TweetPap: A Dataset to Study the Social Media Discourse of Scientific Papers
Naman Jain, Mayank Singh
TL;DR
TweetPap is a large-scale dataset that captures detailed temporal and metadata information of tweets related to scientific papers, enabling deeper analysis of social media discourse and its influence on scientific impact.
Contribution
The paper introduces TweetPap, a novel dataset with temporal and metadata details of tweets about scientific papers, facilitating advanced social media discourse analysis.
Findings
Dataset includes temporal tweet data and metadata
Enables analysis of social media influence on scientific impact
Supports research on scientific communication on Twitter
Abstract
Nowadays, researchers have moved to platforms like Twitter to spread information about their ideas and empirical evidence. Recent studies have shown that social media affects the scientific impact of a paper. However, these studies only utilize the tweet counts to represent Twitter activity. In this paper, we propose TweetPap, a large-scale dataset that introduces temporal information of citation/tweets and the metadata of the tweets to quantify and understand the discourse of scientific papers on social media. The dataset is publicly available at https://github.com/lingo-iitgn/TweetPap
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Taxonomy
TopicsScientific Computing and Data Management · Biomedical Text Mining and Ontologies · Complex Network Analysis Techniques