Analyzing Social Big Data to Study Online Discourse and Its Manipulation

Abstract

The widespread use of social media helps people connect and share their opinions and experiences with millions of others, while simultaneously bringing new threats. This dissertation aims to provide insights into analysis of online conversations and mechanisms that might be used for their manipulation. The first part delves into the effect of geography on information dissemination and user roles in online discourse. I study trending topics on Twitter to highlight mechanisms governing the diffusion of local and national trends. My analysis points to three locally geographic regions and one cluster that contains trendsetting cities coinciding with major travel hubs. When factors limiting information spread are considered, censorship mechanisms mandated by governments are found to be ineffective and even show a correlation with increasing popularity. I also present an analysis of spatiotemporal characteristics and distinct user roles in the Gezi movement. Next, I discuss different forms of social media manipulation. Malicious entities can employ promotion campaigns and social bots. We build machine learning frameworks that exploit features extracted from network, content, and users to train accurate supervised learning models. Our system for early detection of promoted social media trends harnesses multidimensional time series signals to reveal subtle differences between promoted and organic trends. In my research on social bots, I carried out the largest study of the human-bot ecosystem to date. Our estimates suggest that between 9 and 15% of active Twitter accounts are bots. I present distinct behavioral groups and interaction strategies among human and bot accounts. This body of work contributes to a more comprehensive understanding of online user behavior and to the development of systems to detect online abuse.