Abstract

As social media has become an increasingly important source of news and information, scholars have documented the phenomenon of echo chambers (Cinelli et al., 2021), where rather than encountering a broad range of viewpoints, users are often exposed to content that reinforces their existing beliefs and partisan identities. This phenomenon has also coincided with rising affective polarization—the tendency for partisans to like their own party and actively dislike members of the opposing party (Iyengar & Wagner, 2025). Taken together, these trends raise an important question: In an increasingly polarized environment, when are people receptive to views that challenge their own? Prior research suggests that individuals vary in their baseline receptiveness to opposing views, which is defined as “the willingness to access, consider, and evaluate contradictory opinions in a relatively impartial manner” (Minson & Chen, 2022). Yet receptiveness may not only be a matter of individual disposition. As social media platforms and news aggregators largely dictate which news stories users see, the source that curates or recommends information may also determine whether individuals are receptive to opposing viewpoints. Prior work on algorithmic news curation finds mixed results: while some view it as an efficient, customizable, and potentially less biased way to consume information (Thurman et al., 2019), others worry about its opaque, “black box” nature and potentially biased recommendations (Ngo & Krämer, 2022). The main research question this project asks is: How does the news aggregator source influence individuals’ receptiveness to opposing views online, and how does this relationship vary with different levels of affective polarization? To examine the research question, I propose a series of online experiments in which participants are randomly assigned to view a news aggregator interface that presents a series of news articles curated by one of three sources: AI, a human aggregator from their partisan ingroup, or a human aggregator from their partisan outgroup. I expect that participants in the human outgroup condition will report the lowest receptiveness to opposing views, and that participants exposed to AI-curated news will show greater receptiveness to opposing views than those exposed to outgroup-curated news feeds. These effects are expected to be the strongest among individuals high in affective polarization, who are more likely to perceive the out-group human news aggregator as identity-threatening, but may respond more positively to an AI news aggregator. This study expects to contribute to research on social media and political polarization by examining how the online news aggregator source affects receptiveness to opposing views. Practically, the findings may inform the design of AI-based news aggregator interfaces aimed at encouraging greater engagement with diverse viewpoints online.

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