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People are increasingly exposed to science and political information from social media. One consequence is that these sites play host to “alternative influencers,” who spread misinformation. However, content posted by alternative influencers on different social media platforms is unlikely to be homogenous. Our study uses computational methods to investigate how dimensions we refer to as audience and channel of social media platforms influence emotion and topics in content posted by “alternative influencers” on different platforms. Using COVID-19 as an example, we find that alternative influencers’ content contained more anger and fear words on Facebook and Twitter compared to YouTube. We also found that these actors discussed substantively different topics in their COVID-19 content on YouTube compared to Twitter and Facebook. With these findings, we discuss how the audience and channel of different social media platforms affect alternative influencers’ ability to spread misinformation online.
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Platform Convergence or Divergence? Comparing Political Ad Content Across Digital and Social Media Platforms
When it comes to the study of the messaging of online political campaigns, theory suggests that platform divergence should be common, but much research finds considerable convergence across platforms. In this research, we examine variation across digital and social media platforms in the types of paid campaign messages that are distributed, focusing on their goals, tone, and the partisanship of political rhetoric. We use data on the content of paid election advertisements placed on YouTube, Google search, Instagram, and Facebook during the 2020 elections in the United States, examining all federal candidates who advertised on these platforms during the final 2 months of the campaign. We find that YouTube is most distinct from the other platforms, perhaps because it most resembles television, but convergence better describes the two Meta platforms, Facebook and Instagram.
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- PAR ID:
- 10568215
- Publisher / Repository:
- Sage
- Date Published:
- Journal Name:
- Social Science Computer Review
- Volume:
- 42
- Issue:
- 5
- ISSN:
- 0894-4393
- Page Range / eLocation ID:
- 1181 to 1203
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/08944393241258767
