Back to Home

Request Correction

Use this form to request corrections to the paper metadata. Select the fields that need correction and provide the correct information.

Correction Guidelines

  1. Click the edit button next to a field to report a correction.
  2. Fill in the suggested correction value for each field you want to correct.
  3. Provide your name and email so we can contact you if needed.

Paper Information

lrec2026-ws-politicalnlp-06

Analyzing Political Stances on Twitter/X in the Lead-up to the 2024 U.S. Election

Paper Fields

Click the edit button next to a field to report a correction.

Title

Analyzing Political Stances on Twitter/X in the Lead-up to the 2024 U.S. Election

Abstract

Social media platforms play a pivotal role in shaping public opinion and amplifying political discourse, particularly during elections. However, the same dynamics that foster democratic engagement can also exacerbate polarization. To better understand these challenges, here, we investigate the ideological positioning of tweets related to the 2024 U.S. Presidential Election. To this end, we analyze 1,235 tweets from key political figures and 63,322 replies, and classify ideological stances into Pro-Democrat, Anti-Republican, Pro-Republican, Anti-Democrat, and Neutral categories. Using a classification pipeline involving three large language models (LLMs)—GPT-4o, Gemini-Pro, and Claude-Opus—and validated by human annotators, we explore how ideological alignment varies between candidates and constituents. We find that Republican candidates author significantly more tweets in criticism of the Democratic party and its candidates than vice versa, but this relationship does not hold for replies to candidate tweets. Furthermore, we highlight shifts in public discourse observed during key political events. By shedding light on the ideological dynamics of online political interactions, these results provide insights for policymakers and platforms seeking to address polarization and foster healthier political dialogue.


Authors

Expand an author to correct their information. Use the remove button to request author removal, or add a new author.


PDF Attachment

You may attach a PDF as a corrected version of the paper. Max file size: 10MB. Only PDF files are accepted.

Drag & drop a PDF here, or click to select

Your Information

Author Declaration *

Select at least one field to correct using the edit buttons above.