The Challenges of Democratizing News and Information: Examining Data on Social Media, Viral Patterns and Digital Influence

The advent of social media and peer-to-peer technologies offers the possibility of driving the full democratization of news and information, undercutting the agenda-setting of large media outlets and their relative control of news and information flows. We are now about a decade into the era of the social Web. What do the data indicate about changing news flows and access/consumption patterns in the United States? Are we witnessing a paradigm shift yet, or are legacy patterns reasserting themselves? This paper brings together media industry data and perspective—from NPR, the Boston Globe and the Wall Street Journal—with a growing body of social science and computational research produced by universities and firms such as Microsoft Research and the Facebook data science team, as well as survey findings from the Pew Research Center. The bulk of the evidence so far complicates any easy narrative, and it very much remains an open question if we can expect a more radically democratized media ecosystem, despite promising early trends and anecdotes. As I review the evidence, I aim to highlight lessons and insights that can help those thinking about and operating in the social media space. This paper also aims to serve as an accessible survey of news media-related topics within social science and social network analysis scholarship

Informational Quality Labeling on Social Media: In Defense of a Social Epistemology Strategy

Social media platforms have been rapidly increasing the number of informational labels they are appending to user-generated content in order to indicate the disputed nature of messages or to provide context. The rise of this practice constitutes an important new chapter in social media governance, as companies are often choosing this new “middle way” between a laissez-faire approach and more drastic remedies such as removing or downranking content. Yet information labeling as a practice has, thus far, been mostly tactical, reactive, and without strategic underpinnings. In this paper, we argue against defining success as merely the curbing of misinformation spread. The key to thinking about labeling strategically is to consider it from an epistemic perspective and to take as a starting point the “social” dimension of online social networks. The strategy we articulate emphasizes how the moderation system needs to improve the epistemic position and relationships of platform users — i.e., their ability to make good judgements about the sources and quality of the information with which they interact on the platform — while also appropriately respecting sources, seekers, and subjects of information. A systematic and normatively grounded approach can improve content moderation efforts by providing clearer accounts of what the goals are, how success should be defined and measured, and where ethical considerations should be taken into consideration. We consider implications for the policies of social media companies, propose new potential metrics for success, and review research and innovation agendas in this regard

Beyond Fake News and Fact-Checking: A Special Issue to Understand the Political, Social and Technological Consequences of the Battle against Misinformation and Disinformation

Disinformation, hoaxes and false news are part of our daily lives and have numerous antecedents throughout history, and there have been many authors who have described the parallel between communication theories and propaganda theories (Barredo Ibáñez 2021) [...

<i>Beyond Fake News and Fact-Checking</i>: A Special Issue to Understand the Political, Social and Technological Consequences of the Battle against Misinformation and Disinformation

Disinformation, hoaxes and false news are part of our daily lives and have numerous antecedents throughout history, and there have been many authors who have described the parallel between communication theories and propaganda theories (Barredo Ibáñez 2021) [...

Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience

We present analyses from a real-time information monitoring system of online local news in the U.S. We study relationships among online local news coverage of COVID, cases and deaths in an area, and properties of local news outlets and their audiences. Our analysis relies on a unique dataset of the online content of over 300 local news outlets, encompassing over 750,000 articles over a period of 10 months spanning April 2020 to February 2021. We find that the rate of COVID coverage over time by local news outlets was primarily associated with death rates at the national level, but that this effect dissipated over the course of the pandemic as news about COVID was steadily displaced by sociopolitical events, like the 2020 U.S. elections. We also find that both the volume and content of COVID coverage differed depending on local politics, and outlet audience size, as well as evidence that more vulnerable populations received less pandemic-related news

NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-Level News Ecosystems

In this paper, we present a dataset of over 1.4M online news articles from 313 local U.S. news outlets published over 20 months (between April 4th, 2020 and December 31st, 2021). These outlets cover a geographically diverse set of communities across the United States. In order to estimate characteristics of the local audience, included with this news article data is a wide range of county-level metadata, including demographics, 2020 Presidential Election vote shares, and community resilience estimates from the U.S. Census Bureau. The NELA-Local dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GFE66K