Bot detection: Will focusing on recall cause overall performance deterioration?

Tahora H. Nazer, Matthew Davis, Mansooreh Karami, Leman Akoglu, David Koelle, Huan Liu

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

1 Scopus citations

Abstract

Social bots are an effective tool in the arsenal of malicious actors who manipulate discussions on social media. Bots help spread misinformation, promote political propaganda, and inflate the popularity of users and content. Hence, it is necessary to differentiate bot accounts and human users. There are several bot detection methods that approach this problem. Conventional methods either focus on precision regardless of the overall performance or optimize overall performance, say F1, without monitoring its effect on precision or recall. Focusing on precision means that those users marked as bots are more likely than not bots but a large portion of the bots could remain undetected. From a user’s perspective, however, it is more desirable to have less interaction with bots, even if it would incur a loss in precision. This can be achieved by a detection method with higher recall. A trivial, but useless, solution for high recall is to classify every account (human or bot) as bot, hence, resulting in poor overall performance. In this work, we investigate if it is feasible for a method to focus on recall without considerable loss in overall performance. Extensive experiments with recall and precision trade-off suggest that high recall can be achieved without much overall performance deterioration. This research leads to a recall-focused approach to bot detection, REFOCUS, with some lessons learned and future directions.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 12th International Conference, SBP-BRiMS 2019, Proceedings
EditorsRobert Thomson, Christopher Dancy, Ayaz Hyder, Halil Bisgin
PublisherSpringer Verlag
Pages39-49
Number of pages11
ISBN (Print)9783030217402
DOIs
StatePublished - Jan 1 2019
Event12th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2019 - Washington D.C., United States
Duration: Jul 9 2019Jul 12 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11549 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2019
CountryUnited States
CityWashington D.C.
Period7/9/197/12/19

Keywords

  • Bot detection
  • Recall
  • Social bots
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    H. Nazer, T., Davis, M., Karami, M., Akoglu, L., Koelle, D., & Liu, H. (2019). Bot detection: Will focusing on recall cause overall performance deterioration? In R. Thomson, C. Dancy, A. Hyder, & H. Bisgin (Eds.), Social, Cultural, and Behavioral Modeling - 12th International Conference, SBP-BRiMS 2019, Proceedings (pp. 39-49). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11549 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-21741-9_5