Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation

Social Media is used by many as a source of information for current world events, followed by publicly sharing their sentiment about these events. However, when the shared information is not trustworthy and receives a large number of interactions, it alters the public's perception of authentic and false information, particularly when the origin of these stories comes from malicious sources. Over the past decade, there has been an influx of users on the Twitter social network, many of them automated bot accounts with the objective of participating in misinformation campaigns that heavily influence user susceptibility to fake information. This can affect public opinion on real-life matters, as previously seen in the 2020 presidential elections and the current COVID-19 epidemic, both plagued with misinformation. In this paper, we propose an agent-based social simulation environment that utilizes the social network Twitter, with the objective of evaluating how the beliefs of agents representing regular Twitter users can be influenced by malicious users scattered throughout Twitter with the sole purpose of spreading misinformation. We applied two scenarios to compare how these regular agents behave in the Twitter network, with and without malicious agents, to study how much influence malicious agents have on the general susceptibility of the regular users. To achieve this, we implemented a belief value system to measure how impressionable an agent is when encountering misinformation and how its behavior gets affected. The results indicated similar outcomes in the two scenarios as the affected belief value changed for these regular agents, exhibiting belief in the misinformation. Although the change in belief value occurred slowly, it had a profound effect when the malicious agents were present, as many more regular agents started believing in misinformation.