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Papers by Joshua Carroll
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, 2021
Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood... more 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 viceversa)? 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.
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020
Prevailing methods for assessing populationlevel mental health require costly collection of large... more Prevailing methods for assessing populationlevel 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 populationlevel 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.
A principal mechanism by which the SEC fulfills its missions of investor protection and market ef... more A principal mechanism by which the SEC fulfills its missions of investor protection and market efficiency is the widespread dissemination of the information that publicly traded firms submit for disclosure. The continuing evolution of reporting standards like the International Financial Reporting Standards (IFRS) and the global convergence on XBRL as a syntax for sharing data address the quantitative side of the equation. This work complements the ongoing research on financial disclosure by helping investors learn from the textual, narrative portions of the filing. In structured retrieval, terms are differentially weighted based upon the document segments in which a term appears. Our objective is to automatically segment SEC 10-K financial regulatory filings to facilitate structured retrieval and querying. We leverage the regulatory instructions provided by the SEC to identify a set of semantic labels such as "Legal Proceedings" or "Management's Discussion and An...
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, 2021
Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood... more 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 viceversa)? 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.
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020
Prevailing methods for assessing populationlevel mental health require costly collection of large... more Prevailing methods for assessing populationlevel 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 populationlevel 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.
A principal mechanism by which the SEC fulfills its missions of investor protection and market ef... more A principal mechanism by which the SEC fulfills its missions of investor protection and market efficiency is the widespread dissemination of the information that publicly traded firms submit for disclosure. The continuing evolution of reporting standards like the International Financial Reporting Standards (IFRS) and the global convergence on XBRL as a syntax for sharing data address the quantitative side of the equation. This work complements the ongoing research on financial disclosure by helping investors learn from the textual, narrative portions of the filing. In structured retrieval, terms are differentially weighted based upon the document segments in which a term appears. Our objective is to automatically segment SEC 10-K financial regulatory filings to facilitate structured retrieval and querying. We leverage the regulatory instructions provided by the SEC to identify a set of semantic labels such as "Legal Proceedings" or "Management's Discussion and An...