Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street

Authors

  • Richard Alvarez University of Texas at San Antonio
  • Paras Bhatt University of Texas at San Antonio
  • Xingmeng Zhao University of Texas at San Antonio
  • Anthony Rios University of Texas at San Antonio

Keywords:

New social media applications; interfaces; interaction techniques, Text categorization; topic recognition; demographic/gender/age identification, Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior

Abstract

Who actually expresses an intent to buy shares of GameStop Corporation (GME) on Reddit? What convinces people to buy stocks? Are people convinced to support a coordinated plan to adversely impact Wall Street investors? Existing literature on understanding intent has mainly relied on surveys and self-reporting; however there are limitations to these methodologies. Hence, in this paper, we develop an annotated dataset of communications centered on the GameStop phenomenon to analyze the subscriber intention behaviors within the r/WallStreetBets community to buy (or not buy) stocks. Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop. Overall, our dataset can provide insight to social scientists on the persuasive power of social movements online by adopting common language and narrative. WARNING: This paper contains offensive language that commonly appears on Reddit's r/WallStreetBets subreddit.

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Published

2022-05-31

How to Cite

Alvarez, R., Bhatt, P., Zhao, X., & Rios, A. (2022). Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 1192-1200. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19369