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Automatic detection of eating disorder-related social media posts that could benefit from a mental health intervention.

ABSTRACT: OBJECTIVE:Online forums allow people to semi-anonymously discuss their struggles, often leading to greater honesty. This characteristic makes forums valuable for identifying users in need of immediate help from mental health professionals. Because it would be impractical to manually review every post on a forum to identify users in need of urgent help, there may be value to developing algorithms for automatically detecting posts reflecting a heightened risk of imminent plans to engage in disordered behaviors. METHOD:Five natural language processing techniques (tools to perform computational text analysis) were used on a data?set of 4,812 posts obtained from six eating disorder-related subreddits. Two licensed clinical psychologists labeled 53 of these posts, deciding whether or not the content of the post indicated that its author needed immediate professional help. The remaining 4,759 posts were unlabeled. RESULTS:Each of the five techniques ranked the 50 posts most likely to be intervention-worthy (the "top-50"). The two most accurate detection techniques had an error rate of 4% for their respective top-50. DISCUSSION:This article demonstrates the feasibility of automatically detecting-with only a few dozen labeled examples-the posts of individuals in need of immediate mental health support for an eating disorder.

SUBMITTER: Yan H

PROVIDER: S-EPMC6790146 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Automatic detection of eating disorder-related social media posts that could benefit from a mental health intervention.

Yan Hao H Fitzsimmons-Craft Ellen E EE Goodman Micah M Krauss Melissa M Das Sanmay S Cavazos-Rehg Patricia P

The International journal of eating disorders 20190805 10

<h4>Objective</h4>Online forums allow people to semi-anonymously discuss their struggles, often leading to greater honesty. This characteristic makes forums valuable for identifying users in need of immediate help from mental health professionals. Because it would be impractical to manually review every post on a forum to identify users in need of urgent help, there may be value to developing algorithms for automatically detecting posts reflecting a heightened risk of imminent plans to engage in ...[more]

PMID: 31381168

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Project description:BackgroundA growing body of evidence is suggesting a significant association between the COVID-19 pandemic and population-level mental health. Study findings suggest that individuals with a lifetime history of disordered eating behavior may be negatively affected by COVID-19-related anxiety, and prevention measures may disrupt daily functioning and limit access to treatment. However, data describing the influence of the COVID-19 pandemic on disordered eating behaviors are limited, and most findings focus on individuals in treatment settings.ObjectiveThe aim of this study is to characterize the experiences of Reddit users worldwide who post in eating disorder (ED)-related discussion forums describing the influence of the COVID-19 pandemic on their overall mental health and disordered eating behavior.MethodsData were collected from popular subreddits acknowledging EDs as their primary discussion topic. Unique discussion posts dated from January 1 to May 31, 2020 that referenced the COVID-19 pandemic were extracted and evaluated using inductive, thematic data analysis.ResultsSix primary themes were identified: change in ED symptoms, change in exercise routine, impact of quarantine on daily life, emotional well-being, help-seeking behavior, and associated risks and health outcomes. The majority of users reported that the COVID-19 pandemic and associated public health prevention measures negatively impacted their psychiatric health and contributed to increased disordered eating behaviors. Feelings of isolation, frustration, and anxiety were common. Many individuals used Reddit forums to share personal experiences, seek advice, and offer shared accountability.ConclusionsReddit discussion forums have provided a therapeutic community for individuals to share experiences and provide support for peers with ED during a period of increased psychiatric distress. Future research is needed to assess the impact of the COVID-19 pandemic on disordered eating behavior and to evaluate the role of social media discussion forums in mental health treatment, especially during periods of limited treatment access.

Project description:BackgroundPatient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients' experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).ResultsWe found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.ConclusionThe main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Project description:BACKGROUND:Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. OBJECTIVE:This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. METHODS:We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. RESULTS:The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. CONCLUSIONS:In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.

Dataset Information

Automatic detection of eating disorder-related social media posts that could benefit from a mental health intervention.

Publications

Automatic detection of eating disorder-related social media posts that could benefit from a mental health intervention.

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