Keith Harrigian


2022

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Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses
Keith Harrigian | Mark Dredze
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade. However, psychiatric conditions are dynamic; a prior depression diagnosis may no longer be indicative of an individual’s mental health, either due to treatment or other mitigating factors. We ask: to what extent are self-disclosures of mental health diagnoses actually relevant over time? We analyze recent activity from individuals who disclosed a depression diagnosis on social media over five years ago and, in turn, acquire a new understanding of how presentations of mental health status on social media manifest longitudinally. We also provide expanded evidence for the presence of personality-related biases in datasets curated using self-disclosed diagnoses. Our findings motivate three practical recommendations for improving mental health datasets curated using self-disclosed diagnoses:1) Annotate diagnosis dates and psychiatric comorbidities2) Sample control groups using propensity score matching3) Identify and remove spurious correlations introduced by selection bias

2021

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On the State of Social Media Data for Mental Health Research
Keith Harrigian | Carlos Aguirre | Mark Dredze
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.

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Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models
Eli Sherman | Keith Harrigian | Carlos Aguirre | Mark Dredze
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention. For these models to be maximally useful, it is necessary to understand how they perform on various subgroups, especially those defined in terms of protected characteristics. In this paper we study the relationship between user demographics – focusing on gender – and depression. Considering a population of Reddit users with known genders and depression statuses, we analyze the degree to which depression predictions are subject to biases along gender lines using domain-informed classifiers. We then study our models’ parameters to gain qualitative insight into the differences in posting behavior across genders.

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Gender and Racial Fairness in Depression Research using Social Media
Carlos Aguirre | Keith Harrigian | Mark Dredze
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Multiple studies have demonstrated that behaviors expressed on online social media platforms can indicate the mental health state of an individual. The widespread availability of such data has spurred interest in mental health research, using several datasets where individuals are labeled with mental health conditions. While previous research has raised concerns about possible biases in models produced from this data, no study has investigated how these biases manifest themselves with regards to demographic groups in data, such as gender and racial/ethnic groups. Here, we analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial demographic groups. We find that model performance differs for underrepresented groups, and we investigate sources of these biases beyond data representation. Our study results in recommendations on how to avoid these biases in future research.

2020

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Do Models of Mental Health Based on Social Media Data Generalize?
Keith Harrigian | Carlos Aguirre | Mark Dredze
Findings of the Association for Computational Linguistics: EMNLP 2020

Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.

2018

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Geocoding Without Geotags: A Text-based Approach for reddit
Keith Harrigian
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate. In particular, we design a text-based heuristic schema to generate ground truth location labels for reddit users in the absence of explicitly geotagged data. After evaluating the accuracy of our labeling procedure, we train and test several geolocation inference models across our reddit data set and three benchmark Twitter geolocation data sets. Ultimately, we show that geolocation models trained and applied on the same domain substantially outperform models attempting to transfer training data across domains, even more so on reddit where platform-specific interest-group metadata can be used to improve inferences.