Christopher Homan


2018

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Sensing and Learning Human Annotators Engaged in Narrative Sensemaking
McKenna Tornblad | Luke Lapresi | Christopher Homan | Raymond Ptucha | Cecilia Ovesdotter Alm
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

While labor issues and quality assurance in crowdwork are increasingly studied, how annotators make sense of texts and how they are personally impacted by doing so are not. We study these questions via a narrative-sorting annotation task, where carefully selected (by sequentiality, topic, emotional content, and length) collections of tweets serve as examples of everyday storytelling. As readers process these narratives, we measure their facial expressions, galvanic skin response, and self-reported reactions. From the perspective of annotator well-being, a reassuring outcome was that the sorting task did not cause a measurable stress response, however readers reacted to humor. In terms of sensemaking, readers were more confident when sorting sequential, target-topical, and highly emotional tweets. As crowdsourcing becomes more common, this research sheds light onto the perceptive capabilities and emotional impact of human readers.

2017

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Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions
Alexander Calderwood | Elizabeth A. Pruett | Raymond Ptucha | Christopher Homan | Cecilia Ovesdotter Alm
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

Interpersonal violence (IPV) is a prominent sociological problem that affects people of all demographic backgrounds. By analyzing how readers interpret, perceive, and react to experiences narrated in social media posts, we explore an understudied source for discourse about abuse. We asked readers to annotate Reddit posts about relationships with vs. without IPV for stakeholder roles and emotion, while measuring their galvanic skin response (GSR), pulse, and facial expression. We map annotations to coreference resolution output to obtain a labeled coreference chain for stakeholders in texts, and apply automated semantic role labeling for analyzing IPV discourse. Findings provide insights into how readers process roles and emotion in narratives. For example, abusers tend to be linked with violent actions and certain affect states. We train classifiers to predict stakeholder categories of coreference chains. We also find that subjects’ GSR noticeably changed for IPV texts, suggesting that co-collected measurement-based data about annotators can be used to support text annotation.

2016

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Analyzing Gender Bias in Student Evaluations
Andamlak Terkik | Emily Prud’hommeaux | Cecilia Ovesdotter Alm | Christopher Homan | Scott Franklin
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

University students in the United States are routinely asked to provide feedback on the quality of the instruction they have received. Such feedback is widely used by university administrators to evaluate teaching ability, despite growing evidence that students assign lower numerical scores to women and people of color, regardless of the actual quality of instruction. In this paper, we analyze students’ written comments on faculty evaluation forms spanning eight years and five STEM disciplines in order to determine whether open-ended comments reflect these same biases. First, we apply sentiment analysis techniques to the corpus of comments to determine the overall affect of each comment. We then use this information, in combination with other features, to explore whether there is bias in how students describe their instructors. We show that while the gender of the evaluated instructor does not seem to affect students’ expressed level of overall satisfaction with their instruction, it does strongly influence the language that they use to describe their instructors and their experience in class.

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Understanding Discourse on Work and Job-Related Well-Being in Public Social Media
Tong Liu | Christopher Homan | Cecilia Ovesdotter Alm | Megan Lytle | Ann Marie White | Henry Kautz
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Generating Clinically Relevant Texts: A Case Study on Life-Changing Events
Mayuresh Oak | Anil Behera | Titus Thomas | Cecilia Ovesdotter Alm | Emily Prud’hommeaux | Christopher Homan | Raymond Ptucha
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

2015

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An Analysis of Domestic Abuse Discourse on Reddit
Nicolas Schrading | Cecilia Ovesdotter Alm | Ray Ptucha | Christopher Homan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse
Nicolas Schrading | Cecilia Ovesdotter Alm | Raymond Ptucha | Christopher Homan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale
Christopher Homan | Ravdeep Johar | Tong Liu | Megan Lytle | Vincent Silenzio | Cecilia Ovesdotter Alm
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality