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Healthy news consumption requires limited exposure to unreliable content and ideological diversity in the sources consumed. There are two challenges to this normative expectation: the prevalence of unreliable content online; and the prominence of misinformation within individual news diets. Here, we assess these challenges using an observational panel tracking the browsing behavior of N ≈ 140,000 individuals in the United States for 12 months (January–December 2018). Our results show that panelists who are exposed to misinformation consume more reliable news and from a more ideologically diverse range of sources. In other words, exposure to unreliable content is higher among the better informed. This association persists after we control for partisan leaning and consider inter- and intra-person variation. These findings highlight the tension between the positive and negative consequences of increased exposure to news content online. 

We analyze social media activity during one of the largest protest mobilizations in US history to examine ideological asymmetries in the posting of news content. Using an unprecedented combination of four datasets (tracking offline protests, social media activity, web browsing, and the reliability of news sources), we show that there is no evidence of unreliable sources having any prominent visibility during the protest period, but we do identify asymmetries in the ideological slant of the sources shared on social media, with a clear bias towards right-leaning domains. These results support the “amplification of the right” thesis, which points to the structural conditions (social and technological) that lead to higher visibility of content with a partisan bent towards the right. Our findings provide evidence that right-leaning sources gain more visibility on social media and reveal that ideological asymmetries manifest themselves even in the context of movements with progressive goals.

 

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. 

The abundance of media options is a central feature of today’s information environment. Many accounts, often based on analysis of desktop-only news use, suggest that this increased choice leads to audience fragmentation, ideological segregation, and echo chambers with no cross-cutting exposure. Contrary to many of those claims, this paper uses observational multiplatform data capturing both desktop and mobile use to demonstrate that coexposure to diverse news is on the rise, and that ideological self-selection does not explain most of that coexposure. We show that mainstream media outlets offer the common ground where ideologically diverse audiences converge online, though our analysis also reveals that more than half of the US online population consumes no online news, underlining the risk of increased information inequality driven by self-selection along lines of interest. For this study, we use an unprecedented combination of observed data from the United States comprising a 5-y time window and involving tens of thousands of panelists. Our dataset traces news consumption across different devices and unveils important differences in news diets when multiplatform or desktop-only access is used. We discuss the implications of our findings for how we think about the current communication environment, exposure to news, and ongoing attempts to limit the effects of misinformation.