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Researchers have used surveys and experiments to better understand communication dynamics, but confront consistent distortion from self‐report data. But now both digital exposure and resulting expressive behaviors (such as tweets) are potentially accessible for direct analysis with important ramifications for the formulation of communication theory. We utilize “big data” to explore attention and framing in the traditional and social media for 29 political issues during 2012. We find agenda setting for these issues is not a one‐way pattern from traditional media to a mass audience, but rather a complex and dynamic interaction. Although the attentional dynamics of traditional and social media are correlated, evidence suggests that the rhythms of attention in each respond to a significant degree to different drummers.

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Despite increasing warnings about inaccurate information online, little is known about how social media contribute to the widespread diffusion of unverified health information. This study addresses this issue by examining the vaccine-autism controversy. By looking into a large dataset of Twitter, Reddit posts, and online news over 20 months in the US, Canada, and the UK, our time-series analysis shows that Twitter drives news agendas, and Reddit follows news agendas regarding the vaccine-autism debate. Additionally, the results show that both Twitter and Reddit are more likely to discuss the vaccine-autism link compared to online news content.

Despite the existing evaluation of the sampling options for periodical media content, only a few empirical studies have examined whether probability sampling methods can be applicable to social media content other than simple random sampling. This article tests the efficiency of simple random sampling and constructed week sampling, by varying the sample size of Twitter content related to the 2014 South Carolina gubernatorial election. We examine how many weeks were needed to adequately represent 5 months of tweets. Our findings show that a simple random sampling is more efficient than a constructed week sampling in terms of obtaining a more efficient and representative sample of Twitter data. This study also suggests that it is necessary to produce a sufficient sample size when analyzing social media content.

This study examines the dynamics of the framing of mass shooting incidences in the U.S. occurring in the traditional commercial online news media and Twitter. We demonstrate that there is a dynamic, reciprocal relationship between the attention paid to different aspects of mass shootings in online news and in Twitter: tweets tend to be responsive to traditional media reporting, but traditional media framing of these incidents also seems to resonate from public framing in the Twitterverse. We also explore how different frames become prominent as they compete among media as time passes after shooting events. Finally, we find that key differences emerge between norms of journalistic routine and how users rely on Twitter to express their reactions to these tragic shooting incidents.