The rise and fall of WallStreetBets: social roles and opinion leaders across the GameStop saga

• URL: http://arxiv.org/abs/2403.05876v1
• Date: Sat, 9 Mar 2024 11:02:26 GMT
• Title: The rise and fall of WallStreetBets: social roles and opinion leaders across the GameStop saga
• Authors: Anna Mancini, Antonio Desiderio, Giovanni Palermo, Riccardo Di Clemente and Giulio Cimini
• Abstract summary: We study the role of opinion leaders on Reddit WallStreetBets during the GameStop short squeeze of January 2021. Key features of opinion leaders are large risky investments and constant updates on a single stock. This work sheds light on the users' roles and dynamics that led to the GameStop short squeeze.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays human interactions largely take place on social networks, with online users' behavior often falling into a few general typologies or "social roles". Among these, opinion leaders are of crucial importance as they have the ability to spread an idea or opinion on a large scale across the network, with possible tangible consequences in the real world. In this work we extract and characterize the different social roles of users within the Reddit WallStreetBets community, around the time of the GameStop short squeeze of January 2021 -- when a handful of committed users led the whole community to engage in a large and risky financial operation. We identify the profiles of both average users and of relevant outliers, including opinion leaders, using an iterative, semi-supervised classification algorithm, which allows us to discern the characteristics needed to play a particular social role. The key features of opinion leaders are large risky investments and constant updates on a single stock, which allowed them to attract a large following and, in the case of GameStop, ignite the interest of the community. Finally, we observe a substantial change in the behavior and attitude of users after the short squeeze event: no new opinion leaders are found and the community becomes less focused on investments. Overall, this work sheds light on the users' roles and dynamics that led to the GameStop short squeeze, while also suggesting why WallStreetBets no longer wielded such large influence on financial markets, in the aftermath of this event.

Related papers

• Structure and dynamics of growing networks of Reddit threads [0.0]
  We study a Reddit community in which people participate to judge or be judged with respect to some behavior. We model threads of this community as complex networks of user interactions growing in time. We show that the evolution of Reddit networks differ from other real social networks, despite falling in the same category.
  arXiv  Detail & Related papers  (2024-09-06T07:53:33Z)
• SocialBench: Sociality Evaluation of Role-Playing Conversational Agents [85.6641890712617]
  Large language models (LLMs) have advanced the development of various AI conversational agents. SocialBench is the first benchmark designed to evaluate the sociality of role-playing conversational agents at both individual and group levels. We find that agents excelling in individual level does not imply their proficiency in group level.
  arXiv  Detail & Related papers  (2024-03-20T15:38:36Z)
• The dynamics of the Reddit collective action leading to the GameStop short squeeze [0.0]
  In early 2021, the stock prices of GameStop, AMC, Nokia, and BlackBerry experienced dramatic increases. These events showcased the potential of online social networks to catalyze financial collective action. We find that increasing Reddit discussions anticipated high trading volume before the GameStop short squeeze. Our results shed light on the dynamics behind the first large-scale financial collective action driven by social media users.
  arXiv  Detail & Related papers  (2024-01-26T16:55:38Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Identifying key players in dark web marketplaces [58.720142291102135]
  This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets. We show that a large fraction of the traded volume is concentrated in a small group of elite market participants. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
  arXiv  Detail & Related papers  (2023-06-15T20:30:43Z)
• Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users [5.695742189917657]
  Fake news on social media is a major problem with far-reaching negative repercussions on both individuals and society. In this work, we create a model through which we hope to offer a solution that will instill trust in social network communities. Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user.
  arXiv  Detail & Related papers  (2022-10-27T06:57:19Z)
• This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign [70.69387048368849]
  We study the behavior of communities with millions of active members. We develop a hybrid model, combining textual cues, community meta-data, and structural properties. We demonstrate the applicability of our model through Reddit's r/place a large-scale online experiment.
  arXiv  Detail & Related papers  (2022-01-14T08:23:16Z)
• From Reddit to Wall Street: The role of committed minorities in financial collective action [5.4015640793167154]
  In January 2021, retail investors coordinated on Reddit to target short selling activity by hedge funds on GameStop shares. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. We analyse Reddit and financial data and rationalise the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades.
  arXiv  Detail & Related papers  (2021-07-15T14:39:07Z)
• Detecting Ideal Instagram Influencer Using Social Network Analysis [0.0]
  The paper focuses on social network analysis (SNA) for a real-world online marketing strategy. The study contributes by comparing various centrality measures to identify the most central nodes in the network and uses a linear threshold model to understand the spreading behaviour of individual users.
  arXiv  Detail & Related papers  (2021-07-12T20:53:58Z)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [50.08581302050378]
  Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences. We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet) In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
  arXiv  Detail & Related papers  (2020-01-15T08:45:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.