. A. Khan, A. Shah, and W. H. Butt, Detecting changes in context using time series analysis of social network, presented at 2015 SAI Intelligent Systems Conf. (IntelliSys), London, UK, 2015, pp. 996-1001.
[44]
S. D. Johnson and K. Y. Ni, A pricing mechanism using social media and web data to infer dynamic consumer valuations, presented at 2015 IEEE Int. Conf. Big Data (Big Data), Santa Clara, CA, USA, 2015, pp. 2868-2870.
[45]
K. Zhao, Y. Q. Zhang, B. G. Li, and C. F. Zhou, Repost number prediction of micro-blog on Sina Weibo using time series fitting and regression analysis, presented at 2015 Int. Conf. Identification, Information, and Knowledge in the Internet of Things (IIKI), Beijing, China, 2015, pp. 66-69.
[46]
M. Ni, Q. He, and J. Gao, Forecasting the subway passenger flow under event occurrences with social media, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1623-1632, 2017.
[47]
E. A. Dahouei, A cloud-based dashboard for time series analysis on hot topics from social media, presented at 2017 Int. Conf. Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2017, pp. 1-6.
[48]
C. Comito, D. Falcone, and D. Talia, A peak detection method to uncover events from social media, presented at 2017 IEEE Int. Conf. Data Science and Advanced Analytics (DSAA), Tokyo, Japan, 2017, pp. 459-467.
[49]
H. X. Rui and A. Whinston, Designing a social-broadcasting-based business intelligence system, ACM Transactions on Management Information Systems (TMIS), vol. 2, no. 4, pp. 1-19, 2012.
[50]
B. Yuan, Y. Liu, and H. Li, Sentiment classification in Chinese microblogs: Lexicon-based and learning-based approaches, International Proceedings of Economics Development and Research, vol. 68, pp. 1-5, 2013.
[51]
Y. Yu, W. J. Duan, and Q. Cao, The impact of social and conventional media on firm equity value: A sentiment analysis approach, Decision Support Systems, vol. 55, no. 4, pp. 919-926, 2013.
[52]
Z. X. Wang, V. J. C. Tong, and H. C. Chin, Enhancing machine-learning methods for sentiment classification of web data, in Information Retrieval Technology, A. Jaafar, N. M. Ali, S. A. M. Noah, A. F. Smeaton, P. Bruza, Z. A. Bakar, N. Jamil, and T. M. T. Sembok, eds. Cham, Switzerland: Springer, 2014, pp. 394-405.
[53]
Z. X. Wang, C. S. Chong, L. Lan, Y. P. Yang, S. B. Ho, and J. C. Tong, Fine-grained sentiment analysis of social media with emotion sensing, presented at 2016 Future Technologies Conf. (FTC), San Francisco, CA, USA, 2016, pp. 1361-1364.
[54]
A. Popescul and L. H. Ungar, Statistical relational learning for link prediction, in Proc. Workshop on Learning Statistical Models from Relational Data at IJCAI-2003, Acapulco, Mexico, 2003, pp. 109-115.
[55]
D. L. Nowell and J. Kleinberg, The link-prediction problem for social networks, Journal of the American Society for Information Science and Technology, vol. 58, no. 7, pp. 1019-1031, 2007.
[56]
G. Rossetti, M. Berlingerio, and F. Giannotti, Scalable link prediction on multidimensional networks, presented at 2011 IEEE 11th Int. Conf. Data Mining Workshops, Vancouver, Canada, 2011, pp. 979-986.
[57]
R. Michalski, P. Kazienko, and D. Krol, Predicting social network measures using machine learning approach, in Proc. 2012 Int. Conf. Advances in Social Networks Analysis and Mining, Washington, DC, USA, 2012, pp. 1056-1059.
[58]
M. A. Smith, NodeXL: Simple network analysis for social media, presented at 2013 Int. Conf. Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 2013, pp. 89-93.
[59]
F. Erlandsson, P. Bródka, A. Borg, and H. Johnson, Finding influential users in social media using association rule learning, Entropy, vol. 18, no. 5, p. 164, 2016.
[60]
J. Li, Z. P. Cai, M. Y. Yan, and Y. S. Li, Using crowdsourced data in location-based social networks to explore influence maximization, presented at IEEE INFOCOM 2016—The 35th Annu. IEEE Int. Conf. Computer Communications, San Francisco, CA, USA, 2016, pp. 1-9.
[61]
X. H. Zhao, F. A. Liu, J. L. Wang, and T. L. Li, Evaluating influential nodes in social networks by local centrality with a coefficient, ISPRS International Journal of Geo-Information, vol. 6, no. 2, p. 35, 2017.
[62]
W. Y. Tang, G. C. Luo, Y. B. Wu, L. Tian, X. Zheng, and Z. P. Cai, A second-order diffusion model for influence maximization in social networks, IEEE Transactions on Computational Social Systems, vol. 6, no. 4, pp. 702-714, 2019.
[63]
T. O. Sprenger, Essays on the information content of microblogs and their use as an indicator of real-world events, PhD dissertation, Technische Universität München, München, Germany, 2011.
[64]
G. K. Palshikar, Simple algorithms for peak detection in time-series, , 2009.
[65]
P. Du, W. A. Kibbe, and S. M. Lin, Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, vol. 22, no. 17, pp. 2059-2065, 2006.
[66]
J. Lehmann, B. Gonçalves, J. J. Ramasco, and C. Cattuto, Dynamical classes of collective attention in twitter, in Proc. 21st Int. Conf. World Wide Web, Lyon, France, 2012, pp. 251-260.
[67]
L. Y. Liu, J. L. Priestley, Y. Y. Zhou, H. E. Ray, and M. Han, A2Text-Net: A novel deep neural network for sarcasm detection, presented at 2019 IEEE 1st Int. Conf. Cognitive Machine Intelligence, Los Angeles, CA, USA, 2019, pp. 118-126.
[68]
S. Desai and M. Han, Social media content analytics beyond the text: A case study of university branding in instagram, in Proc. 2019 ACM Southeast Conf., Kennesaw, GA, USA, 2019, pp. 94-101.
[69]
J. S. He, M. Han, S. L. Ji, T. Y. Du, and Z. Li, Spreading social influence with both positive and negative opinions in online networks, Big Data Mining and Analytics, vol. 2, no. 2, pp. 100-117, 2019.
[70]
C. Banea, R. Mihalcea, and J. Wiebe, A bootstrapping method for building subjectivity lexicons for languages with scarce resources, in Proc. Int. Conf. Language Resources and Evaluation, Marrakech, Morocco, 2008, pp. 2764-2767.
[71]
C. Strapparava and A. Valitutti, WordNet-affect: An affective extension of WordNet, in Proc. 4th Int. Conf. Language Resources and Evaluation, Lisbon, Portugal, 2004, pp. 1083-1086.
[72]
S. Baccianella, A. Esuli, and F. Sebastiani, SentiWordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, in Proc. Int. Conf. Language Resources and Evaluation, Valletta, Malta, 2010, pp. 2200-2204.
[73]
I. Perikos and I. Hatzilygeroudis, A framework for analyzing big social data and modelling emotions in social media, presented at 2018 IEEE 4th Int. Conf. Big Data Computing Service and Applications (BigDataService), Bamberg, Germany, 2018, pp. 80-84.
[74]
M. Han, M. Y. Yan, Z. P. Cai, Y. S. Li, X. Q. Cai, and J. G. Yu, Influence maximization by probing partial communities in dynamic online social networks, Transactions on Emerging Telecommunications Technologies, vol. 28, no. 4, p. e3054, 2017.
[75]
M. Han, M. Y. Yan, J. B. Li, S. L. Ji, and Y. S. Li, Neighborhood-based uncertainty generation in social networks, Journal of Combinatorial Optimization, vol. 28, no. 3, pp. 561-576, 2014.
[76]
M. Han, Z. J. Duan, C. Y. Ai, F. W. Lybarger, Y. S. Li, and A. G. Bourgeois, Time constraint influence maximization algorithm in the age of big data, International Journal of Computational Science and Engineering, vol. 15, nos. 3&4, pp. 165-175, 2017.
[77]
E. Mykhalovskiy and L. Weir, The global public health intelligence network and early warning outbreak detection, Canadian Journal of Public Health, vol. 97, no. 1, pp. 42-44, 2006.
[78]
C. C. Freifeld, K. D. Mandl, B. Y. Reis, and J. S. Brownstein, HealthMap: Global infectious disease monitoring through automated classification and visualization of internet media reports, Journal of the American Medical Informatics Association, vol. 15, no. 2, pp. 150-157, 2008.
[79]
N. Kshetri and J. Voas, The economics of “fake news”, IT Professional, vol. 19, no. 6, pp. 8-12, 2017.
[80]
Report on ECDC/JRC collaboration on development of online tool for epidemic intelligence, , 2011.
[81]
Event-based surveillance, , 2020.
[82]
Severe weather 101-Floods, , 2020.
[83]
F. Cheong and C. Cheong, Social media data mining: A social network analysis of tweets during the Australian 2010-2011 floods, presented at PACIS 2011—15th Pacific Asia Conf. Information Systems: Quality Research in Pacific, Brisbane, Australia, 2011, pp. 1-16.
[84]
D. Yates and S. Paquette, Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake, International Journal of Information Management, vol. 31, no. 1, pp. 6-13, 2011.
[85]
M. Imran, S. Elbassuoni, C. Castillo, F. Diaz, and P. Meier, Extracting information nuggets from disaster-related messages in social media, in Proc. 10th Int. ISCRAM Conf., Baden-Baden, Germany, 2013, pp. 1-10.
[86]
A. M. MacEachren, A. Jaiswal, A. C. Robinson, S. Pezanowski, A. Savelyev, P. Mitra, X. Zhang, and J. Blanford, SensePlace2: GeoTwitter analytics support for situational awareness, presented at 2011 IEEE Conf. Visual Analytics Science and Technology (VAST), Providence, RI, USA, 2011, pp. 181-190.
[87]
A. Musaev, Z. Jiang, S. Jones, P. Sheinidashtegol, and M. Dzhumaliev, Detection of damage and failure events of road infrastructure using social media, in International Conference on Web Services, H. Jin, Q. Wang, and L. J. Zhang, eds. Seattle, WA, USA: Springer, 2018, pp. 134-148.
[88]
D. Murthy and A. J. Gross, Social media processes in disasters: Implications of emergent technology use, Social Science Research, vol. 63, pp. 356-370, 2017.
[89]
A. Sheth, Citizen sensing, social signals, and enriching human experience, IEEE Internet Computing, vol. 13, no. 4, pp. 87-92, 2009.
[90]
M. Laituri and K. Kodrich, On line disaster response community: People as sensors of high magnitude disasters using internet GIS, Sensors, vol. 8, no. 5, pp. 3037-3055, 2008.
[91]
J. Wichmann, Being B2B social: A conversation with Maersk Line’s head of social media, , 2013.
[92]
S. Desai and M. Han, Social media content analytics beyond the text: A case study of university branding in instagram, in Proc. 2019 ACM Southeast Conf., Kennesaw, GA, USA, 2019, pp. 94-101.
[93]
D. Choudhery and C. K. Leung, Social media mining: prediction of box office revenue, in Proc. 21st Int. Database Engineering & Applications Symp., Bristol, UK, 2017, pp. 20-29.
[94]
W. G. Mangold and D. J. Faulds, Social media: The new hybrid element of the promotion mix, Business Horizons, vol. 52, no. 4, pp. 357-365, 2009.
[95]
Attorney general Becerra publicly releases proposed regulations under the California consumer privacy act, , 2019.
[96]
European Commission, General data protection regulation, Article 4(1), , 2018.
[97]
M. Han, Q. L. Han, L. J. Li, J. Li, and Y. S. Li, Maximising influence in sensed heterogeneous social network with privacy preservation, International Journal of Sensor Networks, vol. 28, no. 2, pp. 69-79, 2018.
[98]
X. Zheng, Z. P. Cai, J. G. Yu, C. K. Wang, and Y. S. Li, Follow but no track: Privacy preserved profile publishing in cyber-physical social systems, IEEE Internet of Things Journal, vol. 4, no. 6, pp. 1868-1878, 2017.
[99]
X. Zheng, G. C. Luo, and Z. P. Cai, A fair mechanism for private data publication in online social networks, IEEE Transactions on Network Science and Engineering, vol. 7, no. 2, pp. 880-891, 2020.
[100]
M. Han, L. Li, Y. Xie, J. B. Wang, Z. J. Duan, J. Li, and M. Y. Yan, Cognitive approach for location privacy protection, IEEE Access, vol. 6, pp. 13 466-13 477, 2018.
[101]
Z. P. Cai, Z. B. He, X. Guan, and Y. S. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 4, pp. 577-590, 2018.
[102]
C. Castillo, M. Mendoza, and B. Poblete, Information credibility on twitter, in Proc. 20th Int. Conf. World Wide Web, Hyderabad, India, 2011, pp. 675-684.
[103]
S. Y. Sun, H. Y. Liu, J. He, and X. Y. Du, Detecting event rumors on Sina Weibo automatically, in Asia-Pacific Web Conference, Y. Ishikawa, J. Li, W. Wang, R. Zhang, and W. Zhang, eds. Berlin, Germany: Springer, 2013, pp. 120-131.
[104]
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, Beijing, China, 2012, pp. 1-7.
[105]
S. Kwon, M. Cha, K. Jung, W. Chen, and Y. J. Wang, Prominent features of rumor propagation in online social media, presented at 2013 IEEE 13 th Int. Conf. Data Mining, Dallas, TX, USA, 2013, pp. 1103-1108.
[106]
R. Ennals, D. Byler, J. M. Agosta, and B. Rosario, What is disputed on the web? in Proc. 4th Workshop on Information Credibility, Raleigh, NC, USA, 2010, pp. 67-74.
[107]
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. World Wide Web, Florence, Italy, 2015, pp. 1395-1405.
[108]
A. V. Sathanur, M. Sui, and V. Jandhyala, Assessing strategies for controlling viral rumor propagation on social media—A simulation approach, presented at 2015 IEEE Int. Symp. Technologies for Homeland Security (HST), Waltham, MA, USA, 2015, pp. 1-6.
[109]
C. M. M. Kotteti, X. S. Dong, and L. J. Qian, Multiple time-series data analysis for rumor detection on social media, presented at 2018 IEEE Int. Conf. Big Data (Big Data), Seattle, WA, USA, 2018, pp. 4413-4419.
[110]
Y. G. Lin, Z. P. Cai, X. M. Wang, and F. Hao, Incentive mechanisms for crowdblocking rumors in mobile social networks, IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9220-9232, 2019.
[111]
Y. Q. Qin, M. M. Chi, X. Liu, Y. F. Zhang, Y. J. Zeng, and Z. M. Zhao, Classification of high resolution urban remote sensing images using deep networks by integration of social media photos, presented at IGARSS 2018-2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, Spain, 2018, pp. 7243-7246.
[112]
E. J. Hoffmann, M. Werner, and X. X. Zhu, Building instance classification using social media images, presented at 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France, 2019, pp. 1-4.
[113]
M. Jing, B. W. Scotney, S. A. Coleman, and M. T. McGinnity, The application of social media image analysis to an emergency management system, presented at 2016 11th Int. Conf. Availability, Reliability and Security (ARES), Salzburg, Austria, 2016, pp. 805-810.
[114]
Q. X. Hou, A. Musaev, Y. Yang, and C. Pu, A comparative study of increasing automation in the integration of multilingual social media information, presented at 2017 IEEE 3rd Int. Conf. Collaboration and Internet Computing (CIC), San Jose, CA, USA, 2017, pp. 319-327.
[115]
L. S. Xia, N. Yamashita, and T. Ishida, Analysis on multilingual discussion for Wikipedia translation, presented at 2011 2nd Int. Conf. Culture and Computing, Kyoto, Japan, 2011, pp. 104-109.
[116]
M. J. Fuadvy and R. Ibrahim, Multilingual sentiment analysis on social media disaster data, presented at 2019 Int. Conf. Electrical, Electronics and Information Engineering (ICEEIE), Denpasar, Indonesia, 2019, pp. 269-272.
[117]
X. Zhou, X. Wan, and J. Xiao, Cross-lingual sentiment classification with bilingual document representation learning, in Proc. 54th Annu. Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016, pp. 1403-1412.
[118]
Y. S. Xie, Z. Z. Chen, K. P. Zhang, Y. Cheng, D. K. Honbo, A. Agrawal, and A. N. Choudhary, MuSES: Multilingual sentiment elicitation system for social media data, IEEE Intelligent Systems, vol. 29, no. 4, pp. 34-42, 2014.
[119]
L. Y. Liu and M. Han, Privacy and security issues in the 5g-enabled internet of things, in 5G-Enabled Internet of Things, Y. L. Wu, H. J. Huang, C. X. Wang, and Y. Pan, eds. Boca Raton, FL, USA: CRC Press, 2019, pp. 241-268.
[120]
Cisco annual internet report (2018-2023) white paper, , 2020.
[121]
S. Mattisson, Overview of 5g requirements and future wireless networks, presented at ESSCIRC 2017—43rd IEEE European Solid State Circuits Conf., Leuven, Belgium, 2017, pp. 1-6.
[122]
IAB, Live video streaming: A global perspective, , 2018.
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Publication history

Received: 21 May 2020
Accepted: 08 June 2020
Published: 16 November 2020
Issue date: December 2020

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© The authors 2020

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