Elected officials occupy privileged positions in public communication about important topics—roles that extend to the digital world. In the same way that public officials stand to lead constructive online dialogue, they also hold the potential to accelerate the dissemination of low-factual and harmful content. This study aims to explore and explain the sharing of low-factual content by examining nearly 500,000 Facebook posts by U.S. state legislators from 2020 to 2021. We validate a widely used low-factual content detection approach in misinformation studies, and apply the measure to all of the posts we collect.
Despite recent advances in understanding the capabilities and limits of generative artificial intelligence (GenAI) models, we are just beginning to understand their capacity to assess and reason about the veracity of content. We evaluate multiple GenAI models across tasks that involve the rating of, and reasoning about, the credibility of information. The information in our experiments comes from content that subnational U.S. politicians post to Facebook. We find that GPT-4o outperforms other models, but all models exhibit only moderate agreement with human coders.
The rise in hate speech targeting minority communities underscores the urgent need for effective tools to detect and address harmful content in digital communication. We examine over 3 million tweets posted by state legislators between 2020 and 2021, focusing on messages directed at Asian communities. To address the nuanced nature of hate speech, we develop three comprehensive definitions for identifying hate speech. With a human-in-the-loop approach, our fine-tuned BERT-NLI model achieved improved classification performance.