Comparative Sentiment Analysis of Campaign and Third-Party Communications in the 2024 U.S. Elections

In an era defined by constant digital interaction, personal information is frequently collected, shared, and repurposed in ways that raise serious concerns about privacy and transparency. As part of a broader project, which investigates how individuals’ personal information is used, an experiment was designed to test how political campaigns wittingly or unwittingly use personal information. To study these behaviors in a realistic setting, believable synthetic identities were generated and a controlled sign-up framework was built to register those identities across almost every pre-primary campaign in the 2024 U.S. election cycle, enabling the longitudinal collection of emails, voicemails, and SMS messages. Over three years, these communications were accumulated and aggregated, and a sentiment-analysis pipeline was then developed to characterize the tone of incoming messages. This analysis consisted of classifying each of the 77k emails in a continuous range from extremely negative (0) to extremely positive (1) and then analyzing their behavior. The descriptive results showed that while the Democratic and Republican parties were close to one another with moderate, marginally positive average sentiments, Republican emails contained a higher observed proportion of emails in both tails of the sentiment distributions. These party-level differences were not tested for statistical significance and should therefore be interpreted as descriptive observations of the collected dataset. More extreme sentiments are routinely displayed, however, toward the candidates by news sources and special interest groups, with a visible translation of expressed sentiment to apparent political bias.