During times of hot crises, traditional news organizations have historically contributed to public fear and panic by emphasizing risks and uncertainties. The degree to which digital and social media platforms contribute to this panic is essential to consider in the new media landscape. This research examines news coverage of the 2014 Ebola crisis, exploring differences in presentation between newspaper coverage and news shared on the social news platform Reddit. Results suggest that news shared on Reddit amplified panic and uncertainty surrounding Ebola, while traditional newspaper coverage was significantly less likely to produce panic-inducing coverage.

Is it possible to identify opinion leaders in a semi-anonymous online network? To answer this question, this study examines the social news site Reddit to determine whether opinion leadership can be recognized in an online network that, at face value, does not allow users to associate with their off-line personas. Identifiable characteristics, such as commenting, longevity, karma scores, posting frequency, and posting scores were analyzed. Results indicate that semi-anonymous opinion leadership may exist, as several users appear frequently as top-voted posters and commenters. In addition, Reddit’s reputational value karma may help users and researchers identify opinion leaders.

Twitter gained new levels of political prominence with Donald J. Trump’s use of the platform. Although previous work has been done studying the content of Trump’s tweets, there remains a dearth of research exploring who opinion leaders were in the early days of his presidency and what they were tweeting about. Therefore, this study retroactively investigates opinion leaders on Twitter during Trump’s 1st month in office and explores what those influencers tweeted about. We uniquely used a historical data set of 3 million tweets that contained the word “trump” and used Latent Dirichlet Allocation, a probabilistic algorithmic model, to extract topics from both general Twitter users and opinion leaders. Opinion leaders were identified by measuring eigenvector centrality and removing users with fewer than 10,000 followers. The top 1% users with the highest score in eigencentrality ( N = 303) were sampled, and their attributes were manually coded. We found that most Twitter-based opinion leaders are either media outlets/journalists with a left-center bias or social bots. Immigration was found to be a key topic during our study period. Our empirical evidence underscores the influence of bots on social media even after the 2016 U.S. presidential election, providing further context to ongoing revelations and disclosures about influence operations during that election. Furthermore, our results provide evidence of the continued relevance of established, “traditional” media sources on Twitter as opinion leaders.

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This study presents a comparison of themes found in the U.S. and South Korean news coverage of the Paralympics. It is commonplace to associate rugged individualism with the former country and collectivism with the latter, and we assumed that these national traits would be apparent in the news coverage of the Paralympic Games in the two countries. Using the keyword “Paralympics,” we searched two decades of newspaper coverage and performed latent Dirichlet allocation (LDA) topic modeling on the articles that the search produced. The results of the modeling confirmed that the reporting in each country reflected its national characteristics, with the U.S. coverage reinforcing ableism and the South Korean coverage focusing on the government’s efforts to promote large-scale sporting events. The news stories in both countries were alike, though, in treating disabled athletes and traditional athletes differently. Part of the contribution of this research to the academic study of disabled athletes is the use of computational textual analysis to address these issues.