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Leveraging node neighborhoods and egograph topology for better bot detection in social graphs

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Abstract

Due to their popularity, online social networks are a popular target for spam, scams, malware distribution and more recently state-actor propaganda. In this paper, we review a number of recent approaches to fake account and bot classification. Based on this review and our experiments, we propose our own method which leverages the social graph’s topology and differences in egographs of legitimate and fake user accounts to improve identification of the latter. We evaluate our approach against other common approaches on a real-world dataset of users of the social network Twitter.

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Notes

  1. Twitter developer policy, accessed November 15, 2020: https://developer.twitter.com/en/developer-terms/agreement-and-policy.

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Acknowledgements

This work was partly supported by the Institute for Information & Communications Technology Promotion (2015-0-00310-SW.StarLab, 2017-0-01772-VTT, 2018-0-00622-RMI, 2019-0-01367-BabyMind) and the Korea Institute for Advancement Technology (P0006720-GENKO) Grant funded by the Korean government.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea

    Björn Bebensee & Nagmat Nazarov

  2. Artificial Intelligence Institute (AIIS), Seoul National University, Seoul, South Korea

    Byoung-Tak Zhang

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  1. Björn Bebensee
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  2. Nagmat Nazarov
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  3. Byoung-Tak Zhang
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Correspondence to Björn Bebensee.

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Appendix

Appendix

1.1 Feature correlation heatmap

See Fig. 12.

Fig. 12
figure 12

Correlation heatmap of all features scraped or generated (neighborhood features and egograph features)

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Bebensee, B., Nazarov, N. & Zhang, BT. Leveraging node neighborhoods and egograph topology for better bot detection in social graphs. Soc. Netw. Anal. Min. 11, 10 (2021). https://doi.org/10.1007/s13278-020-00713-z

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