The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
User Identity Linkage across Online Social Networks: A Review
Abstract
The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.
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Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
Self-citationAn overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference Attacks
Zhang, S., Yuan, W., & Yin, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3295601 · Cited by 97
Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges
Huang, B., Chen, C., & Shu, K. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715076 · Cited by 42
“…digital realm, authorship attribution helps safe-guard the integrity of content by preventing deceptive social media activities (Hazell, 2023), detecting account compromises (Barbon et al., 2017), and linking user profiles across various social networks (Shu et al., 2017, Sinnott and Wang, 2021).”
Self-citationLiu, H., Wang, Y., Zhang, Z., Deng, J., Chen, C., & Zhang, L. Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103720 · Cited by 34
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Metadata
- Authors
- Shu, K., Wang, S., Tang, J., Zafarani, R., & Liu, H.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2017-03-22
- Volume
- 18
- Issue
- 2
- Pages
- 5-17
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2598689838
- Cited by
- 267
- References
- 70
- FWCI
- 29.02
- Type
- journal article
- Language
- en
- Open access
- closed
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