During the COVID-19 pandemic, the proliferation of misinformation on social media has been rapidly increasing. Automated Bot authors are believed to be significant contributors of this surge. It is hypothesized that Bot authors deliberately craft online misinformation aimed at triggering and exploiting human cognitive biases, thereby enhancing tweet engagement and persuasive influence. This study investigates this hypothesis by studying triggers of biases embedded in Bot-authored misinformation and comparing them with their counterparts, Human-authored misinformation. We complied a Misinfo Dataset that contains COVID-19 vaccine-related misinformation tweets annotated by author identities, Bots vs Humans, from Twitter during the vaccination period from July 2020 to July 2021. We developed an algorithm to computationally automate the extraction of triggers for eight cognitive biase. Our analysis revealed that the Availability Bias, Cognitive Dissonance, and Confirmation Bias were most commonly present in misinformation, with Bot-authored tweets exhibiting a greater prevalence, with distinct patterns in utilizing bias triggers between Humans and Bots. We further linked these bias triggers with engagement metrics, inferring their potential influence on tweet engagement and persuasiveness. Overall, our findings indicate that bias-triggering tactics have been more influential on Bot-authored tweets than Human-authored tweets. While certain bias triggers boosted engagement for Bot-authored tweets, some other bias triggers unexpectedly decreased it. Conversely, triggers of most biases appeared to be unrelated to the engagement of Human-authored tweets. Our work sheds light on the differential utilization and effect of persuasion strategies between Bot-authored and Human-authored misinformation from the lens of human biases, offering insights for the development of effective counter-measures. 

pdf/exploring-cognitive-bias-triggers-in-covid-19-misinformation-4oud4a4acu.pdf

Exploring Cognitive Bias Triggers in COVID-19 Misinformation Tweets: A
  Bot vs. Human Perspective

Social media is used by billions of people and organisations worldwide for entertainment, advertising, news and many other reasons It is also often used for gauging public opinion and spreading of information This information can directly influence public opinion Unfortunately, the potential for automatically influencing public opinion by potentially nefarious organisations and indeed countries through flooding of posts and tweets is increasingly common and of concern Bots are often used to automate the sending of this information and potential misinformation In this paper we explore approaches for bot identification in an automated manner using extensive Twitter collections related to the city of Melbourne Despite the work of social media platforms such as Twitter in cleaning up the use of their platform and minimizing the widespread use/misuse of bots, we identify that a very small percentage of bots may still be active and must be addressed

A Case Study in Twitter Bot Identification: Are They Still a Problem?

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A Case Study in Twitter Bot Identification: Are They Still a Problem?