Pith. sign in

REVIEW 2 cited by

A Public Dataset Tracking Social Media Discourse about the 2024 U.S. Presidential Election on Twitter/X

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.00376 v1 pith:PS6ZNWN6 submitted 2024-11-01 cs.SI

Ashwin Balasubramanian , Vito Zou , Hitesh Narayana , Christina You , Luca Luceri , Emilio Ferrara This is my paper
classification cs.SI
keywords datasetelectiondiscoursemediapoliticalsocialavailablekeywords
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

In this paper, we introduce the first release of a large-scale dataset capturing discourse on $\mathbb{X}$ (a.k.a., Twitter) related to the upcoming 2024 U.S. Presidential Election. Our dataset comprises 22 million publicly available posts on X.com, collected from May 1, 2024, to July 31, 2024, using a custom-built scraper, which we describe in detail. By employing targeted keywords linked to key political figures, events, and emerging issues, we aligned data collection with the election cycle to capture evolving public sentiment and the dynamics of political engagement on social media. This dataset offers researchers a robust foundation to investigate critical questions about the influence of social media in shaping political discourse, the propagation of election-related narratives, and the spread of misinformation. We also present a preliminary analysis that highlights prominent hashtags and keywords within the dataset, offering initial insights into the dominant themes and conversations occurring in the lead-up to the election. Our dataset is available at: url{https://github.com/sinking8/usc-x-24-us-election

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems

    cs.SI 2025-05 conditional novelty 7.0 of 10

    A new platform-agnostic network method reveals that 0.33% of users introduce nearly 70% of narratives that migrate between Truth Social and X during the 2024 U.S. election.

  2. Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity

    cs.HC 2025-07 conditional novelty 5.0 of 10

    When LLMs are given more context about a real social media user, they become more ideologically consistent but also more extreme, toxic, and stereotyped than the user actually is.

Pith tools