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

arXiv: https://arxiv.org/abs/2201.05334 · HTML: https://mlsanity.com/paper/2201.05334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 89 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Deep Multimodal Fusion by Channel Exchanging](https://mlsanity.com/paper/2011.05005/) (2020)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?](https://mlsanity.com/paper/2104.10809/) (2021)
- [Echo Chambers: Emotional Contagion and Group Polarization on Facebook](https://mlsanity.com/paper/1607.01032/) (2016)
- [DyNet: The Dynamic Neural Network Toolkit](https://mlsanity.com/paper/1701.03980/) (2017)
- [Community Identity and User Engagement in a Multi-Community Landscape](https://mlsanity.com/paper/1705.09665/) (2017)
- [Loyalty in Online Communities](https://mlsanity.com/paper/1703.03386/) (2017)
- [The anatomy of Reddit: An overview of academic research](https://mlsanity.com/paper/1810.10881/) (2018)
- [From Reddit to Wall Street: The role of committed minorities in financial collective action](https://mlsanity.com/paper/2107.07361/) (2021)
- [Extracting Inter-community Conflicts in Reddit](https://mlsanity.com/paper/1808.04405/) (2018)
- [Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit](https://mlsanity.com/paper/2005.01790/) (2020)
- [Latent Structure in Collaboration: the Case of Reddit r/place](https://mlsanity.com/paper/1804.05962/) (2018)
- [How Community Feedback Shapes User Behavior](https://mlsanity.com/paper/1405.1429/) (2014)
- [Predicting User-Interactions on Reddit](https://mlsanity.com/paper/1707.00195/) (2017)
- [The Effect of Social Feedback in a Reddit Weight Loss Community](https://mlsanity.com/paper/1602.07936/) (2016)

## Built on by (most cited first, 1 of 1)

- [With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns](https://mlsanity.com/paper/2307.09650/) (2023)

## Abstract (arXiv)

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 rp~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 four years later, in the GameStop short squeeze campaign of 2021.