Arming the public with artificial intelligence to counter social bots

By Kai-Cheng Yang, Onur Varol, Clayton A. Davis, Alessandro Flammini, Filippo Menczer. 2019

412 citations recorded by the source corpus.

The increased relevance of social media in our daily life has been accompanied by efforts to manipulate online conversations and opinions. Deceptive social bots -- automated or semi-automated accounts designed to impersonate humans -- have been successfully exploited for these kinds of abuse. Researchers have responded by developing AI tools to arm the public in the fight against social bots. Here we review the literature on different types of bots, their impact, and detection methods. We use the case study of Botometer, a popular bot detection tool developed at Indiana University, to illustrate how people interact with AI countermeasures. A user experience survey suggests that bot detection has become an integral part of the social media experience for many users. However, barriers in interpreting the output of AI tools can lead to fundamental misunderstandings. The arms race between machine learning methods to develop sophisticated bots and effective countermeasures makes it necessary to update the training data and features of detection tools. We again use the Botometer case to illustrate both algorithmic and interpretability improvements of bot scores, designed to meet user expectations. We conclude by discussing how future AI developments may affect the fight between malicious bots and the public.

Subfields: Information Systems, Sociology and Political Science, Safety, Robustness & Interpretability, Signal Processing

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Citations in the corpus

Release Strategies and the Social Impacts of Language Models (2019, background): “[67] also supports the view that metadata is useful in identifying social bots online, as they use features such as time zone, device information, and content deletion patterns.”
Scalable and Generalizable Social Bot Detection through Data Selection (2019, methodology): “Such coordination cannot be captured by our feature-based approach (Cresci et al. 2017; Yang et al. 2019).”
Content Moderation As a Political Issue: The Twitter Discourse Around Trump's Ban (2022, methodology): “To address the concern that the Twitter discourse may be driven by bots, we classified a random sample of 100,000 user profiles in our dataset using the Bot-O-Meter API v414 (Yang et al., 2019).”
Finding and Characterizing Information Warfare Campaigns (2020, neutral): “Several authors have discussed this phenomena [260].”
A decade of social bot detection (2020, background): “By leveraging more than 1,200 features of an account, Botometer evaluates possible bots based on their profile characteristics, social network structure, the content they produce, their sentiment expressions, and the timings of their actions [35].”