TweetPap:一个研究科学论文的社交媒体话语的数据集

Naman Jain, M. Singh
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引用次数: 1

摘要

如今,研究人员已经转向像Twitter这样的平台来传播有关他们的想法和经验证据的信息。最近的研究表明,社交媒体会影响一篇论文的科学影响力。然而,这些研究仅利用tweet计数来表示Twitter活动。在本文中,我们提出了一个大规模数据集TweetPap,该数据集引入了引用/推文的时间信息和推文的元数据,以量化和理解社交媒体上科学论文的话语。该数据集可在https://github.com/lingo-iitgn/TweetPap上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TweetPap: A Dataset to Study the Social Media Discourse of Scientific Papers
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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