Molly Ireland

Also published as: Molly E. Ireland


2019

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Depressed Individuals Use Negative Self-Focused Language When Recalling Recent Interactions with Close Romantic Partners but Not Family or Friends
Taleen Nalabandian | Molly Ireland
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Depression is characterized by a self-focused negative attentional bias, which is often reflected in everyday language use. In a prospective writing study, we explored whether the association between depressive symptoms and negative, self-focused language varies across social contexts. College students (N = 243) wrote about a recent interaction with a person they care deeply about. Depression symptoms positively correlated with negative emotion words and first-person singular pronouns (or negative self-focus) when writing about a recent interaction with romantic partners or, to a lesser extent, friends, but not family members. The pattern of results was more pronounced when participants perceived greater self-other overlap (i.e., interpersonal closeness) with their romantic partner. Findings regarding how the linguistic profile of depression differs by type of relationship may inform more effective methods of clinical diagnosis and treatment.

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Dictionaries and Decision Trees for the 2019 CLPsych Shared Task
Micah Iserman | Taleen Nalabandian | Molly Ireland
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

In this summary, we discuss our approach to the CLPsych Shared Task and its initial results. For our predictions in each task, we used a recursive partitioning algorithm (decision trees) to select from our set of features, which were primarily dictionary scores and counts of individual words. We focused primarily on Task A, which aimed to predict suicide risk, as rated by a team of expert clinicians (Shing et al., 2018), based on language used in SuicideWatch posts on Reddit. Category-level findings highlight the potential importance of social and moral language categories. Word-level correlates of risk levels underline the value of fine-grained data-driven approaches, revealing both theory-consistent and potentially novel correlates of suicide risk that may motivate future research.

2018

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An Approach to the CLPsych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection
Micah Iserman | Molly Ireland | Andrew Littlefield | Tyler Davis | Sage Maliepaard
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

The Computational Linguistics and Clinical Psychology (CLPsych) 2018 Shared Task asked teams to predict cross-sectional indices of anxiety and distress, and longitudinal indices of psychological distress from a subsample of the National Child Development Study, started in the United Kingdom in 1958. Teams aimed to predict mental health outcomes from essays written by 11-year-olds about what they believed their lives would be like at age 25. In the hopes of producing results that could be easily disseminated and applied, we used largely theory-based dictionaries to process the texts, and a simple data-driven approach to model selection. This approach yielded only modest results in terms of out-of-sample accuracy, but most of the category-level findings are interpretable and consistent with existing literature on psychological distress, anxiety, and depression.

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Within and Between-Person Differences in Language Used Across Anxiety Support and Neutral Reddit Communities
Molly Ireland | Micah Iserman
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Although many studies have distinguished between the social media language use of people who do and do not have a mental health condition, within-person context-sensitive comparisons (for example, analyzing individuals’ language use when seeking support or discussing neutral topics) are less common. Two dictionary-based analyses of Reddit communities compared (1) anxious individuals’ comments in anxiety support communities (e.g., /r/PanicParty) with the same users’ comments in neutral communities (e.g., /r/todayilearned), and, (2) within popular neutral communities, comments by members of anxiety subreddits with comments by other users. Each comparison yielded theory-consistent effects as well as unexpected results that suggest novel hypotheses to be tested in the future. Results have relevance for improving researchers’ and practitioners’ ability to unobtrusively assess anxiety symptoms in conversations that are not explicitly about mental health.

2017

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Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality
Kristy Hollingshead | Molly E. Ireland | Kate Loveys
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

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A Dictionary-Based Comparison of Autobiographies by People and Murderous Monsters
Micah Iserman | Molly Ireland
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

People typically assume that killers are mentally ill or fundamentally different from the rest of humanity. Similarly, people often associate mental health conditions (such as schizophrenia or autism) with violence and otherness - treatable perhaps, but not empathically understandable. We take a dictionary approach to explore word use in a set of autobiographies, comparing the narratives of 2 killers (Adolf Hitler and Elliot Rodger) and 39 non-killers. Although results suggest several dimensions that differentiate these autobiographies - such as sentiment, temporal orientation, and references to death - they appear to reflect subject matter rather than psychology per se. Additionally, the Rodger text shows roughly typical developmental arcs in its use of words relating to friends, family, sex, and affect. From these data, we discuss the challenges of understanding killers and people in general.

2016

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The Clinical Panel: Leveraging Psychological Expertise During NLP Research
Glen Coppersmith | Kristy Hollingshead | H. Andrew Schwartz | Molly Ireland | Rebecca Resnik | Kate Loveys | April Foreman | Loring Ingraham
Proceedings of the First Workshop on NLP and Computational Social Science