Publication Type

Conference Proceeding Article

Publication Date

10-2016

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

Social Media

Research Areas

Information Systems and Management

Publication

Proceedings of 8th International Conference on Social Informatics: SocInfo 2016, Bellevue, United States, 2016 November 11-14

Volume

10046

Identifier

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

Publisher

Springer Verlag

City or Country

Bellevue, United States

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

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

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