James Shanahan

Journalists’ use of personal social media accounts and websites to disseminate content has necessitated investigating their significance for Kenyan media professionals. Based on a qualitative research design, this study draws on semi-structured in-depth interviews with Kenyan journalists (N = 22) to investigate their perceptions of migrating from traditional media outlets to digital platforms, in particular, personal social media channels and independent websites, the underlying reasons for this platform migration, and their perceptions of how legacy media are responding to such shifts. The findings highlight that economic incentives and challenging media environments are the primary drivers of platform migration and that participants perceive journalists’ independent websites (i.e., j-blogs) as valuable additions to the diversification of news sources. According to respondents, legacy media organizations have responded by restructuring their newsrooms, adopting convergence strategies, and establishing on-demand platforms to slow journalists’ migration to digital media. The implications for journalists, legacy and digital media, and media consumers are also discussed.

Many factors contributed to support for Donald Trump in the 2016 US presidential election, among them media influences. Morgan and Shanahan (2017) found that television viewing was associated with support for Trump, mediated through authoritarianism. In light of the changes in the political and media environments during Trump’s presidency, our study examined whether Morgan and Shanahan’s (2017) findings still held in the 2020 US presidential election. Replicating their findings, we found that authoritarianism still mediates the relationship between television viewing and Trump support. As in the original study, the indirect effect is moderated by political ideology and gender, with stronger indirect effects among liberals and females.

Automated accounts on social media that impersonate real users, often called "social bots," have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.

A viewer happening now across a rerun of the original Hawaii Five-O, the very successful American police procedural that ran from 1968-1980 on CBS, would be tempted to see it as an archaic example ...

In the last decades, there have been substantial changes in public attitudes toward gender roles and in television’s landscape and messages. Our meta-analysis of nearly 50 years of studies of television’s contribution to gender role attitudes is based on 485 effect sizes from 69 independent samples (N = 57,542) and reveals an overall effect size of .102. While we found no evidence of any decline of this association over time, it is significantly weaker for gender role attitudes related to the public sphere. Our findings imply that television viewing may strengthen an “egalitarian essentialism ideology,” that is, a discrepancy between the endorsement of gender equity in the public sphere/workplace and the persistence of traditional views regarding the private/domestic sphere.

This study is a meta-analysis of cultivation research from the 1970s to the present, based on three-level analyses of 3842 effect sizes from 406 independent samples. We found an overall effect size of .107 that varies only within a fairly narrow range across many potential moderators. Cultivation effects remain stable over the decades, implying an enduring relationship between television’s message system and viewers’ conceptions of social reality, despite immense changes in the institutional structure and technology of television. That bolsters a “traditional” perspective on cultivation, which is further corroborated by a stronger positive effect of overall viewing compared with genre viewing. However, this (stronger) effect weakens over time, which provides at least partial support for a “reformist” perspective on cultivation. Moreover, sample size and mode of data collection also moderate cultivation effects, with larger effects in smaller samples and in data collected via questionnaires compared with other methods.

Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study investigates the ability to differentiate bots with partisan personas from humans on Twitter. Our online experiment ( N = 656) explores how various characteristics of the participants and of the stimulus profiles bias recognition accuracy. The analysis reveals asymmetrical partisan-motivated reasoning, in that conservative profiles appear to be more confusing and Republican participants perform less well in the recognition task. Moreover, Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots. We discuss implications for how partisan identities affect motivated reasoning and how political bots exacerbate political polarization.

Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study examines perceptions of bots with partisan personas by conducting an online experiment (N = 656) that examines the ability to differentiate bots from humans on Twitter. We explore how characteristics of the experiment participants and the profiles being evaluated bias recognition accuracy. Our analysis reveals asymmetrical partisan-motivated reasoning, in that conservative profiles appear to be more confusing and Republican participants perform less well in the recognition task. Moreover, Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots. We discuss implications for how partisan identities affect motivated reasoning and how political bots exacerbate political polarization.