Social Media-Driven User Community Finding with Privacy Protection
Xie, Jianye; Wang, Xudong; Liu, Yuwen; Gong, Wenwen; Yan, Chao; Rafique, Wajid; Khan, Maqbool; Khan, Arif Ali (2025-03-03)
Tsinghua University Press
03.03.2025
J. Xie et al., "Social Media-Driven User Community Finding with Privacy Protection," in Tsinghua Science and Technology, vol. 30, no. 4, pp. 1782-1792, August 2025, doi: 10.26599/TST.2024.9010065.
https://creativecommons.org/licenses/by/4.0/
© 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/).
https://creativecommons.org/licenses/by/4.0/
© 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/).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503192110
https://urn.fi/URN:NBN:fi:oulu-202503192110
Tiivistelmä
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.
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.
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