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The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the “distant reading” of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstraction methods complement the metrics on which research reviews currently rely. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail in the context of an institutional review of the Earth Research Institute (ERI) at the University of California at Santa Barbara. From these, we design science maps that reveal the latent thematic structure of ERI's interdisciplinary research and enable reviewers to “read” a body of research at multiple levels of detail. We find that our approach provides decision support and reveals trends that strengthen the institutional review process by exposing regions of thematic expertise, distributions and clusters of work, and the evolution of these aspects. 

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models---one supervised classifier and one unsupervised topic model---provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress. 

Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities. 

Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguish between high and low levels of future hostility with an AUC of .91 (task 2). 

In an effort to create new sociotechnical tools to combat online harassment, we developed a scale to detect and measure verbal violence within individual tweets. Unfortunately, we found that the scale, based on scales effective at detecting harassment offline, was unreliable for tweets. Here, we begin with information about the development and validation of our scale, then discuss the scale’s shortcomings for detecting harassment in tweets, and explore what we can learn from this scale’s failures. We explore how rarity, context, and individual coder’s differences create challenges for detecting verbal violence in individual tweets. We also examine differences in on- and offline harassment that limit the utility of existing harassment measures for online contexts. We close with a discussion of potential avenues for future work in automated harassment detection.