Chatter on social media is 20% bots and 80% humans. Chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated while humans use cues that require dialogue understanding. Bots use words that match the identities they choose to present, while humans may send messages that are not related to the identities they present. Bots and humans differ in their communication structure: sampled bots have a star interaction structure, while sampled humans have a hierarchical structure. These conclusions are based on a large-scale analysis of social media tweets across ~200mil users across 7 events. Social media bots took the world by storm when social-cybersecurity researchers realized that social media users not only consisted of humans but also of artificial agents called bots. These bots wreck havoc online by spreading disinformation and manipulating narratives. Most research on bots are based on special-purposed definitions, mostly predicated on the event studied. This article first begins by asking, "What is a bot?", and we study the underlying principles of how bots are different from humans. We develop a first-principle definition of a social media bot. With this definition as a premise, we systematically compare characteristics between bots and humans across global events, and reflect on how the software-programmed bot is an Artificial Intelligent algorithm, and its potential for evolution as technology advances. Based on our results, we provide recommendations for the use and regulation of bots. Finally, we discuss open challenges and future directions: Detect, to systematically identify these automated and potentially evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; Disrupt, to moderate the impact of malicious bots.

The 2022 Russian invasion of Ukraine was accompanied by a large-scale, pro-Russian propaganda campaign on social media. However, the strategy behind the dissemination of propaganda has remained unclear, particularly how the online discourse was strategically shaped by the propagandists' community. Here, we analyze the strategy of the Twitter community using an inverse reinforcement learning (IRL) approach. Specifically, IRL allows us to model online behavior as a Markov decision process, where the goal is to infer the underlying reward structure that guides propagandists when interacting with users with a supporting or opposing stance toward the invasion. Thereby, we aim to understand empirically whether and how between-user interactions are strategically used to promote the proliferation of Russian propaganda. For this, we leverage a large-scale dataset with 349,455 posts with pro-Russian propaganda from 132,131 users. We show that bots and humans follow a different strategy: bots respond predominantly to pro-invasion messages, suggesting that they seek to drive virality; while messages indicating opposition primarily elicit responses from humans, suggesting that they tend to engage in critical discussions. To the best of our knowledge, this is the first study analyzing the strategy behind propaganda from the 2022 Russian invasion of Ukraine through the lens of IRL.

Bots are social media accounts which are controlled by artificial software rather than by humans and serve a variety of purposes from news aggregation to automated customer assistance for online retailers. However, bots have recently been under the spotlight as they are regularly employed as part of large-scale efforts on social media to manipulate public opinion, such as during electoral campaigns. A new study in Frontiers in Physics has revealed the presence of short-term behavioral trends in humans that are absent in social media bots, providing an example of a'human signature' on social media which could be leveraged to develop more sophisticated bot detection strategies. The research is the first study of its kind to apply user behavior over a social media session to the problem of bot detection. "Remarkably, bots continuously improve to mimic more and more of the behavior humans typically exhibit on social media. Every time we identify a characteristic we think is prerogative of human behavior, such as sentiment of topics of interest, we soon discover that newly-developed open-source bots can now capture those aspects," says co-author Emilio Ferrara, Assistant Professor of Computer Science and Research Team Leader at the University of Southern California Information Sciences Institute.

Artificial intelligence is being used to spot the difference between human users and fake accounts on Twitter. Emilio Ferrara at the University of Southern California in the US, and his colleagues have trained an AI to detect bots on Twitter based on differences in patterns of activity between real and fake accounts. The team analysed two separate datasets of Twitter users, which had been classified either manually or by a pre-existing algorithm as either bot or human. The manually verified dataset consisted of 8.4 million tweets from 3500 human accounts, and 3.4 million tweets from 5000 bots. The researchers found that human users replied four to five times more often to other tweets than bots did.