Reverse Engineering Socialbot Infiltration Strategies in Twitter
Carlos A. Freitas, Fabr\'icio Benevenuto, Saptarshi Ghosh, Adriano, Veloso

TL;DR
This paper investigates how socialbots infiltrate Twitter by creating 120 varied accounts, revealing that even simple bots can successfully infiltrate, and provides insights for designing detection and countermeasures.
Contribution
It systematically analyzes socialbot infiltration strategies on Twitter and quantifies their effectiveness using factorial design, aiding in developing detection methods.
Findings
Simple automated socialbots can infiltrate Twitter successfully.
Different bot strategies have quantifiable infiltration effectiveness.
Insights for designing detection and countermeasure approaches.
Abstract
Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots automated accounts that mimic real users seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics…
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Taxonomy
TopicsSpam and Phishing Detection · Misinformation and Its Impacts · Internet Traffic Analysis and Secure E-voting
