Sexually oriented digital media use is important in adolescents’ sexuality development. The literature points to affordances of media uses (e.g., accessibility), inviting adolescents to use media for sexuality construction and engagement in sexual behaviors. Different theories on sexually oriented digital media use (e.g., Uses and Gratifications and Self-effects) explain why adolescents use media and how it shapes adolescents’ sexuality. Research has documented motivations of sexually oriented digital media use (e.g., sexual exploration) and how they relate to sexual self-development outcomes (e.g., sexual certainty), attitudinal outcomes (e.g., gender stereotypical beliefs), relationship quality indicators (e.g., commitment), and sexual behavioral outcomes (e.g., risky sexual behavior). The literature suggests challenges and future directions of sexually oriented digital media use research. Future research should explore the beneficial implications of digital media uses for adolescents’ sexuality. More attention should cover processes explaining the link between adolescents’ digital media uses and sexuality-related outcomes, and their bidirectional nature.

Today’s information environment is drastically different from the heyday of print and broadcast, but these changes exceed the scope of researchers’ agendas. More is known now than when these technologies were in their infancy, yet efforts to understand the implications of changing communication technology for media effects have produced mixed findings, limiting progress towards cohesive and generalisable theoretical explanations. A literature review suggests one reason for this is that media effects scholarship has often neglected insights from political psychology and information processing, contributing to a lack of theoretical coherence across these bodies of work. Though research thoroughly examines directional motivations dictating media choice and exposure, it does not equally consider other cognitive biases driving choice, exposure, and processing, which can offset effects from the structural aspects of digital media. Given ample evidence that communication technology influences information processing, any viable, contemporary explanation of media effects must reconcile with these literatures.

This chapter offers our first empirical analyses of media coverage of policy, across the various policy domains and news organizations. We first compare the aggregated “media signals” to actual changes in policy. Does aggregated coverage follow policy over time? Does this relationship vary across domains? Given the multiple measures developed in the previous chapter, this chapter also considers whether and how the measures matter for what we observe. This chapter centers on figures depicting the ebb and flow of policy and media coverage over time. In so doing, it offers the first large-scale comparison of policy change, and media coverage of policy change, across six domains over a forty-year period. Do patterns vary across newspapers? How about across media, particularly television coverage? Does it match what we see in newspapers? This chapter offers some critical diagnostics, assessing the degree to which media coverage has followed public policy; and relatedly, whether media coverage reliably includes the information citizens need to respond to policy change.

This chapter spells out how we believe the mass media cover public policy, particularly the outputs government produces. Although there is a considerable body of work detailing a range of biases in coverage and a lack of policy content, we posit that mass media can and do track trends in policy, at least in very salient policy areas that attract a lot of attention. Put differently, even as media can be biased and provide inaccurate information, there also can be a signal of important policy actions amidst the noise. News organizations have a professional and economic interest in doing so, at least up to a point. We are especially interested in media coverage of policy change. This is in part because we suppose that media often reports on change in policy, not levels, much as research on news coverage of other areas, for example, economic conditions, has revealed. (Change also seems easier to directly measure.) The conceptualization and theory in this chapter guide both the measurement and analyses that follow.

Chapter 3 laid out the building blocks for our measures of the media policy signal and presented a preliminary version of that signal across newspapers, television, and social media content. We now turn to a series of refinements and robustness tests, critical checks on the accuracy of our media policy signal measures. We begin with some comparisons between crowdsourced codes and those produced by trained student coders. Assessing the accuracy of crowdsourced data is important for the dictionary-based measures in the preceding chapter and for the comparisons with machine-learning-based measures introduced in this chapter. We then turn to crowdsourced content analyses of the degree to which extracted content reflects past, present, or future changes in spending. Our measures likely reflect some combination of these spending changes, and understanding the balance of each will be important for analyses in subsequent chapters. Finally, we present comparisons of dictionary-based measures and those based on machine-learning, using nearly 30,000 human-coded sentences and random forest models to replicate that coding across our entire corpus.

Does media coverage matter for the functioning of representative democracy? Do people notice news coverage? Do they take it into account? In particular, do citizens use the information that media content conveys to update their policy preferences? These questions are the central motivation for this book. In this chapter we try to provide some answers. We begin by introducing our principal measures of public preferences from the General Social Survey. We then consider a smaller, unique body of data on public perceptions of policy change, from the American National Election Studies. These data allow us some preliminary insight into whether the public notices government spending and media coverage of government spending. The remainder of the chapter then presents results of analyses of public preferences, first to establish the effects of spending on preferences, and then to assess the role of the media signal. Results document thermostatic public responsiveness, as found in previous research, and also that news coverage is a critical mediating force.

Preceding chapters have provided evidence that media coverage frequently reflects public policy, and that public preferences respond to a combination of policy and the media “policy signal.” Those results speak to some important questions about the nature and functioning of representative democracy, we believe. A good number of questions nevertheless remain. This chapter attempts to address some of what seem to us to be the most pressing issues. First, we consider the impact that trends in media consumption have on public responsiveness. Second, we consider heterogeneity in public responsiveness to the media policy signal. Third, we reconsider the causal relationships between policy, news coverage, and the public. Fourth and finally, we investigate several of the domain-specific media effects identified in Chapter 6. Media coverage of policy matters, but to varying degrees and in different ways. We offer additional analyses here to help illuminate some of these domain-level differences in information flows.

This chapter provides an introduction to the ideas and literatures that guide the analyses that follow. We consider past work on the potential role of media coverage in representative democracy and public responsiveness.

This chapter moves from theory to practice and implements a measure of media coverage. We introduce our database of news coverage. We also described the unique “layered dictionary” approach used to identify sentences on the direction of policy change. The focus on change in policy and not levels is critical, and we discuss this in some detail. We also compare the use of application of both dictionary and supervised machine-learning approaches to content analyses of news content. This chapter is necessarily technical, but it also is an opportunity for us to introduce the methods to a broader audience. We plan to escort readers through the various available approaches, our implementation of them, and then an assessment of the outputs they produce. We end the chapter with some substantive findings: the overall amount of coverage of policy change in newspapers and television, and the general trends in aggregated “media signals” generated by the different approaches.

This chapter reviews the findings in previous chapter and considers their implications for research on media democracy, as well as for citizens and journalists.

Explores the emerging subdiscipline of Peace Communication (PeaceComm), beginning with a discussion about the history of the practice, and the author’s ongoing quest to introduce a subdiscipline, dedicated to assessing and evaluating the critical efficacy of the practice. A methodological template for comparative global assessment and evaluation is offered, stressing the need to prioritize political conflict data and conflict zones-based context analyses, given that political conflict is caused by collective grievances related to “group”-level disadvantages and perceived disadvantages, not individual prejudice. The template is operationalized through the assessment of Sesame Street interventions into the Israeli Palestinian ethnopolitical nationalist conflict, drawn from field work in 2001, 2004-2006, and 2011. Best practices and other interdisciplinary contributions for practitioners are recommended, to understand conflict intractability where socialization, culture, and inter-“group” (mediated and interpersonal) communication intersect in glocalized conflict zone contexts, and in the Israeli-Palestinian conflict, specifically. The interventions targeted children, who comprise the majority within conflict zones. The model used, mediated contact effects, is one of seven models and six subtypes of PeaceComm practiced historically worldwide the author has previously categorized, and is one of those most in need of PeaceComm scholarship, with potential to succeed but scarce evidence collected about its efficacy.