Bots are less central than verified accounts during contentious political events

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 11; pp. 1 - 8
• Main Authors: González-Bailón, Sandra, De Domenico, Manlio
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 16.03.2021
• Subjects: Digital media; Human influences; Information flow; Learning algorithms; Machine learning; Messages; Natural language processing; News; Physical Sciences; Politics; Social networks; Social Sciences; Software agents; Visibility; political mobilization; online networks; computational social science; information diffusion; social media
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2013443118

Information manipulation is widespread in today’s media environment. Online networks have disrupted the gatekeeping role of traditional media by allowing various actors to influence the public agenda; they have also allowed automated accounts (or bots) to blend with human activity in the flow of information. Here, we assess the impact that bots had on the dissemination of content during two contentious political events that evolved in real time on social media. We focus on events of heightened political tension because they are particularly susceptible to information campaigns designed to mislead or exacerbate conflict. We compare the visibility of bots with human accounts, verified accounts, and mainstream news outlets. Our analyses combine millions of posts from a popular microblogging platform with web-tracking data collected from two different countries and timeframes. We employ tools from network science, natural language processing, and machine learning to analyze the diffusion structure, the content of the messages diffused, and the actors behind those messages as the political events unfolded. We show that verified accounts are significantly more visible than unverified bots in the coverage of the events but also that bots attract more attention than human accounts. Our findings highlight that social media and the web are very different news ecosystems in terms of prevalent news sources and that both humans and bots contribute to generate discrepancy in news visibility with their activity.