Not Just Depressed: Bipolar Disorder Prediction on Reddit

Sekulić, Ivan; Gjurković, Matej; Šnajder, Jan

doi:10.48550/arxiv.1811.04655

Cited by 5 publications

(10 citation statements)

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“…Self-disclosure allows various approaches to discover users who may have a mental illness on social media [4,20]. To offer gender analyses, there are two major data collecting steps in our study which are (1) BD and control data collection; and (2) Gender information retrieval.…”

Section: Gender-enriched Datasetmentioning

confidence: 99%

“…Experimental Setup. The proposed syntatic patterns were performed as a normalized bag of pattern features and first compared with with two traditional baseline method: (1) TF-IDF featured model [4,5]: uses 1 and 2 grams features, where a document represents a user; and (2) LIWC featured model [5,20]: 64 categories were selected from the LIWC affect lexicon, including syntactic features, topical features (e.g., work and friends), and psychological features (e.g., emotions and social context). A random forest algorithm was applied to leverage the features with a tree size of 128 as well as a minimum number of samples required to be at a leaf node of 3.…”

Section: Recognition Performancementioning

confidence: 99%

“…To directly compare to our goals, the review below only focuses on studies on text contents. Two major text-based research methods are: (a) Dictionary approaches, such as the LIWC dictionary [14] which categorizes words in different classes based on psychological usages, were used to construct recognition models for mental illnesses [5,6,20]. However, such a pre-defined dictionary requires expensive manual effort for maintenance and needs constant renewal; and (b) Character language models (CLMs), the data-driven keywords/phrases methods, were also proposed for recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences

Huang,

Chen,

Alvarado

et al. 2019

Preprint

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Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 male). We construct a set of syntactic patterns in terms of the word usage based on graph pattern construction and pattern attention mechanism. The factors examined are gender differences, syntactic patterns, and bipolar recognition performance. The performance indicates our F1 scores reach over 91% and outperform several baselines, including those using TF-IDF, LIWC and pretrained language models (ELMO and BERT). The contributions of the present study are: (1) The features are contextualized, domainagnostic, and purely linguistic. (2) The performance of BD recognition is improved by gender-enriched linguistic pattern features, which are constructed with gender differences in language usage.

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Section: Gender-enriched Datasetmentioning

confidence: 99%

Section: Recognition Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences

Huang,

Chen,

Alvarado

et al. 2019

Preprint

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“…People are also more likely to talk about suicide after an attempt than before it. Sekulić et al (2018) indicate that users diagnosed with bipolar disorders use more firstperson singular pronouns, same as depressed people. They also use more words associated with emotions; words associated with positive emotions as well as words associated with negative emotions explained by alternating episodes of mania and depression.…”

Section: Introductionmentioning

confidence: 95%

Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores

Bucur¹,

Dinu²

2020

Preprint

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English. Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process. 1 Recent studies for depression detection from text are reviewed by Guntuku et al.

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“…Coppersmith et al [7] showed that several mental disorders (e.g., anxiety, eating disorders, and schizophrenia) can be detected in Twitter messages by using character n-gram language models (CLMs). Previous mental health studies have largely focused on several specific mental disorders, such as depression [8], post-traumatic stress disorder (PTSD) [4], suicide [9], anxiety [10], schizophrenia [11], and bipolar disorder [12].…”

Section: Introductionmentioning

confidence: 99%

Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

et al. 2022

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In this study, we aim to detect in social media texts written in Hebrew girls who are suspected of being anorexic. We constructed a dataset containing 100 blog posts written by females who are probably anorexic, and 100 blog posts written by females who are likely to be non-anorexic. The construction of this dataset was supervised and approved by an international expert on anorexia. We tested several text classification (TC) methods, using various feature sets (content-based and style-based), five machine learning (ML) methods, three RNN models, four BERT models, three basic preprocessing methods, three feature filtering methods, and parameter tuning. Several insights were found as follows. A set of 50-word n-grams (mostly word unigrams) given by an expert was found as a good basic detector. A heuristic process based on the random forest ML method has overcome a combinatorial explosion and led to significant improvement over a baseline result at a level of P = .01. Application of an iterative process that tests combinations of ''k out of n '' where n < n (n is the number of feature sets) lead to a result of 90.63%, using a combination of 300 features from ten feature sets.

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Not Just Depressed: Bipolar Disorder Prediction on Reddit

Cited by 5 publications

References 0 publications

Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences

Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences

Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores

Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

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