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Judith Gilsbach

Publications and source records attributed to Judith Gilsbach.

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Longitudinal Relational Publics and their Discursive Overlap with Issue Publics

Online discussions of political issues do not always happen in places explicitly dedicated to political talk; they also arise in online spaces focused on at least nominally apolitical interests, identities, and/or places. Whatever one's normative view of politics entering these ``online third spaces,'' understanding who brings political issues into them, and when, requires studying these spaces at scale. In turn, studying these spaces at scale requires a construct that captures both who is speaking and who is listening, and that holds up over time. Building on Bruns' distinction between participant-centered personal publics and post-centered issue publics, we introduce the longitudinal relational networked public (or, simply, the longitudinal public): the coupling of discourse produced by a socially connected set of creators with the durable attention their shared audience gives it. The longitudinal public departs from related relational constructs in three ways: it is anchored in the attention patterns of a non-elite, population-level audience; it treats within-public structure as an object of analysis rather than assuming homogeneity; and it incorporates the audience as a force that shapes creator discourse. In a case study, we identify 150 longitudinal publics from the following ties of a panel of Twitter/X users matched to U.S. voter records, then measure their discursive overlap with the electoral politics and Black Lives Matter issue publics across 2020, a period spanning the murder of George Floyd and the general election. We find that the spaces that best fit the idea of a third space have the most politically heterogeneous audiences, and thus the most potential room for cross-partisan talk. This result, among several others, shows how a relational, audience-aware construct can reveal where and through whom political talk enters everyday online life.

cs.SI

Beyond time delays: How web scraping distorts measures of online news consumption

As the exploration of digital behavioral data revolutionizes communication research, understanding the nuances of data collection methodologies becomes increasingly pertinent. This study focuses on one prominent data collection approach, web scraping, and more specifically, its application in the growing field of research relying on web browsing data. We investigate discrepancies between content obtained directly during user interaction with a website (in-situ) and content scraped using the URLs of participants' logged visits (ex-situ) with various time delays (0, 30, 60, and 90 days). We find substantial disparities between the methodologies, uncovering that errors are not uniformly distributed across news categories regardless of classification method (domain, URL, or content analysis). These biases compromise the precision of measurements used in existing literature. The ex-situ collection environment is the primary source of the discrepancies (~33.8%), while the time delays in the scraping process play a smaller role (adding ~6.5 percentage points in 90 days). Our research emphasizes the need for data collection methods that capture web content directly in the user's environment. However, acknowledging its complexities, we further explore strategies to mitigate biases in web-scraped browsing histories, offering recommendations for researchers who rely on this method and laying the groundwork for developing error-correction frameworks.

cs.CY