Academic Research

Social media is used by millions of Americans to access news and politics. Yet there are no studies, to date, examining whether these behaviors systematically vary for those whose political incorporation process is distinct from those in the majority. We fill this void by examining how Latino online political activity compares to that of white Americans and the role of language in Latinos’ online political engagement. We hypothesize that Latino online political activity is comparable to white Americans. Moreover, given media reports suggesting that greater quantities of political misinformation are circulating on Spanish versus English-language social media, we expect that reliance on Spanish-language social media for news predicts beliefs in inaccurate political narratives. Our survey findings, which we believe to be the largest original survey of the online political activity of Latinos and whites, reveal support for these expectations. Latino social media political activity, as measured by sharing/viewing news, talking about politics, and following politicians, is comparable to whites, both in self-reported and digital trace data. Latinos also turned to social media for news about COVID-19 more often than did whites. Finally, Latinos relying on Spanish-language social media usage for news predicts beliefs in election fraud in the 2020 U.S. Presidential election.

Online panels have become an important resource for research in political science, but the financial compensation involved incentivizes respondents to become “survey professionals”, which raises concerns about data quality. We provide evidence on survey professionalism using behavioral web browsing data from three U.S. samples, recruited via Lucid, YouGov, and Facebook (total n = 3,886). Survey professionalism is common but varies across samples: By our most conservative measure, we identify 1.7% of respondents on Facebook, 7.9% of respondents on YouGov, and 34.3% of respondents on Lucid as survey professionals. However, evidence that professionals lower data quality is limited: they do not systematically differ demographically or politically from non-professionals and do not respond more randomly—although they are somewhat more likely to speed, to straightline, and to take questionnaires repeatedly. While concerns are warranted, we conclude that survey professionals do not, by and large, distort inferences of research based on online panels.

A challenge for public opinion surveys is achieving representativeness of respondents across demographic groups. We test the extent to which ideological alignment with a survey’s sponsor shapes differential partisan response and users’ choice of whether to participate in a research study on Facebook. While the use of Facebook advertisements for recruitment has increased in recent years and offers potential benefits, it can yield difficulties in recruiting politically representative samples. We recruit respondents for a short survey through two otherwise identical advertisements associated with either New York University (from a liberal state) or the University of Mississippi (from a conservative state). Contrary to our expectations, we don’t find an asymmetry in completion rates between self-reported Democrats and Republicans based on the survey sponsor. Nor do we find statistically significant differences in attitudes of respondents across the two survey sponsors when we control for observables.

In most advanced democracies, concerns about the spread of misinformation are typically associated with feed-based social media platforms like Twitter and Facebook. These platforms also account for the vast majority of research on the topic. However, in most of the world, particularly in Global South countries, misinformation often reaches citizens through social media messaging apps, particularly WhatsApp. To fill the resulting gap in the literature, we conducted a multimedia deactivation experiment to test the impact of reducing exposure to potential sources of misinformation on WhatsApp during the weeks leading up to the 2022 Presidential election in Brazil. We find that this intervention significantly reduced participants’ exposure to false rumors circulating widely during the election. However, consistent with theories of mass media minimal effects, a short-term reduction in exposure to misinformation ahead of the election did not lead to significant changes in belief accuracy, political polarization, or well-being.

Existing text scaling methods often require a large corpus, struggle with short texts, or require labeled data. We develop a text scaling method that leverages the pattern recognition capabilities of generative large language models (LLMs). Specifically, we propose concept-guided chain-of-thought (CGCoT), which uses prompts designed to summarize ideas and identify target parties in texts to generate concept-specific breakdowns, in many ways similar to guidance for human coder content analysis. CGCoT effectively shifts pairwise text comparisons from a reasoning problem to a pattern recognition problem. We then pairwise compare concept-specific breakdowns using an LLM. We use the results of these pairwise comparisons to estimate a scale using the Bradley-Terry model. We use this approach to scale affective speech on Twitter. Our measures correlate more strongly with human judgments than alternative approaches like Wordfish. Besides a small set of pilot data to develop the CGCoT prompts, our measures require no additional labeled data and produce binary predictions comparable to a RoBERTa-Large model fine-tuned on thousands of human-labeled tweets. We demonstrate how combining substantive knowledge with LLMs can create state-of-the-art measures of abstract concepts.

Existing approaches to estimating politicians' latent positions along specific dimensions often fail when relevant data is limited. We leverage the embedded knowledge in generative large language models (LLMs) to address this challenge and measure lawmakers' positions along specific political or policy dimensions. We prompt an instruction/dialogue-tuned LLM to pairwise compare lawmakers and then scale the resulting graph using the Bradley-Terry model. We estimate novel measures of U.S. senators' positions on liberal-conservative ideology, gun control, and abortion. Our liberal-conservative scale, used to validate LLM-driven scaling, strongly correlates with existing measures and offsets interpretive gaps, suggesting LLMs synthesize relevant data from internet and digitized media rather than memorizing existing measures. Our gun control and abortion measures -- the first of their kind -- differ from the liberal-conservative scale in face-valid ways and predict interest group ratings and legislator votes better than ideology alone. Our findings suggest LLMs hold promise for solving complex social science measurement problems.

Can a series of online conversations with a marginalized outgroup member improve majority group members’ attitudes about that outgroup? While the intergroup contact literature provides (mixed) insights about the effects of extended interactions between groups, less is known about how relatively short and casual interactions may play out in highly polarized settings. In an experiment in India, I bring together Hindus and Muslims for five days of conversations on WhatsApp, a popular messaging platform, to investigate the extent to which chatting with a Muslim about randomly assigned discussion prompts affects Hindus’ perceptions of Muslims and approval for mainstream religious nationalist statements. I find that intergroup conversations greatly reduce prejudice against Muslims and approval for religious nationalist statements at least two to three weeks post-conversation. Intergroup conversations about non-political issues are especially effective at reducing prejudice, while conversations about politics substantially decrease support for religious nationalism. I further show how political conversations and non-political conversations affect attitudes through distinct mechanisms.

To what extent does the YouTube recommendation algorithm push users into echo chambers, ideologically biased content, or rabbit holes? Despite growing popular concern, recent work suggests that the recommendation algorithm is not pushing users into these echo chambers. However, existing research relies heavily on the use of anonymous data collection that does not account for the personalized nature of the recommendation algorithm. We asked a sample of real users to install a browser extension that downloaded the list of videos they were recommended. We instructed these users to start on an assigned video and then click through 20 sets of recommendations, capturing what they were being shown in real time as they used the platform logged into their real accounts. Using a novel method to estimate the ideology of a YouTube video, we demonstrate that the YouTube recommendation algorithm does, in fact, push real users into mild ideological echo chambers where, by the end of the data collection task, liberals and conservatives received different distributions of recommendations from each other, though this difference is small. While we find evidence that this difference increases the longer the user followed the recommendation algorithm, we do not find evidence that many go down `rabbit holes' that lead them to ideologically extreme content. Finally, we find that YouTube pushes all users, regardless of ideology, towards moderately conservative and an increasingly narrow range of ideological content the longer they follow YouTube's recommendations.

We test whether candidates move to the extremes before a primary but then return to the center for the general election to appeal to the different preferences of each electorate. Incumbents are now more vulnerable to primary challenges than ever as social media offers a viable pathway for fundraising and messaging to challengers, while homogeneity of districts has reduced general election competitiveness. To assess candidates' ideological trajectories, we estimate the revealed ideology of 2020 congressional candidates (incumbents, their primary challengers, and open seat candidates) before and after their primaries, using a homophily-based measure of domains shared on Twitter. This method provides temporally granular data to observe changes in communication within a single election campaign cycle. We find that incumbents did move towards extremes for their primaries and back towards the center for the general election, but only when threatened by a well-funded primary challenge, though non-incumbents did not.

Social networks play an important role in many political science studies. With the rise of social media, these networks have grown in both size and complexity. Analysis of these large networks requires generation of feature representations that can be used in machine learning models. One way to generate these feature representations is to use network embedding methods for learning low-dimensional feature representations of nodes and edges in a network. While there is some literature comparing the advantages and shortcomings of these models, to our knowledge, there has not been any analysis on the applicability of network embedding models to classification tasks in political science. In this paper, we compare the performance of five prominent network embedding methods on prediction of ideology of Twitter users and ideology of Internet domains. We find that LINE provides the best feature representation across all 4 datasets that we use, resulting in the highest performance accuracy. Finally, we provide the guidelines for researchers on the use of these models for their own research.

Automated text classification has rapidly become an important tool for political analysis. Recent advancements in NLP enabled by advances in deep learning now achieve state of the art results in many standard tasks for the field. However, these methods require large amounts of both computing power and text data to learn the characteristics of the language, resources which are not always accessible to political scientists. One solution is a transfer learning approach, where knowledge learned in one area or source task is transferred to another area or a target task. A class of models that embody this approach are language models, which demonstrate extremely high levels of performance. We investigate the performance of these models in the political science by comparing multiple text classification methods. We find RoBERTa and XLNet, language models that rely on theTransformer, require fewer computing resources and less training data to perform on par with – or outperform – several political science text classification methods. Moreover, we find that the increase in accuracy is especially significant in the case of small labeled data, highlighting the potential for reducing the data-labeling cost of supervised methods for political scientists via the use of pretrained language models.

The role of the media in influencing people’s attitudes and opinions is difficult to demonstrate because media consumption by survey respondents is usually unobserved in datasets containing information on attitudes and vote choice. This paper leverages behavioral data combined with responses from a multi-wave panel to test whether Democrats who see more stories from liberal news sources on Twitter develop more liberal positions over time and, conversely, whether Republicans are more likely to revise their views in a conservative direction if they are exposed to more news on Twitter from conservative media sources. We find evidence that exposure to ideologically framed information and arguments changes voters’ own positions, but has a limited impact on perceptions of where the candidates stand on the issues.

The following report is intended to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news. The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known—and unknown—about the relationship between social media, political polarization, and disinformation. The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them.