Online Human-Bot Interactions: Detection, Estimation, and Characterization

Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM 2017), pp. 280-289. 2017

10 Pages Posted: 1 Feb 2019

See all articles by Onur Varol

Onur Varol

Northeastern University

Emilio Ferrara

University of Southern California - Information Sciences Institute

Clayton B. Davis

Indiana University Bloomington, School of Informatics and Computing, Students

Filippo Menczer

Indiana University Bloomington

Alessandro Flammini

Indiana University Bloomington

Date Written: March 27, 2017

Abstract

Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9% and 15% of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.

Suggested Citation

Varol, Onur and Ferrara, Emilio and Davis, Clayton B. and Menczer, Filippo and Flammini, Alessandro, Online Human-Bot Interactions: Detection, Estimation, and Characterization (March 27, 2017). Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM 2017), pp. 280-289. 2017. Available at SSRN: https://ssrn.com/abstract=3324593

Onur Varol

Northeastern University ( email )

Northeastern University
901 E. 10th Street
Boston, MA 02115
United States

HOME PAGE: http://www.onurvarol.com

Emilio Ferrara (Contact Author)

University of Southern California - Information Sciences Institute ( email )

United States

HOME PAGE: http://emilio.ferrara.name

Clayton B. Davis

Indiana University Bloomington, School of Informatics and Computing, Students ( email )

Bloomington, IN
United States

Filippo Menczer

Indiana University Bloomington ( email )

Dept of Biology
100 South Indiana Ave.
Bloomington, IN 47405
United States

Alessandro Flammini

Indiana University Bloomington ( email )

Dept of Biology
100 South Indiana Ave.
Bloomington, IN 47405
United States

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