Social media platforms have become essential for interpersonal communication, accessing news and other information. There are growing concerns about the presence of social bots that mimic human behaviour while pushing their own agenda distorting online communities and the news environment. Previous research has demonstrated social networks and social media platforms play a key role in the emergence of polarisation. The selective exposure of filter bubbles limits the information users get exposed to and promotes certain content. Echo chambers, or highly homophilous communities, can also limit the exposure to information and people that disagree with the majority view. Social bots are an additional layer of complexity in this process. To better understand their behaviour and consequences for data quality this paper proposes an agent-based simulation model (ABM) to explore how social bots distort the opinion dynamics on social media platforms. It uses survey data on political attitudes and media trust in Germany in combination with web scrapping data on internet use to calibrate some of the model parameters. Simulation conditions separately manipulated (1) the number of social bots, (2) their information strategies, (3) the homophily or group similarity in personal networks and (4) the level of selective exposure through filter bubbles on social media. We are currently implementing the social bots on the model and designing their behaviours. We will compare the synthetic and empirical opinion data to determine the effects social bots had in terms of both, data quality and opinion dynamics. We will present our findings at the conference.