Does Social Media Use Promote Political Mass Polarization?

Abstract

In past years, a large amount of research was conducted to determine whether the use of social media causes political polarization. This research field, however, lacks clear terminological definitions and concepts such as fragmentation and selective exposure are often imprecisely equated with political polarization, which may explain the widespread assumption that social media cause political polarization. With this article, we aim to un ravel conceptual confusion and offer distinct definitions of affective, ideo logical, and partisan polarization. We conducted a structured literature review of 88 studies addressing the potential effects of social media use on polarization. We find the operationalization of relevant concepts to differ significantly between research projects, making the comparability of results difficult and possibly contributing to inconsistent findings. No clear evidence is found to support the generalized perception of strong polarization effects through the use of social media. Implications for future research are proposed.

Divided by the Algorithm? The (Limited) Effects of Content- and Sentiment-Based News Recommendation on Affective, Ideological, and Perceived Polarization

“…To sum up, this study adds to previous research indicating that the "filter-bubble" effect of content-based NRS seems overstated (e.g., Ludwig & Müller, 2022;Möller et al, 2018;Neumann et al, 2021). We only found small effects of the content-based NRS, enriched with sentiment, on the three dimensions of political polarization analyzed.…”

“…As deliberative democracy implies the need for exposure to a range of diverse viewpoints in order to make well-informed decisions (Gentzkow & Shapiro, 2010), a possible consequence of this development is growing political polarization, as an individually tailored news diet might amplify the growing distance between political parties, their supporters, and ideologies (Warner, 2010). Empirical research has begun to investigate (a) to which extent filter bubbles actually occur in the context of NRS and (b) which consequences these algorithmic information environments have on the user level (for an overview, see, Ludwig & Müller, 2022; Rau & Stier, 2019). Yet, evidence on both questions is mixed, at best, and a lot of blank spots remain.…”

“…The few studies that have been conducted mostly analyze the effects of algorithmic curation systems on content diversity (Möller et al, 2018; Bakshy et al, 2015; Levy, 2021; Dylko et al, 2017; Yang et al, 2016) and selective exposure of users (Nguyen et al, 2014; Beam & Kosicki, 2014; Ohme, 2021). So far, nevertheless, only a very limited number of studies incorporated actual running algorithmic recommendation systems (Ludwig & Müller, 2022). Most of this research relies upon experimental mock setups or survey designs which are sometimes combined with behavioral web-tracking data.…”

Reciprocal Communication and Political Deliberation on Twitter

“…Arora et al (2022);Iandoli et al (2021);Lorenz-Spreen et al (2023);Ludwig and Müller (2022); Terren and Borge-Bravo (2021) for systematic reviews. For hate speech in particular, seeCastaño-Pulgarín et al (2021);Strippel et al (2023);Tontodimamma et al (2021).…”