The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Understanding user behavior in online social networks: a survey
Abstract
Currently, online social networks such as Facebook, Twitter, Google+, LinkedIn, and Foursquare have become extremely popular all over the world and play a significant role in people¿s daily lives. People access OSNs using both traditional desktop PCs and new emerging mobile devices. With more than one billion users worldwide, OSNs are a new venue of innovation with many challenging research problems. In this survey, we aim to give a comprehensive review of state-of-the-art research related to user behavior in OSNs from several perspectives. First, we discuss social connectivity and interaction among users. Also, we investigate traffic activity from a network perspective. Moreover, as mobile devices become a commodity, we pay attention to the characteristics of social behaviors in mobile environments. Last but not least, we review malicious behaviors of OSN users, and discuss several solutions to detect misbehaving users. Our survey serves the important roles of both providing a systematic exploration of existing research highlights and triggering various potentially significant research in these topics.
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Cites
What is Twitter, a social network or a news media?
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BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks
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Self-citationUser interactions in social networks and their implications
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Characterizing user behavior in online social networks
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Find me if you can: improving geographical prediction with social and spatial proximity
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Social Serendipity: Mobilizing Social Software
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Aiding the detection of fake accounts in large scale social online services
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An analysis of social network-based Sybil defenses
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Machine learning algorithms for social media analysis: A survey
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Advances in Crowd Analysis for Urban Applications Through Urban Event Detection
Kaiser, M. S., Lwin, K. T., Mahmud, M., Hajializadeh, D., Chaipimonplin, T., Sarhan, A., & Hossain, M. A. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2771746 · Cited by 98
A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embedding
Hamdi, T., Slimi, H., Bounhas, I., & Slimani, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-36987-3_17 · Cited by 70
Emergent properties, models, and laws of behavioral similarities within groups of twitter users
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2020) · Computer Communications · doi:10.1016/j.comcom.2019.10.019 · Cited by 40
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Huang, W.-C., Zhang, W., Liang, Y., Bei, Y., Chen, Y., Feng, T., Pan, X., Tan, Z., Wang, Y., Wei, T., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.06052 · Cited by 70
Self-citation
Metadata
- Authors
- Jin, L., Chen, Y., Wang, T., Hui, P., & Vasilakos, A. V.
- Venue
- IEEE Communications Magazine
- Published
- 2013-09
- Volume
- 51
- Issue
- 9
- Pages
- 144-150
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2152202842
- Cited by
- 316
- References
- 19
- FWCI
- 22.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jin, L., Chen, Y., Wang, T., Hui, P., & Vasilakos, A. V. (2013). Understanding user behavior in online social networks: a survey. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2013.6588663
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