Patrick Crutchley


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Individual Differences in the Movement-Mood Relationship in Digital Life Data
Glen Coppersmith | Alex Fine | Patrick Crutchley | Joshua Carroll
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood, and wellbeing. Such “digital life data” provides crucial insight into the lives of patients outside the healthcare setting that has long been lacking, from a better understanding of mundane patterns of exercise and sleep routines to harbingers of emotional crisis. Moreover, information about individual differences and personalities is encoded in digital life data. In this paper we examine the relationship between mood and movement using linguistic and biometric data, respectively. Does increased physical activity (movement) have an effect on a person’s mood (or vice-versa)? We find that weak group-level relationships between movement and mood mask interesting and often strong relationships between the two for individuals within the group. We describe these individual differences, and argue that individual variability in the relationship between movement and mood is one of many such factors that ought be taken into account in wellbeing-focused apps and AI systems.


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Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data
Alex Fine | Patrick Crutchley | Jenny Blase | Joshua Carroll | Glen Coppersmith
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.


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In your wildest dreams: the language and psychological features of dreams
Kate Niederhoffer | Jonathan Schler | Patrick Crutchley | Kate Loveys | Glen Coppersmith
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.

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Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language
Kate Loveys | Patrick Crutchley | Emily Wyatt | Glen Coppersmith
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.

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DLATK: Differential Language Analysis ToolKit
H. Andrew Schwartz | Salvatore Giorgi | Maarten Sap | Patrick Crutchley | Lyle Ungar | Johannes Eichstaedt
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.