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Open Access

Social Media-Driven User Community Finding with Privacy Protection

College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China
Department of Computer Science and Technology, Tsinghua University, Beijing 100083, China
School of Computer Science, Qufu Normal University, Qufu 273165, China
Department of Electrical and Software Engineering, University of Calgary, Colgary, T2N1N4, Canada
Software Competence Center Hagenberg, Hagenberg 4232, Austria
M3S Empirical Software Engineering Research Unit, University of Oulu, Oulu 90570, Finland
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Abstract

In the digital era, social media platforms play a crucial role in forming user communities, yet the challenge of protecting user privacy remains paramount. This paper proposes a novel framework for identifying and analyzing user communities within social media networks, emphasizing privacy protection. In detail, we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users, thereby maintaining confidentiality. Finally, we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches, demonstrating its effectiveness in community detection while upholding stringent privacy standards. This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.

Keywords

multi-source social mediaprivacy-preservationuser community findinghash

References

[1]

B. Auxier and M. Anderson, Social media use in 2021, Pew Research Center, vol. 27, pp. 1–4, 2021.
Google Scholar
[2]

D. J. Hughes, M. Rowe, M. Batey, and A. Lee, A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage, Comput. Human Behav., vol. 28, no. 2, pp. 561–569, 2012.
Crossref Google Scholar
[3]

W. L. Bennett and A. Segerberg, The logic of connective action: Digital media and the personalization of contentious politics, Inform. Commun. Soc., vol. 15, no. 5, pp. 739–768, 2012.
Crossref Google Scholar
[4]

J. Poushter, Smartphone ownership and internet usage continues to climb in emerging economies, Pew Research Center, vol. 22, no. 1, pp. 1–44, 2016.
Google Scholar
[5]
R. Magrath, J. Cleland, and E. Anderson, Introducing the Palgrave Handbook of Masculinity and Sport, in The Palgrave Handbook of Masculinity and Sport, R. Magrath, J. Cleland, and E. Anderson, eds. Cham, Switzerland: Palgrave Macmillan, 2020, pp. 1–16.
Crossref
[6]
A. Pavlidis and D. Rowe, The sporting bubble as gilded cage: Gendered professional sport in pandemic times and beyond, M/C J., vol. 24, no. 1, 2021.
Crossref
[7]

Y. Gao, J. Wang, Z. Li, and Z. Peng, The social media big data analysis for demand forecasting in the context of globalization: Development and case implementation of innovative frameworks, J. Organ. End User Comput., vol. 35, no. 3, pp. 1–15, 2023.
Crossref Google Scholar
[8]

A. Illia, A. Lawson-Body, J. Jackson, V. Hinsz, and L. Lawson-Body, Cybersecurity and social media networks for donations: An empirical investigation of triad of trust, commitment, and loyalty, J. Organ. End User Comput., vol. 35, no. 1, pp. 1–26, 2023.
Crossref Google Scholar
[9]

L. Qi, X. Xu, X. Wu, Q. Ni, Y. Yuan, and X. Zhang, Digital-twin-enabled 6G mobile network video streaming using mobile crowdsourcing, IEEE J. Sel. Areas Commun., vol. 41, no. 10, pp. 3161–3174, 2023.
Crossref Google Scholar
[10]

F. Wang, G. Li, Y. Wang, W. Rafique, M. R. Khosravi, G. Liu, Y. Liu, and L. Qi, Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city, ACM Trans. Internet Technol., vol. 23, no. 3, pp. 44, 2023.
Crossref Google Scholar
[11]

B. Liu, S. Tang, X. Sun, Q. Chen, J. Cao, J. Luo, and S. Zhao, Context-aware social media user sentiment analysis, Tsinghua Science and Technology, vol. 25, no. 4, pp. 528–541, 2020.
Crossref Google Scholar
[12]

X. Xu, J. Gu, H. Yan, W. Liu, L. Qi, and X. Zhou, Reputation-aware supplier assessment for blockchain-enabled supply chain in industry 4.0, IEEE Trans. Industr. Inform., vol. 19, no. 4, pp. 5485–5494, 2023.
Crossref Google Scholar
[13]

X. Xu, H. Li, Z. Li, and X. Zhou, Safe: Synergic data filtering for federated learning in cloud-edge computing, IEEE Trans. Industr. Inform., vol. 19, no. 2, pp. 1655–1665, 2023.
Crossref Google Scholar
[14]
D. Li and J. A. Esquivel, Trust-aware hybrid collaborative recommendation with locality-sensitive hashing, Tsinghua Science and Technology, DOI: 10.26599/TST.2023.9010096.
Crossref
[15]

L. Kong, G. Li, W. Rafique, S. Shen, Q. He, M. R. Khosravi, R. Wang, and L. Qi, Time-aware missing healthcare data prediction based on ARIMA model, IEEE/ACM Trans. Computat. Biol. Bioinform., vol. 21, no. 4, pp. 1042–1050, 2024.
Crossref Google Scholar
[16]
S. Velichety and S. Ram, Finding a needle in the haystack-recommending online communities on social media platforms using network and design science, Forthcoming, Journal of Association for Information Systems, vol. 22, no. 5, p. 1285, 2021.
Crossref
[17]
E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, Finding high-quality content in social media, in Proc. 2008 Int. Conf. Web Search and Data Mining, Palo Alto, CA, USA, 2008, pp. 183–194.
Crossref
[18]

B. Widjajanta, S. H. Senen, M. Masharyono, L. Lisnawati, and C. P. Anggraeni, The impact of social media usage and self-esteem on conspicuous consumption: Instagram user of Hijabers Community Bandung member, International Journal of eBusiness and eGovernment Studies, vol. 10, no. 2, pp. 1–13, 2018.
Google Scholar
[19]
D. Bär, N. Pröllochs, and S. Feuerriegel, Finding Qs: Profiling QAnon supporters on parler, in Proc. 17 th Int. AAAI Conf. Web and Social Media, Limassol, Cyprus, 2023, pp. 34–46.
Crossref
[20]

P. S. Khorgade, S. S. Sherekar, and V. M. Thakare, Social media protection framework against the various attacks using data mining, J. Emerg. Technol. Innov. Res., vol. 6, no. 5, pp. 39–43, 2019.
Google Scholar
[21]

J. Taylor and C. Pagliari, Mining social media data: How are research sponsors and researchers addressing the ethical challenges? Res. Ethics, vol. 14, no. 2, pp. 1–39, 2018.
Crossref Google Scholar
[22]

K. Lin, Integrating ethical guidelines and situated ethics for researching social-media-based interactions: Lessons from a virtual ethnographic case study with Chinese youth, J. Inf. Ethics, vol. 25, no. 1, pp. 114–131, 2016.
Google Scholar
[23]
G. Samuel, G. E. Derrick, and T. N. Van Leeuwen, Ethical challenges around the use of social media data: Views of researchers and research ethics committee members, in Proc. 23 rd Int. Conf. Science and Technology Indicators, Leiden, The Netherlands, 2018, pp. 221–227.
Crossref
[24]

A. N. Abdul and Y. Sultan, Social media: The importance and security of user data, Addaiyan Journal of Arts, Humanities and Social Sciences, vol. 4, no. 2, pp. 39–48, 2022.
Crossref Google Scholar
[25]
N. Moorosi and V. Marivate, Privacy in mining crime data from social media: A South African perspective, in Proc. 2015 Second Int. Conf. Information Security and Cyber Forensics, Cape Town, South Africa, 2015, pp. 171–175.
Crossref
[26]
Z. Zhang, LDPCD: A novel method for locally differentially private community detection, Computat. Intell. Neurosc., vol. 2022, p. 4080047, 2022.
Crossref
[27]
L. Shang, Z. Kou, Y. Zhang, J. Chen, and D. Wang, A privacy-aware distributed knowledge graph approach to QoIS-driven COVID-19 misinformation detection, in Proc. 2022 IEEE/ACM 30 th Int. Symp. Quality of Service. Oslo, Norway, 2022, pp. 1–10.
Crossref
[28]
C. Geeng and A. Hiniker, LGBTQ privacy concerns on social media, arXiv preprint arXiv: 2112.00107, 2021.
[29]
T. Georgiou, A. El Abbadi, and X. Yan, Privacy-preserving community-aware trending topic detection in online social media, in Proc. 31 st Annu. IFIP WG 11.3 Conf. Data and Applications Security and Privacy XXXI, Philadelphia, PA, USA, 2017, pp. 205–224.
Crossref
[30]

L. Qi, W. Lin, X. Zhang, W. Dou, X. Xu, and J. Chen, A correlation graph based approach for personalized and compatible web APIs recommendation in mobile APP development, IEEE Trans. Knowl. Data Eng., vol. 35, no. 6, pp. 5444–5457, 2023.
Google Scholar
[31]

F. Wang, H. Zhu, G. Srivastava, S. Li, M. R. Khosravi, and L. Qi, Robust collaborative filtering recommendation with user-item-trust records, IEEE Trans. Computat. Soc. Syst., vol. 9, no. 4, pp. 986–996, 2022.
Crossref Google Scholar
[32]
J. S. Breese, D. Heckerman, and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, arXiv preprint arXiv: 1301.7363, 2013.
[33]
J. Zhu, P. He, Z. Zheng, and M. R. Lyu, A privacy-preserving QoS prediction framework for web service recommendation, in Proc. 2015 IEEE Int. Conf. Web Services, New York, NY, USA, 2015, pp. 241–248.
Crossref
[34]

D. Li, C. Chen, Q. Lv, L. Shang, Y. Zhao, T. Lu, and N. Gu, An algorithm for efficient privacy-preserving item-based collaborative filtering, Future Gener. Comput. Syst., vol. 55, pp. 311–320, 2016.
Crossref Google Scholar
[35]
H. Dai, Y. Xu, G. Chen, W. Dou, C. Tian, X. Wu, and T. He, ROSE: Robustly safe charging for wireless power transfer, IEEE Trans. Mob. Comput., vol. 21, no. 6, pp. 2180–2197, 2022.
Crossref
[36]

H. Dai, J. Yu, M. Li, W. Wang, A. X. Liu, J. Ma, L. Qi, and G. Chen, Bloom filter with noisy coding framework for multi-set membership testing, IEEE Trans. Knowl. Data Eng., vol. 35, no. 7, pp. 6710–6724, 2023.
Google Scholar
[37]

Z. Li, X. Xu, T. Hang, H. Xiang, Y. Cui, L. Qi, and X. Zhou, A knowledge-driven anomaly detection framework for social production system, IEEE Trans. Computat. Soc. Syst., vol. 11, no. 3, pp. 3179–3192, 2024.
Crossref Google Scholar
[38]

H. Dai, X. Wang, X. Lin, R. Gu, S. Shi, Y. Liu, W. Dou, and G. Chen, Placing wireless chargers with limited mobility, IEEE Trans. Mob. Comput., vol. 22, no. 6, pp. 3589–3603, 2023.
Crossref Google Scholar
[39]
R. Gu, K. Zhang, Z. Xu, Y. Che, B. Fan, H. Hou, H. Dai, L. Yi, Y. Ding, G. Chen, et al., Fluid: Dataset abstraction and elastic acceleration for cloud-native deep learning training jobs, in Proc. 2022 IEEE 38 th Int. Conf. Data Engineering, Kuala Lumpur, Malaysia, 2022, pp. 2182–2195.
Crossref
[40]
X. Xu, S. Tang, L. Qi, X. Zhou, F. Dai, and W. Dou, CNN partitioning and offloading for vehicular edge networks in web3, IEEE Commun. Mag., vol. 61, no. 8, pp. 36–42, 2023.
Crossref
[41]

R. Gu, Y. Chen, S. Liu, H. Dai, G. Chen, K. Zhang, Y. Che, and Y. Huang, Liquid: Intelligent resource estimation and network-efficient scheduling for deep learning jobs on distributed GPU clusters, IEEE Trans. Parallel Distrib. Syst., vol. 33, no. 11, pp. 2808–2820, 2022.
Google Scholar
Tsinghua Science and Technology
Volume 30 Issue 4,
August 2025
Pages 1782-1792
DOI: 10.26599/TST.2024.9010065
Cite this article:
Xie J, Wang X, Liu Y, et al. Social Media-Driven User Community Finding with Privacy Protection. Tsinghua Science and Technology, 2025, 30(4): 1782-1792. https://doi.org/10.26599/TST.2024.9010065

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Received: 07 February 2024
Revised: 11 March 2024
Accepted: 24 March 2023
Published: 03 March 2025
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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