Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2016

Abstract

The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling framework that includes a rich set of features and classifier bank. We conduct extensive experiments to evaluate the performances of different classifiers under varying time windows, identify the key features of bots, and infer about bots in a larger Twitter population. Our analysis encompasses more than 159K bot and human (non-bot) accounts in Twitter. The results provide interesting insights on the behavioral traits of both benign and malicious bots

Keywords

Bot profiling, Classification, Feature extraction, Social media

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

Social informatics: 8th International Conference, SocInfo 2016, Bellevue, WA, November 11-14: Proceedings

Volume

10046

First Page

92

Last Page

109

ISBN

9783319478807

Identifier

10.1007/978-3-319-47880-7_6

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Comments

Submit request for dataset at https://larc.smu.edu.sg/twitter-bot-profiling

Additional URL

https://doi.org/10.1007/978-3-319-47880-7_6

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