Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

Resnik, Philip; Armstrong, William W.; Claudino, Leonardo; Nguyen, Tuan Thanh; Nguyen, Viet-An; Boyd-Graber, Jordan

doi:10.3115/v1/w15-1212

Cited by 165 publications

(136 citation statements)

References 19 publications

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“…Moreover, related work has found that differences in frequencies of part-of-speech (POS) tags were useful in detecting depression from writing (Morales and Levitan, 2016b). Resnik et al (2015) explored the use of supervised topic models in the analysis of detecting depression from Twitter. They use 3 million tweets from about 2,000 twitter users, of whom roughly 600 self-identify as having been diagnosed with depression.…”

Section: Linguistic and Social Indicatorsmentioning

confidence: 99%

A Cross-modal Review of Indicators for Depression Detection Systems

Morales¹,

Scherer²,

Levitan³

2017

Proceedings of the Fourth Workshop on Computational Linguistics And Clinical Psychology –- From Linguistic Signal to

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Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.

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Section: Linguistic and Social Indicatorsmentioning

confidence: 99%

A Cross-modal Review of Indicators for Depression Detection Systems

Morales¹,

Scherer²,

Levitan³

2017

Proceedings of the Fourth Workshop on Computational Linguistics And Clinical Psychology –- From Linguistic Signal to

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“…From this data set, Nadeem (2016) achieved 86% accuracy with a naïve Bayes unigram classifier. Resnik et al (2015) used the same data set with latent Dirichlet allocation (LDA) and supervised LDA techniques to predict the likelihood of target classes based on topics. Their supervised LDA techniques included the associated labels of documents as priors for topic modeling.…”

Section: Introductionmentioning

confidence: 99%

“…

AbstractPrevious investigations into detecting mental illnesses through social media have predominately focused on detecting depression through Twitter corpora (De Choudhury et al, 2013;Resnik et al, 2015;Pedersen, 2015). In this paper, we study anxiety disorders through personal narratives collected through the popular social media website, Reddit.

…”

mentioning

confidence: 99%

Detecting Anxiety through Reddit

Shen

Rudzicz

2017

Proceedings of the Fourth Workshop on Computational Linguistics And Clinical Psychology –- From Linguistic Signal to

174

106

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Previous investigations into detecting mental illnesses through social media have predominately focused on detecting depression through Twitter corpora (De Choudhury et al., 2013;Resnik et al., 2015;Pedersen, 2015). In this paper, we study anxiety disorders through personal narratives collected through the popular social media website, Reddit. We build a substantial data set of typical and anxietyrelated posts, and we apply N -gram language modeling, vector embeddings, topic analysis, and emotional norms to generate features that accurately classify posts related to binary levels of anxiety. We achieve an accuracy of 91% with vectorspace word embeddings, and an accuracy of 98% when combined with lexiconbased features.

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“…The use of the supervised LDA and the supervised anchor model was proven to be highly successful compared to the unsupervised clustering approaches, and even more efficient than using linguistic methods such as the use of n-grams and other lexicon based approaches (Resnik et al, 2015b). Resnik et al (2015a) proved that such approaches can be successfully used in identifying users with depression, who have self-disclosed their mental illnesses on Twitter.…”

Section: Related Workmentioning

confidence: 99%

Monitoring Tweets for Depression to Detect At-risk Users

Jamil¹,

Inkpen²,

Buddhitha³

et al. 2017

Proceedings of the Fourth Workshop on Computational Linguistics And Clinical Psychology –- From Linguistic Signal to

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We propose an automated system that can identify at-risk users from their public social media activity, more specifically, from Twitter. The data that we collected is from the #BellLetsTalk campaign, which is a wide-reaching, multi-year program designed to break the silence around mental illness and support mental health across Canada. To achieve our goal, we trained a user-level classifier that can detect atrisk users that achieves a reasonable precision and recall. We also trained a tweetlevel classifier that predicts if a tweet indicates depression. This task was much more difficult due to the imbalanced data. In the dataset that we labeled, we came across 5% depression tweets and 95% non-depression tweets. To handle this class imbalance, we used undersampling methods. The resulting classifier had high recall, but low precision. Therefore, we only use this classifier to compute the estimated percentage of depressed tweets and to add this value as a feature for the userlevel classifier.

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Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

Abstract: Topic models can yield insight into how depressed and non-depressed individuals use language differently. In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.

Cited by 165 publications

References 19 publications

A Cross-modal Review of Indicators for Depression Detection Systems

A Cross-modal Review of Indicators for Depression Detection Systems

Detecting Anxiety through Reddit

Monitoring Tweets for Depression to Detect At-risk Users

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