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.

The literature of media effects is frequently characterized as a three-stage progression initially embracing a theory of strong effects followed by a repudiation of earlier work and new model of minimal effects followed by yet another repudiation and a rediscovery of strong effects. We argue that although this dramatic and somewhat romantic simplification may be pedagogically useful in introductory courses, it may prove a significant impediment to further theoretical refinement and progress in advanced scholarship. We analyze the citation patterns of 20,736 scholarly articles in five communication journals with special attention to the 200 most frequently cited papers in an effort to provide an alternative six-stage model of, we argue, cumulative media effects theories for the period 1956-2005. This is an article about the last 50 years of communication effects research. It aspires to develop two arguments. The first is that the evolving character of this research reveals an underlying structure moving from relatively simple models of persuasion and prospective attitude change to more sophisticated and layered models as scholars successively address the conditions and contexts of communication effects. The progression is cumulative, we argue, because once an effect of some sort has been identified, subsequent research can systematically address the conditions under which such an effect is diminished or strengthened. The second argument is that this underlying structure is routinely obscured and the advance of cumulative scientific refinement is potentially derailed by a widely held construction of this history known as the ''minimal-effects hypothesis.'' We can demonstrate empirically through citation analysis that the first argument is true, although the structure of citations is modest rather than dramatic. We are unable to prove that the second is true, although we can identify what we believe is ample anecdotal evidence. On the second argument, we would be pleased to have successfully raised the issue rather than conclusively won the point.We start with the notion of ''media effects.'' It represents one of the core ideas of communication research since its inception. Elihu Katz (2001b) characteristically

Demonstrations that analyses of social media content can align with measurement from sample surveys have raised the question of whether survey research can be supplemented or even replaced with less costly and burdensome data mining of already-existing or "found" social media content. But just how trustworthy such measurement can be-say, to replace official statistics-is unknown. Survey researchers and data scientists approach key questions from starting assumptions and analytic traditions that differ on, for example, the need for representative samples drawn from frames that fully cover the population. New conversations between these scholarly communities are needed to understand the potential points of alignment and non-alignment. Across these approaches, there are major differences in (a) how participants (survey respondents and social media posters) understand the activity they are engaged in; (b) the nature of the data produced by survey responses and social media posts, and the inferences that are legitimate given the data; and (c) practical and ethical considerations surrounding the use of the data. Estimates are likely to align to differing degrees depending on the research topic and the populations under consideration, the particular features of the surveys and social media sites involved, and the analytic techniques for extracting opinions and experiences from social media. Traditional population coverage may not be required for social media content to effectively predict social phenomena to the extent that social media content distills or summarizes broader conversations that are also measured by surveys.

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.