This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign

• URL: http://arxiv.org/abs/2201.05334v1
• Date: Fri, 14 Jan 2022 08:23:16 GMT
• Title: This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign
• Authors: Abraham Israeli, Alexander Kremiansky, Oren Tsur
• Abstract summary: 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.
• Score: 70.69387048368849
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. In this work we study the behavior of thousands of communities with millions of active members. We define a novel task: predicting which community will undertake an unexpected, large-scale, distributed campaign. To this end, we develop a hybrid model, combining textual cues, community meta-data, and structural properties. We show how this multi-faceted model can accurately predict large-scale collective decision-making in a distributed environment. We demonstrate the applicability of our model through Reddit's r/place a large-scale online experiment in which millions of users, self-organized in thousands of communities, clashed and collaborated in an effort to realize their agenda. Our hybrid model achieves a high F1 prediction score of 0.826. We find that coarse meta-features are as important for prediction accuracy as fine-grained textual cues, while explicit structural features play a smaller role. Interpreting our model, we provide and support various social insights about the unique characteristics of the communities that participated in the r/place experiment. Our results and analysis shed light on the complex social dynamics that drive collective behavior, and on the factors that propel user coordination. The scale and the unique conditions of the r/place experiment suggest that our findings may apply in broader contexts, such as online activism, (countering) the spread of hate speech and reducing political polarization. The broader applicability of the model is demonstrated through an extensive analysis of the WallStreetBets community, their role in r/place and the GameStop short squeeze campaign of 2021.

Related papers

• Generative Agent Simulations of 1,000 People [56.82159813294894]
  We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions.
  arXiv  Detail & Related papers  (2024-11-15T11:14:34Z)
• The Dynamics of Social Conventions in LLM populations: Spontaneous Emergence, Collective Biases and Tipping Points [0.0]
  We investigate the dynamics of conventions within populations of Large Language Model (LLM) agents using simulated interactions. We show that globally accepted social conventions can spontaneously arise from local interactions between communicating LLMs. Minority groups of committed LLMs can drive social change by establishing new social conventions.
  arXiv  Detail & Related papers  (2024-10-11T16:16:38Z)
• On the steerability of large language models toward data-driven personas [98.9138902560793]
  Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to achieve controllable generation of specific viewpoints using LLMs.
  arXiv  Detail & Related papers  (2023-11-08T19:01:13Z)
• 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)
• With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns [2.487445341407889]
  We study the correspondence between the effectiveness of a community, measured by its success level in a competitive online campaign, and the dynamics between its members. To this end, we define a novel task: predicting the success level of online communities in Reddit's r/place.
  arXiv  Detail & Related papers  (2023-07-18T21:43:37Z)
• Flexible social inference facilitates targeted social learning when rewards are not observable [58.762004496858836]
  Groups coordinate more effectively when individuals are able to learn from others' successes. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
  arXiv  Detail & Related papers  (2022-12-01T21:04:03Z)
• Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? [90.35044668396591]
  A recurring theme in machine learning is algorithmic monoculture: the same systems, or systems that share components, are deployed by multiple decision-makers. We propose the component-sharing hypothesis: if decision-makers share components like training data or specific models, then they will produce more homogeneous outcomes. We test this hypothesis on algorithmic fairness benchmarks, demonstrating that sharing training data reliably exacerbates homogenization. We conclude with philosophical analyses of and societal challenges for outcome homogenization, with an eye towards implications for deployed machine learning systems.
  arXiv  Detail & Related papers  (2022-11-25T09:33:11Z)
• It is rotating leaders who build the swarm: social network determinants of growth for healthcare virtual communities of practice [0.0]
  The purpose of this paper is to identify the factors influencing the growth of healthcare virtual communities of practice (VCoPs) through a seven-year longitudinal study conducted using metrics from social-network and semantic analysis.
  arXiv  Detail & Related papers  (2021-05-26T16:15:31Z)
• Community detection and Social Network analysis based on the Italian wars of the 15th century [0.0]
  We study social network modelling by using human interaction as a basis. We propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. We develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network.
  arXiv  Detail & Related papers  (2020-07-06T11:05:07Z)
• How individual behaviors drive inequality in online community sizes: an agent-based simulation [8.575789696858477]
  Our work bridges the divide by testing whether two influential social mechanisms can also explain the distribution of community sizes. Using agent-based simulations, we evaluate how well individual-level processes of social exposure and decisions based on individual expected benefits reproduce empirical community size data from Reddit. Our results also illustrate the potential value of agent-based simulation to online community researchers to both evaluate and bridge individual and group-level theories.
  arXiv  Detail & Related papers  (2020-06-04T20:20:43Z)

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.