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This study explores the framing strategies and language features of U.S. news coverage surrounding sexual violence and gender issues across the ideological media spectrum at two pivotal phases of the #MeToo movement: (1) during its initial rise as a hashtag-driven social movement in 2017 and (2) during the Kavanaugh nomination and confirmation in 2018. Using structural topic modeling, community detection, and feature extraction, we reveal a heightened employment of political framing during the Kavanaugh accusations. Topical prevalence and language use in news treatment also showed clear partisan differences, consistent with theories of moral foundation and issue ownership. Implications for research on news coverage of gender rights and framing of social movements are discussed. [ABSTRACT FROM AUTHOR]

Guided by the integrative theory of communication work, we present a computational and qualitative analysis of Reddit comments authored by patient-users living with chronic overlapping pain conditions. We first employed computational text analysis to compress the data corpus via structural topic modeling. The community detection algorithm Louvain was employed to identify communities of topics within the data. Then, we extracted a subset of the data and performed a computer-aided qualitative thematic co-occurrence analysis. Topic modeling revealed 35 topics and five thematic communities. The qualitative analysis revealed that these topics mapped onto and extended the typology of communication work and contributing factors and identified co-occurrence patterns both among and across research questions. Our findings offer methodological, theoretical, and practical implications. [ABSTRACT FROM AUTHOR]