Abstract

The presence of bots has been felt in many aspects of social media. Twitter, one example of social media, has especially felt the impact, with bots accounting for a large portion of its users. These bots have been used for malicious tasks such as spreading false information about political candidates and inflating the perceived popularity of celebrities. Furthermore, these bots can change the results of common analyses performed on social media. It is important that researchers and practitioners have tools in their arsenal to remove them. Approaches exist to remove bots, however they focus on precision to evaluate their model at the cost of recall. This means that while these approaches are almost always correct in the bots they delete, they ultimately delete very few, thus many bots remain. We propose a model which increases the recall in detecting bots, allowing a researcher to delete more bots. We evaluate our model on two real-world social media datasets and show that our detection algorithm removes more bots from a dataset than current approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages533-540
Number of pages8
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period8/18/168/21/16

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social media
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popularity
candidacy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

Cite this

Morstatter, F., Wu, L., Nazer, T. H., Carley, K. M., & Liu, H. (2016). A new approach to bot detection: Striking the balance between precision and recall. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 533-540). [7752287] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752287

A new approach to bot detection : Striking the balance between precision and recall. / Morstatter, Fred; Wu, Liang; Nazer, Tahora H.; Carley, Kathleen M.; Liu, Huan.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 533-540 7752287.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Morstatter, F, Wu, L, Nazer, TH, Carley, KM & Liu, H 2016, A new approach to bot detection: Striking the balance between precision and recall. in Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752287, Institute of Electrical and Electronics Engineers Inc., pp. 533-540, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 8/18/16. https://doi.org/10.1109/ASONAM.2016.7752287
Morstatter F, Wu L, Nazer TH, Carley KM, Liu H. A new approach to bot detection: Striking the balance between precision and recall. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 533-540. 7752287 https://doi.org/10.1109/ASONAM.2016.7752287
Morstatter, Fred ; Wu, Liang ; Nazer, Tahora H. ; Carley, Kathleen M. ; Liu, Huan. / A new approach to bot detection : Striking the balance between precision and recall. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 533-540
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