Twitter

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 whites. Moreover, given media reports suggesting that greater quantities of political misinformation are circulating on Spanish versus English-language social media, we expect 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.

Existing text scoring methods require a large corpus, struggle with short texts, or require hand-labeled data. We develop a text scoring framework that leverages generative large language models (LLMs) to (1) set texts against the backdrop of information from the near-totality of the web and digitized media, and (2) effectively transform pairwise text comparisons from a reasoning problem to a pattern recognition task. Our approach, concept-guided chain-of-thought (CGCoT), utilizes a chain of researcher-designed prompts with an LLM to generate a concept-specific breakdown for each text, akin to guidance provided to human coders. We then pairwise compare breakdowns using an LLM and aggregate answers into a score using a probability model. We apply this approach to better understand speech reflecting aversion to specific political parties on Twitter, a topic that has commanded increasing interest because of its potential contributions to democratic backsliding. We achieve stronger correlations with human judgments than widely used unsupervised text scoring methods like Wordfish. In a supervised setting, besides a small pilot dataset to develop CGCoT prompts, our measures require no additional hand-labeled data and produce predictions on par with RoBERTa-Large fine-tuned on thousands of hand-labeled tweets. This project showcases the potential of combining human expertise and LLMs for scoring tasks.