Political news is often slanted toward its publisher’s ideology and seeks to influence readers by focusing on selected aspects of contentious social and political issues. We investigate political slants in news and their influence on readers by analyzing election-related news and reader reactions to the news on Twitter. To this end, we collected election-related news from six major US news publishers who covered the 2020 US presidential elections. We computed each publisher’s political slant based on the favorability of its news toward the two major parties’ presidential candidates. We found that the election-related news coverage shows signs of political slant both in news headlines and on Twitter. The difference in news coverage of the two candidates between the left-leaning (LEFT) and right-leaning (RIGHT) news publishers is statistically significant. The effect size is larger for the news on Twitter than for headlines. And, news on Twitter expresses stronger sentiments than the headlines. We identified moral foundations in reader reactions to the news on Twitter based on Moral Foundation Theory. Moral foundations in readers’ reactions to LEFT and RIGHT differ statistically significantly, though the effects are small. Further, these shifts in moral foundations differ across social and political issues. User engagement on Twitter is higher for RIGHT than for LEFT. We posit that an improved understanding of slant and influence can enable better ways to combat online political polarization.
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Mapping the Invocation Structure of Online Political Interaction
The surge in political information, discourse, and interaction has been one of the most important developments in social media over the past several years. There is rich structure in the interaction among different viewpoints on the ideological spectrum. However, we still have only a limited analytical vocabulary for expressing the ways in which these viewpoints interact.
In this paper, we develop network-based methods that operate on the ways in which users share content; we construct invocation graphs on Web domains showing the extent to which pages from one domain are invoked by users to reply to posts containing pages from other domains. When we locate the domains on a political spectrum induced from the data, we obtain an embedded graph showing how these interaction links span different distances on the spectrum. The structure of this embedded network, and its evolution over time, helps us derive macro-level insights about how political interaction unfolded through 2016, leading up to the US Presidential election. In particular, we find that the domains invoked in replies spanned increasing distances on the spectrum over the months approaching the election, and that there was clear asymmetry between the left-to-right and right-to-left patterns of linkage.
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- Award ID(s):
- 1741441
- PAR ID:
- 10075217
- Date Published:
- Journal Name:
- Proceedings of the 2018 World Wide Web Conference
- Page Range / eLocation ID:
- 629 - 638
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3178876.3186129
