Digital Democracy: Accelerating a New Field of Knowledge

Knight Foundation's historic $50 million investments in 2019, aimed at accelerating a new field of research at the intersection of technology and democracy, have shown encouraging early signs. This report examines progress among the major research institutions that were funded, considers how the grantees responded under challenging conditions through mid-2020, and details concerns and emerging issues that might be addressed in the coming years. It also details the activities of the Knight Research Network grantees in their initial stages, with primary emphasis on the 11 institutions that were funded in July 2019, although it also makes reference to others that have subsequently been added to the KRN

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

Digital scholarship allows the media to magnify the power and reach of academic research but the partnership between academics and journalists must be developed

Bridging the gap between academia and the media is not a simple task but it is essential if academics are to impact and improve society and inform its citizens. John Wihbey looks at how the Internet is bringing academics and journalists closer together and argues that there is still work to be done on both sides

Homicides in Boston (1963-2016)

This dataset contains the structure and organization of the Homicides in Boston database, which draws off of Boston Police Department records to document those homicides that occurred in Boston from 1963 to September, 17 2016. The database was constructed by John Wihbey (Northeastern University School of Journalism) through a series of freedom of information requests

Predicting News Coverage of Scientific Articles

Journalists act as gatekeepers to the scientific world, controlling what information reaches the public eye and how it is presented. Analyzing the kinds of research that typically receive more media attention is vital to understanding issues such as the “science of science communication” (National Academies of Sciences, Engineering, and Medicine 2017), patterns of misinformation, and the “cycle of hype.” We track the coverage of 91,997 scientific articles published in 2016 across various disciplines, publishers, and news outlets using metadata and text data from a leading tracker of scientific coverage in social and traditional media, Altmetric. We approach the problem as one of ranking each day’s, or week’s, papers by their likely level of media attention, using the learning-to-rank model lambdaMART (Burges 2010). We find that ngram features from the title, abstract and press release significantly improve performance over the metadata features journal, publisher, and subjects

Source Attribution: Recovering the Press Releases Behind Health Science News

We explore the task of intrinsic source attribution: inferring which portions of a derived document were adapted from an unobserved source document. Specifically, we model the relationship between news articles and their press release sources using a dataset of 64,784 health science news articles and 23,068 press releases. We approach the problem at the sentence level and work with science journalism professors to develop a four point Likert scale describing the extent to which a news article sentence is derived from the content in the corresponding press release. Because manual annotation of news article - press release pairs is time-consuming, we turn to a mix of expert, non-expert, and heuristic-based annotation to label our dataset. After a small pilot study, which found that humans, when only able to view the text of the news article, struggle to identify which content is derived or not, we compare four different sentence regression models on the task. We find that modeling a sentence's context in the entire document is important, with the best performing model, a sequence regression model with BERT token representations, achieving a spearman's ρ of 0.49 and NDCG@1 of 0.60 on the expert-labeled test set. Examining the model's predictions, we find that it successfully identifies copied or closely paraphrased sentences in articles with a mix of derived and original content, but struggles to differentiate between loosely paraphrased and original sentences in articles with mostly original writing