Seeing the invisible: Extracting signs of depression and suicidal ideation from college students’ writing using LIWC a computerized text analysis

Robinson, Z; Lumontod, Iii

doi:10.5861/ijrse.2020.5007

Cited by 15 publications

(9 citation statements)

References 38 publications

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“…In terms of consistency with the prior literature, negative emotions 28,34,35,[40][41][42] , 1 st person singular pronouns 27,28,30,35,36,40,41 , swear words 34,36,37,39,40,43 , and negations 36,37,40,44 were shown to be positively associated with depression severity. While, positive emotions 31,36,40,43,45,46 , 1 st person plural 28,36,41 , 2 nd person 36,40,44 , and 3 rd person pronouns 36,40,41,44,47 have been found to be negatively associated with depression. Article use has been found to be significantly associated with depression severity, although there is inconsistency regarding the direction of the effect 28,36,37,39,40 .…”

Section: Resultsmentioning

confidence: 97%

Using language in social media posts to study the network dynamics of depression longitudinally

Kelley

Gillan

2022

Nat Commun

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Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. We observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.

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Section: Resultsmentioning

confidence: 97%

Using language in social media posts to study the network dynamics of depression longitudinally

Kelley

Gillan

2022

Nat Commun

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“…Less use of first-person singular suggests that users might experience less thus express less personal physical or emotional pain. There was a decrease in the use of death words meaning less expression of suicidal thoughts and attempts ( Lumontod and Robinson, 2020 ). Emotions reflect the hedonistic aspect of well-being ( Houben et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

“…We segment and tokenize users’ posts and comments for content analysis with Butter 6 and feed the data into the Chinese version dictionary of Linguistic Inquiry Word (SC-LIWC), 7 a validated and well-adopted toolkit for psychometric analysis in mental expression research ( Lieberman, 2007 ; Pennebaker et al, 2015 ; Xu and Zhang, 2016 ; Lumontod and Robinson, 2020 ). LIWC classifies the input words into four categories: linguistic processes (e.g., pronouns, adverbs), psychological processes (e.g., emotions and cognitive process), personal concerns (e.g., biological concerns, death), and spoken (everyday language use).…”

Section: Methodsmentioning

confidence: 99%

Online health community for change: Analysis of self-disclosure and social networks of users with depression

Khoo

2023

Front. Psychol.

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BackgroundA key research question with theoretical and practical implications is to investigate the various conditions by which social network sites (SNS) may either enhance or interfere with mental well-being, given the omnipresence of SNS and their dual effects on well-being.Method/processWe study SNS’ effects on well-being by accounting for users’ personal (i.e., self-disclosure) and situational (i.e., social networks) attributes, using a mixed design of content analysis and social network analysis.Result/conclusionWe compare users’ within-person changes in self-disclosure and social networks in two phases (over half a year), drawing on Weibo Depression SuperTalk, an online community for depression, and find: ① Several network attributes strengthen social support, including network connectivity, global efficiency, degree centralization, hubs of communities, and reciprocal interactions. ② Users’ self-disclosure attributes reflect positive changes in mental well-being and increased attachment to the community. ③ Correlations exist between users’ topological and self-disclosure attributes. ④ A Poisson regression model extracts self-disclosure attributes that may affect users’ received social support, including the writing length, number of active days, informal words, adverbs, negative emotion words, biological process words, and first-person singular forms.InnovationWe combine social network analysis with content analysis, highlighting the need to understand SNS’ effects on well-being by accounting for users’ self-disclosure (content) and communication partners (social networks).Implication/contributionAuthentic user data helps to avoid recall bias commonly found in self-reported data. A longitudinal within-person analysis of SNS’ effects on well-being is helpful for policymakers in public health intervention, community managers for group organizations, and users in online community engagement.

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“…In the last several years, a considerable amount of research also studied the relationship between mental health and language use in order to acquire new insights into how suicidal thoughts may be identified and how they might be prevented. For the aim of this study, linguistic characteristics that are established in the field of psychiatry, such as the LIWC [15], emotion features [16], and suicide notes [17], were used. However, this method employs language-specific strategies that can evaluate only individual posts in isolation and cannot perform well with vast amounts of diverse data.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models

Aldhyani

Alsubari

Alshebami

et al. 2022

IJERPH

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Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comprehending patterns of suicidal ideation represent a challenging task. Therefore, it is essential to develop a machine learning system for automated early detection of suicidal ideation or any abrupt changes in a user’s behavior by analyzing his or her posts on social media. In this paper, we propose a methodology based on experimental research for building a suicidal ideation detection system using publicly available Reddit datasets, word-embedding approaches, such as TF-IDF and Word2Vec, for text representation, and hybrid deep learning and machine learning algorithms for classification. A convolutional neural network and Bidirectional long short-term memory (CNN–BiLSTM) model and the machine learning XGBoost model were used to classify social posts as suicidal or non-suicidal using textual and LIWC-22-based features by conducting two experiments. To assess the models’ performance, we used the standard metrics of accuracy, precision, recall, and F1-scores. A comparison of the test results showed that when using textual features, the CNN–BiLSTM model outperformed the XGBoost model, achieving 95% suicidal ideation detection accuracy, compared with the latter’s 91.5% accuracy. Conversely, when using LIWC features, XGBoost showed better performance than CNN–BiLSTM.

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Seeing the invisible: Extracting signs of depression and suicidal ideation from college students’ writing using LIWC a computerized text analysis

Cited by 15 publications

References 38 publications

Using language in social media posts to study the network dynamics of depression longitudinally

Using language in social media posts to study the network dynamics of depression longitudinally

Online health community for change: Analysis of self-disclosure and social networks of users with depression

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models

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