An official website of the United States government
The prevalence and spread of online misinformation during the 2020 US presidential election served to perpetuate a false belief in widespread election fraud. Though much research has focused on how social media platforms connected people to election-related rumors and conspiracy theories, less is known about the search engine pathways that linked users to news content with the potential to undermine trust in elections. In this paper, we present novel data related to the content of political headlines during the 2020 US election period. We scraped over 800,000 headlines from Google's search engine results pages (SERP) in response to 20 election-related keywords—10 general (e.g., "Ballots") and 10 conspiratorial (e.g., "Voter fraud")—when searched from 20 cities across 16 states. We present results from qualitative coding of 5,600 headlines focused on the prevalence of delegitimizing information. Our results reveal that videos (as compared to stories, search results, and advertisements) are the most problematic in terms of exposing users to delegitimizing headlines. We also illustrate how headline content varies when searching from a swing state, adopting a conspiratorial search keyword, or reading from media domains with higher political bias. We conclude with policy recommendations on data transparency that allow researchers to continue to monitor search engines during elections.
more »
« less
Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections
Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
more »
« less
- PAR ID:
- 10402731
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Human Behaviour
- ISSN:
- 2397-3374
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Candido, Silvio_Eduardo Alvarez (Ed.)As social media becomes a key channel for news consumption and sharing, proliferating partisan and mainstream news sources must increasingly compete for users’ attention. While affective qualities of news content may promote engagement, it is not clear whether news source bias influences affective content production or virality, or whether any differences have changed over time. We analyzed the sentiment of ~30 million posts (ontwitter.com) from 182 U.S. news sources that ranged from extreme left to right bias over the course of a decade (2011–2020). Biased news sources (on both left and right) produced more high arousal negative affective content than balanced sources. High arousal negative content also increased reposting for biased versus balanced sources. The combination of increased prevalence and virality for high arousal negative affective content was not evident for other types of affective content. Over a decade, the virality of high arousal negative affective content also increased, particularly in balanced news sources, and in posts about politics. Together, these findings reveal that high arousal negative affective content may promote the spread of news from biased sources, and conversely imply that sentiment analysis tools might help social media users to counteract these trends.more » « less
-
Intermedia agenda setting (IAS) theory suggests that different news sources can influence each other's agenda. While this theory has been well-established in existing literature, whether it still holds in today's high-choice media environment, which includes news producers of different credibility and ideology dispositions, is an open question. Through two case studies--the 2016 and 2020 U.S. presidential elections--we show that media are still largely aligned, especially in broad topics they choose to cover, and that the level of alignment along the credibility dimension is comparable to that along the ideology dimension. Furthermore, we find that the coverage of the Republican candidate is better aligned across different media types than that of the Democratic candidate, and that media divergence has increased along both dimensions from 2016 to 2020. Finally, we demonstrate that high-credibility media still plays a dominant role in the IAS process, yet with a cautious warning of its declining IAS power for the Democratic candidate over the course of four years.more » « less
-
Scarano, Stephen; Vasudevan, Vijayalakshmi; Samory, Mattia; Yang, Kai-Cheng; Yang, JungHwan; Grabowicz, Przemyslaw A (Ed.)Social media platforms allow users to create polls to gather public opinion on diverse topics. However, we know little about what such polls are used for and how reliable they are, especially in significant contexts like elections. Focusing on the 2020 presidential elections in the U.S., this study shows that outcomes of election polls on Twitter deviate from election results despite their prevalence. Leveraging demographic inference and statistical analysis, we find that Twitter polls are disproportionately authored by male Republicans and exhibit a large bias towards candidate Donald Trump in comparison to mainstream polls. We investigate potential sources of biased outcomes from the point of view of inauthentic, automated, and counter-normative behavior. Using social media experiments and interviews with poll authors, we identify inconsistencies between public vote counts and those privately visible to poll authors, with the gap potentially attributable to purchased votes. We find that election polls tend to be more biased, contain more questionable votes, and attract more bots before the election day than after. We highlight and compare key factors contributing to biased poll outcomes. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple of thousand such polls were posted in 2020. The study discusses the implications of biased election polls in the context of transparency and accountability of social media platforms.more » « less
-
Polls posted on social media can provide information about public opinion on a variety of issues from business decisions to support for presidential election candidates. However, it is largely unknown whether the information provided by social polls is useful or not. To enhance our understanding of social polls, we examine nearly two thousand Twitter polls gauging support for U.S. presidential candidates during the 2016 and 2020 election campaigns. First, we describe the prevalence of social polls. Second, we characterize social polls in terms of the engagement they elicit and the response options they present. Third, leveraging machine learning models, we infer and describe several characteristics, including demographics and political leanings, of the users who author and interact with social polls. Finally, we study the relationship between social poll results, their attributes, and the characteristics of users interacting with them. Our findings suggest how and to what extent polling on Twitter is biased in terms of content, authorship, and audience. The 2016 and 2020 polls were predominantly crafted by older males and manifested a pronounced bias favoring candidate Donald Trump, whereas traditional surveys favored Democratic candidates. We further identify and explore the potential reasons for such biases and discuss their repercussions.more » « less
