Collaborative Research: Further Strengthening Qualitative Research Through Methodological Innovation and Integration: Media Coverage of Social Movements

要了解弱势群体的社会运动和政治，就需要了解媒体对社会运动和政治倡导组织的报道。 这些组织为寻求社会变革、帮助构建政治身份和促进公民参与提供了关键资源。大众新闻媒体的关注对这些组织至关重要。 新闻报道表明，这些组织被视为合法的发言人，并帮助它们影响公众舆论和政策议程。 这个项目增加了以前收集的大约一百万篇文章发表在二十世纪的四个全国性报纸-纽约时报，华盛顿邮报，华尔街日报，和洛杉矶时报。该项目将通过ProQuest Historical Newspapers收集所有18份报纸的数据，包括大量的区域和非洲裔美国人报纸，并将这些数据扩展到本世纪的前15年。该项目还将收集有关文章的更广泛的信息，主要利益集团的覆盖数据，以及样本中倡导和运动组织的组织数据。这些数据将得到电视新闻和社交媒体数据的补充。除了创建这一宝贵的数据集外，该项目的主要优点包括解决了关于运动及其组织的覆盖数量和质量的原因的几个研究问题。该项目还将培训研究生，编写若干学术文章，确定和解释运动范围及其后果，并编写一本学术专著。该项目解决了关于这些组织的几个关键的经验问题和"为什么"的问题。新闻报道：哪些组织和联盟运动获得了最大的报纸和电视报道？ 哪些组织在其运动中受到最大的关注？随着时间的推移和运动的不同，覆盖面有何变化？ 全国性、地区性和非裔美国人报纸的报道模式是否相似？在提到运动的文章中，运动是否通常得到实质性的报道？ 是什么原因导致报纸对一场运动及其活动进行了广泛的报道？ 是什么原因导致报纸对运动中的各个组织进行了广泛的报道？ 报道的模式是否与社会运动的主要理论相一致？资源动员、新社会运动、民怨、政治过程与政治改革理论？ 随着报纸的衰落以及互联网和社交媒体的兴起，报纸报道和运动活动之间的联系发生了怎样的变化？在什么情况下运动的参与者会得到实质性的新闻报道？ 该项目将通过综合对计数数据的负二项回归分析和定性方法，特别是模糊集定性比较分析和比较历史分析来解决这些问题。 这些分析将联合收割机大数据收集与报纸的定性编码相结合。 与大多数大N工作不同，该项目可以返回到原始数据，即报纸文章，以完善假设并建立理论。

To understand social movements and the politics of the disadvantaged one needs to understand the media coverage of social movement and political advocacy organizations. These organizations provide critical resources to seek social change, help construct political identities, and spur civic engagement. The attention of the mass news media is critical to these organizations. News coverage shows that these organizations are being treated as legitimate spokespersons and helps them influence public opinion and policy agendas. This project augments a previous collection of approximately one million articles published in the twentieth century in four national newspapers--the New York Times, Washington Post, Wall Street Journal, and Los Angeles Times. This project will capture data in all 18 newspapers available through ProQuest Historical Newspapers, including a wealth of regional and African-American newspapers, and will extend these data through the first 15 years of this century. The project will also gather more extensive information about the articles, coverage data on major interest groups, and organizational data on the advocacy and movement organizations in the sample. These data will be supplemented with television news and social media data. In addition to creating this valuable data set, the key merits of the project include the addressing of several research questions regarding the causes of the amount and quality of coverage of movements and their organizations. The project will also result in the training of graduate students and produce several scholarly articles that identify and explain movement coverage and its consequences, and a scholarly monograph. The project addresses several key empirical and "why" questions about these organizations? news coverage: Which organizations and allied movements have received the greatest newspaper and TV coverage? Which organizations have received the greatest attention in their movements? How has the coverage changed over time and across movements? Are coverage patterns similar across national, regional, and African-American newspapers? Do movements usually gain substantive coverage in articles in which they are mentioned? What accounts for extensive newspaper coverage of a movement and its activities? What accounts for extensive newspaper coverage for individual organizations within a movement? Are the patterns of coverage consistent with the main theories of social movements?resource mobilization, new social movement, grievance, and political process and political reform theories? How has the connection between newspaper coverage and movement activity changed with the decline of newspapers and the rise of the Internet and social media? Under which conditions do movement actors receive substantive news coverage? The project will address these questions by synthesizing negative binomial regression analyses on counts data and qualitative methods, notably fuzzy set Qualitative Comparative Analyses and comparative historical analyses. The analyses combine big-data gathering with qualitative coding of newspapers. Unlike most large-N work, the project can return to the original data, the newspaper articles, to refine hypotheses and build theory.