When social movement organizations receive extensive newspaper coverage, why is it sometimes substantive and sometimes not? By “substantive,” we mean coverage that reflects serious treatment of the movement's issues, demands, or policy claims. Scholars agree that the news media are key to movement organizations' influence, helping them alter public discourse and effect political change, but often find that protests are covered nonsubstantively. Employing insights from literatures on historical institutionalism, the social organization of the news, and the consequences of movements, we elaborate an “institutional mediation” model that identifies the interactive effects on coverage of news institutions' operating procedures, movement organizations' characteristics and action, and political contexts. Although movement actors suffer compound legitimacy deficits with journalists, the institutional mediation model identifies the openings news institutions provide, the movement organizational characteristics, the forms of collective action likely to induce substantive news treatment, and the political contexts that will amplify or dampen these effects. We derive four interactive hypotheses from this model, addressing the effects of organizational identities, collective action, and political contexts on news outcomes. We appraise the hypotheses with comparative and qualitative comparative analyses of more than 1000 individually coded articles discussing the five most-covered organizations of the 1960s U.S. civil rights movement across four national newspapers. We find support for each hypothesis and discuss the implications for other movement organizations and the current media context.
more »
« less
What Drives the News Coverage of US Social Movements?
What drives the news coverage of social movements in the professional news media? We address this question by elaborating an institutional mediation model arguing that the news values, routines, and characteristics of the news media induce them to pay attention to movements depending on their characteristics and the political contexts in which they engage. The news-making characteristics of movements include their disruptive capacities and organizational strength, and the political contexts include a partisan regime in power, benefitting from national policies, and congressional investigations. To appraise these arguments, we analyze approximately 1 million news articles mentioning 29 social movements over the twentieth century, published in four national newspapers. We use negative binomial regression analyses and separate time-series analyses of the labor movement to assess the model’s robustness across different movements, time periods, and news sources. In each analysis, the results support the hypotheses based on the institutional mediation model. More generally, we argue that the influence of social movements on institutions depends on the structure and operating procedures of those institutions. This insight has implications for future studies of the influence of movements on major social institutions.
more » « less- PAR ID:
- 10413625
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Social Forces
- Volume:
- 102
- Issue:
- 1
- ISSN:
- 0037-7732
- Format(s):
- Medium: X Size: p. 242-262
- Size(s):
- p. 242-262
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Information manipulation is widespread in today’s media environment. Online networks have disrupted the gatekeeping role of traditional media by allowing various actors to influence the public agenda; they have also allowed automated accounts (or bots) to blend with human activity in the flow of information. Here, we assess the impact that bots had on the dissemination of content during two contentious political events that evolved in real time on social media. We focus on events of heightened political tension because they are particularly susceptible to information campaigns designed to mislead or exacerbate conflict. We compare the visibility of bots with human accounts, verified accounts, and mainstream news outlets. Our analyses combine millions of posts from a popular microblogging platform with web-tracking data collected from two different countries and timeframes. We employ tools from network science, natural language processing, and machine learning to analyze the diffusion structure, the content of the messages diffused, and the actors behind those messages as the political events unfolded. We show that verified accounts are significantly more visible than unverified bots in the coverage of the events but also that bots attract more attention than human accounts. Our findings highlight that social media and the web are very different news ecosystems in terms of prevalent news sources and that both humans and bots contribute to generate discrepancy in news visibility with their activity.more » « less
-
more » « less
Abstract The extensive data generated on social media platforms allow us to gain insights over trending topics and public opinions. Additionally, it offers a window into user behavior, including their content engagement and news sharing habits. In this study, we analyze the relationship between users’ political ideologies and the news they share during Argentina’s 2019 election period. Our findings reveal that users predominantly share news that aligns with their political beliefs, despite accessing media outlets with diverse political leanings. Moreover, we observe a consistent pattern of users sharing articles related to topics biased to their preferred candidates, highlighting a deeper level of political alignment in online discussions. We believe that this systematic analysis framework can be applied to similar scenarios in different countries, especially those marked by significant political polarization, akin to Argentina.
-
During the COVID-19 pandemic, local news organizations have played an important role in keeping communities informed about the spread and impact of the virus. We explore how political, social media, and economic factors impacted the way local media reported on COVID-19 developments at a national scale between January 2020 and July 2021. We construct and make available a dataset of over 10,000 local news organizations and their social media handles across the U.S. We use social media data to estimate the population reach of outlets (their “localness”), and capture underlying content relationships between them. Building on this data, we analyze how local and national media covered four key COVID-19 news topics: Statistics and Case Counts, Vaccines and Testing, Public Health Guidelines, and Economic Effects. Our results show that news outlets with higher population reach reported proportionally more on COVID-19 than more local outlets. Separating the analysis by topic, we expose more nuanced trends, for example that outlets with a smaller population reach covered the Statistics and Case Counts topic proportionally more, and the Economic Effects topic proportionally less. Our analysis further shows that people engaged proportionally more and used stronger reactions when COVID-19 news were posted by outlets with a smaller population reach. Finally, we demonstrate that COVID-19 posts in Republican-leaning counties generally received more comments and fewer likes than in Democratic counties, perhaps indicating controversy.more » « less
-
Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of mainstream media in US and social media posts from Reddit and Twitter. We conduct in-depth analyses on news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.more » « less
