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Chatter on social media about global events comes from 20% bots and 80% humans. The chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated (e.g., increased hashtags, and positive terms) while humans use cues that require dialogue understanding (e.g. replying to post threads). Bots use words in categories that match the identities they choose to present, while humans may send messages that are not obviously 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 ~ 200 million 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. However, most research on bots are based on special-purposed definitions, mostly predicated on the event studied. In this article, we first begin 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. This definition refines existing academic and industry definitions: "A Social Media Bot is An automated account that carries out a series of mechanics on social media platforms, for content creation, distribution and collection, and/or for relationship formation and dissolutions." With this definition as a premise, we systematically compare the 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 of bots and for the regulation of bots. Finally, we discuss three open challenges and future directions of the study of bots: 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, while not unsettling human conversations.
Ng et al. (Mon,) studied this question.