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Ryan J. Gallagher
Researcher at Northeastern University
Publications - 15
Citations - 439
Ryan J. Gallagher is an academic researcher from Northeastern University. The author has contributed to research in topics: Social media & Public opinion. The author has an hindex of 7, co-authored 15 publications receiving 210 citations. Previous affiliations of Ryan J. Gallagher include University of Vermont & University of Southern California.
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Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter.
TL;DR: The authors study how these protests and counter-protests diverge by quantifying aspects of their discourse and find that #AllLivesMatter facilitates opposition between #BlackLives Matter and hashtags such as #PoliceLives Matter and #BlueLives matter in such a way that historically echoes the tension between Black protesters and law enforcement.
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Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
TL;DR: Correlation Explanation is introduced, an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework that generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions.
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Reclaiming Stigmatized Narratives: The Networked Disclosure Landscape of #MeToo
TL;DR: This work illustrates how feminist hashtag activism, like #MeToo, can unify individual and collective narratives to dismantle the stigma surrounding disclosures of sexual violence.
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Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts
Ryan J. Gallagher,Morgan R. Frank,Morgan R. Frank,Morgan R. Frank,Lewis Mitchell,Aaron J. Schwartz,Aaron J. Schwartz,Andrew J. Reagan,Christopher M. Danforth,Peter Sheridan Dodds +9 more
TL;DR: Generalized word shift graphs are introduced, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average.
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A Clarified Typology of Core-Periphery Structure in Networks
TL;DR: A new typology and corresponding statistical models for characterizing the core-periphery structure of networks are introduced, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology.