United States

The aggregation of knowledge embedded in large language models (LLMs) holds the promise of new solutions to problems of observability and measurement in the social sciences. We examine this potential in a challenging setting: measuring latent ideology — crucial for better understanding core political functions such as democratic representation. We scale pairwise liberal-conservative comparisons between members of the 116th U.S. Senate using prompts made to ChatGPT. Our measure strongly correlates with widely used liberal-conservative scales such as DW-NOMINATE. Our scale also has interpretative advantages, such as not placing senators who vote against their party for ideologically extreme reasons towards the middle. Our measure is more strongly associated with political activists' perceptions of senators than other measures, consistent with LLMs synthesizing vast amounts of politically relevant data from internet/book corpora rather than memorizing existing measures. LLMs will likely open new avenues for measuring latent constructs utilizing modeled information from massive text corpora.

There is widespread concern that foreign actors are using social media to interfere in elections worldwide. Yet data have been unavailable to investigate links between exposure to foreign influence campaigns and political behavior. Using longitudinal survey data from US respondents linked to their Twitter feeds, we quantify the relationship between exposure to the Russian foreign influence campaign and attitudes and voting behavior in the 2016 US election. We demonstrate, first, that exposure to Russian disinformation accounts was heavily concentrated: only 1% of users accounted for 70% of exposures. Second, exposure was concentrated among users who strongly identified as Republicans. Third, exposure to the Russian influence campaign was eclipsed by content from domestic news media and politicians. Finally, we find no evidence of a meaningful relationship between exposure to the Russian foreign influence campaign and changes in attitudes, polarization, or voting behavior. The results have implications for understanding the limits of election interference campaigns on social media.