Detection of Depression-Related Posts in Reddit Social Media Forum

Tadesse, Michael M.; Lin, Hongfei; Xu, Bo; Yang, Liang

doi:10.1109/access.2019.2909180

Cited by 314 publications

(172 citation statements)

References 38 publications

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“…Clout has also been described as an indication of expertise in professional settings (Newman et al ., 2016), a barometer for relative social status or leadership (O’Dea, Larsen, Batterham, Calear, & Christensen, 2017) and even a bellwether for bias (Hagiwara, Slatcher, Eggly, & Penner, 2017). The role of clout has been examined in a variety of contexts including education (Moore et al ., 2019; Oliver et al ., 2020; Smith‐Keiling & Hyun, 2019; Smith‐Keiling, Swanson, & Dehnbostel, 2018), mental health (O’Dea et al ., 2017; Tadesse, Lin, Xu, & Yang, 2019), dreams (Bulkeley & Graves, 2018), social hierarchies (Kacewicz et al ., 2014) and bias (Hagiwara et al ., 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

Moore¹,

Yen²,

Powers³

2020

Brit J Educational Tech

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The purpose of this study was to explore the relationship between clout and cognitive processing in massive open online course (MOOC) discussion forum posts. Cognitive processing, a category variable generated by the automated text analysis tool, Linguistic Inquiry Word Count (LIWC), is made up of six sub‐scores (insight, causation, discrepancy, tentativeness, certainty and differentiation). Clout is a nontransparent summary variable in LIWC that can be used to understand the level of confidence conveyed in the text. Because clout is nontransparent, we do not know the algorithm used to calculate its value. To better understand this variable, this study examined cognitive processing alongside clout. In this study, a series of linear mixed models were fitted to evaluate, after controlling for gender, degree and type of post, if the focal relationship between each sub‐score of cognitive processing as the predictor variables and clout as the dependent variable changed across courses with different pacing (self‐paced or instructor‐paced). Next, the focal relationship between each predictor and clout was examined with or without regard to pacing. Findings showed words classified as showing discrepancy, certainty or differentiation were negatively associated with clout scores.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

Moore¹,

Yen²,

Powers³

2020

Brit J Educational Tech

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“…Na literaturaé comum encontrar trabalhos que utilizem o LIWC como apoio para extrair somente a polaridade de sentimento (negativo / positivo) presente no documento em análise [Olagunju et al, 2020] [Tadesse et al, 2019]. Aprofundar o conhecimento do documento após a sua classificação de polaridade por meio da combinação de outras categorias do LIWC (que extraiam aspectos cognitivos) contribui para um melhor enten- A Tabela 4 exibe o resultado das categorias do LIWC que trazem um aspecto cognitivo referente aos documentos com polaridade negativa do sistema PJe.…”

Section: Resultsunclassified

Mineração de Opiniões com LIWC: abordagem prática sobre sistemas judiciais eletrônicos brasileiros

Nascimento¹,

Nascimento²,

Carvalho³

et al. 2020

Anais Do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2020)

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A Mineração de Opinião engloba o estudo computacional de opiniões ou avaliações que as pessoas expressam em relação a entidades, produtos e serviços. O objetivo deste trabalho é avaliar e comparar opiniões, publicadas em redes sociais, de usuários de sistemas de processos judiciais eletrônicos (Projudi, e-SAJ, PJe e e-Proc). Para isso, utiliza o mais recente léxico do LIWC em Português do Brasil para obter valores referentes a componentes psicolinguísticos, observando emoções e aspectos cognitivos presentes nos textos. Resultados preliminares indicam que a metodologia adotada pode trazer informações relevantes sobre a percepção dos usuários acerca desses sistemas, podendo ser adotada no direcionamento de ações para aperfeiçoá-los.

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“…• Unigram: Tadesse et al [4] extracted 3000 dimensional characteristics based on unigram in term frequency-inverse document frequency (TF-IDF) for depression detection. • LSTM: LSTM was proposed by Hochreiter and Schmidhuber [33].…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Bigram: Tadesse et al[4] extracted 2736 dimensional characteristics based on bigram in TF-IDF for depression detection. JMIR Med Inform 2021 | vol.…”

mentioning

confidence: 99%

Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

Ren¹,

Lin²,

Xu³

et al. 2021

JMIR Med Inform

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Background As a common mental disease, depression seriously affects people’s physical and mental health. According to the statistics of the World Health Organization, depression is one of the main reasons for suicide and self-harm events in the world. Therefore, strengthening depression detection can effectively reduce the occurrence of suicide or self-harm events so as to save more people and families. With the development of computer technology, some researchers are trying to apply natural language processing techniques to detect people who are depressed automatically. Many existing feature engineering methods for depression detection are based on emotional characteristics, but these methods do not consider high-level emotional semantic information. The current deep learning methods for depression detection cannot accurately extract effective emotional semantic information. Objective In this paper, we propose an emotion-based attention network, including a semantic understanding network and an emotion understanding network, which can capture the high-level emotional semantic information effectively to improve the depression detection task. Methods The semantic understanding network module is used to capture the contextual semantic information. The emotion understanding network module is used to capture the emotional semantic information. There are two units in the emotion understanding network module, including a positive emotion understanding unit and a negative emotion understanding unit, which are used to capture the positive emotional information and the negative emotional information, respectively. We further proposed a dynamic fusion strategy in the emotion understanding network module to fuse the positive emotional information and the negative emotional information. Results We evaluated our method on the Reddit data set. The experimental results showed that the proposed emotion-based attention network model achieved an accuracy, precision, recall, and F-measure of 91.30%, 91.91%, 96.15%, and 93.98%, respectively, which are comparable results compared with state-of-the-art methods. Conclusions The experimental results showed that our model is competitive with the state-of-the-art models. The semantic understanding network module, the emotion understanding network module, and the dynamic fusion strategy are effective modules for depression detection. In addition, the experimental results verified that the emotional semantic information was effective in depression detection.

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Detection of Depression-Related Posts in Reddit Social Media Forum

Cited by 314 publications

References 38 publications

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

Mineração de Opiniões com LIWC: abordagem prática sobre sistemas judiciais eletrônicos brasileiros

Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

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