In a high-choice media environment, there are fears that individuals will select media and content that reinforce their existing beliefs and lead to segregation based on interest and/or partisanship. This could lead to partisan echo chambers among those who are politically interested and could contribute to a growing gap in knowledge between those who are politically interested and those who are not. However, the high-choice environment also allows individuals, including those who are politically interested, to consume a wide variety of media, which could lead them to more diverse content and perspectives. This study examines the relationship between political interest as well as media diversity and being caught in an echo chamber (measured by five different variables). Using a nationally representative survey of adult internet users in the United Kingdom (N = 2000), we find that those who are interested in politics and those with diverse media diets tend to avoid echo chambers. This work challenges the impact of echo chambers and tempers fears of partisan segregation since only a small segment of the population are likely to find themselves in an echo chamber. We argue that single media studies and studies which use narrow definitions and measurements of being in an echo chamber are flawed because they do not test the theory in the realistic context of a multiple media environment.

As trust in news media and social media dwindles and fears of disinformation and echo chambers spread, individuals need to find ways to access and assess reliable and trustworthy information. Despite low levels of trust in social media, they are used for accessing political information and news. In this study, we examine the information verification practices of opinion leaders (who consume political information above average and share their opinions on social media above average) and of opinion seekers (who seek out political information from friends and family) to understand similarities and differences in their news media trust, fact-checking behaviors, and likeliness of being caught in echo chambers. Based on a survey of French Internet users (N = 2,000) we find that not only opinion leaders, but also opinion seekers, have higher rates across all three of these dependent variables. We discuss the implications of findings for the development of opinion leadership theory as well as for social media platforms wishing to increase trust.

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data are being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N = 1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data, (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion, and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

The article examines whether and how the ever-evolving practice of using social media to screen job applicants may undermine people’s trust in the organizations that are engaging in this practice. Using a survey of 429 participants, we assess whether their comfort level with cybervetting can be explained by the factors outlined by Petronio’s communication privacy management theory: culture, gender, motivation, and risk-benefit ratio. We find that respondents from India are significantly more comfortable with social media screening than those living in the United States. We did not find any gender-based differences in individuals’ comfort with social media screening, which suggests that there may be some consistent set of norms, expectations, or “privacy rules” that apply in the context of employment seeking—irrespective of gender. As a theoretical contribution, we apply the communication privacy management theory to analyze information that is publicly available, which offers a unique extension of the theory that focuses on private information. Importantly, the research suggests that privacy boundaries are not only important when it comes to private information, but also with information that is publicly available on social media. The research identifies that just because social media data are public, does not mean people do not have context-specific and data-specific expectations of privacy.

Social media platforms make choices about what content is and is not permissible on their platforms. For example, choices about if and how to deal with online harassment and hate speech are growing problems in many online settings. But these choices are often opaque, can vary from platform to platform, and can change over time with little notice. This study examines the ways Facebook, Twitter, and Reddit have defined harassment and hate speech, as well as who they frame as responsible for dealing with harassment and hate speech over time. Using content analysis, the policy structures that house relevant policies, the policy documents themselves, and blog posts are examined. The results illustrate a phased approach to defining harassment and hate, which has become increasingly complex and nuanced over time. Additionally, this work shows a compounding view of who is responsible, which began with users but over time has come to include the platform itself, technology, and external actors such as civil society groups. This paper highlights continued opacity and increasing complexity while also providing contextual historical information necessary for both future research and platform governance decisions.

The rapid acceleration of artificial intelligence (AI) and, more recently, generative AI is reshaping journalism in ways that extend far beyond earlier forms of news automation. As generative AI tools become widely accessible and capable of processing unstructured data, longstanding definitions of automated journalism—once centered on structured datasets and template-based text generation—are being fundamentally reconfigured. This paper presents the most comprehensive and up-to-date systematized review of automated journalism scholarship, expanding on earlier research by synthesizing 185 peer-reviewed, English studies published between 2012 and 2024 about machine-generated textual news content published online. Through a rigorously designed search strategy across four major social science databases, this review maps how the field’s conceptual, methodological, and geographical contours have transformed as AI and generative AI become increasingly ubiquitous. The findings show a surge of research in 2024 alone, as well as the emergence of more than 150 overlapping terms used to describe AI- and algorithmically generated news, illustrating significant conceptual fragmentation. Despite no overly dominant theories, concepts or frameworks, key themes include credibility and trust, human–machine collaboration, newsroom adoption and institutional logics, transparency and disclosure, and the ethical and regulatory challenges introduced by increasingly sophisticated AI systems. By consolidating patterns, evaluating an expanded selection of key terms, and assessing theoretical and conceptual frameworks, this review demonstrates how AI and especially generative AI reflect the speed of industrial change, but also the lack of shared academic frameworks to make sense of that change.