James Evans


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Aligning Multidimensional Worldviews and Discovering Ideological Differences
Jeremiah Milbauer | Adarsh Mathew | James Evans
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The Internet is home to thousands of communities, each with their own unique worldview and associated ideological differences. With new communities constantly emerging and serving as ideological birthplaces, battlegrounds, and bunkers, it is critical to develop a framework for understanding worldviews and ideological distinction. Most existing work, however, takes a predetermined view based on political polarization: the “right vs. left” dichotomy of U.S. politics. In reality, both political polarization – and worldviews more broadly – transcend one-dimensional difference, and deserve a more complete analysis. Extending the ability of word embedding models to capture the semantic and cultural characteristics of their training corpora, we propose a novel method for discovering the multifaceted ideological and worldview characteristics of communities. Using over 1B comments collected from the largest communities on Reddit.com representing ~40% of Reddit activity, we demonstrate the efficacy of this approach to uncover complex ideological differences across multiple axes of polarization.


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Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
Jingwei Zhang | Aaron Gerow | Jaan Altosaar | James Evans | Richard Jean So
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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The Modular Community Structure of Linguistic Predication Networks
Aaron Gerow | James Evans
Proceedings of TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing