Arming the public with artificial intelligence to counter social bots

Abstract

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 artificial intelligence (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.

Unveiling Coordinated Groups Behind White Helmets Disinformation

“…To demonstrate the strategies and behaviors of suspicious accounts, a few coordinated groups are highlighted in Fig. 4: (2) Few hyperactive accounts with similar tweets, likely to be automated according to Botometer [23]. (3) Factory of news websites, with multiple accounts pretending to post news from distinct websites that actually present the same content.…”

“…e automation rules and the coordinated behavior of this type of group are more trivial. us, current bot detection tools, such as Botometer from Indiana University [23], are able to capture the suspicious behaviors at the individual level. (3) Factory of news websites-several accounts sharing a headline and a shortened URL by social media automation tools (o en dlvr.it).…”

Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models

“…We choose to analyze ActiveRT where the user information of individual events is available. We use the Botometer API [37] Fig. (c) shows diffusion final popularity prediction performance on NEWS.…”

Where Are We? Using Scopus to Map the Literature at the Intersection Between Artificial Intelligence and Crime

“…With different level of sophistication and performance, studies have also addressed crime-related problems [49,50,51,52]. Finally, other relevant applications have focused on agricultural issues [53,54], health care [55,56,57], traffic prediction and transport optimization [58,59] and individual and collective behaviors on social media [60,61,62]. Furthermore, as AI systems are increasingly deployed in everyday lives, they have triggered conceptual, moral and philosophical debates on ethics, fairness, and accountability, reinforcing discussions on the use of these technological and scientific advances for ensuring social good.…”