Depression, Language, and Affect

Bernard, Jared D.; Baddeley, Jenna L.; Rodriguez, Benjamin F.; Burke, Philip A.

doi:10.1177/0261927x15589186

Cited by 43 publications

(14 citation statements)

References 20 publications

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“…However, it might be plausible to assume that depression can be seen by seeing the invisible cues embedded in the person's written language. The present result corroborates previous studies that show the significant association between depression and personal pronouns (Bernard, Baddeley, Rodriguez, & Burke, 2015;Brockmeyer, et al, 2015). In addition, the finding of this study supports the contention of Pennebaker, Mehl, and Niederhoffer (2003) which suggests that psychological states can be identified through the individuals' written words.…”

Section: The Relationship Between Liwc Dimensions and Depression With Suicide Ideationsupporting

confidence: 92%

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

Robinson¹,

Lumontod²

2020

IJRSE

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Depression remains one of the leading problems around the world. Partly, the problem stems from the fact that depression is still difficult to diagnose using traditional assessment tools. A few pieces of evidence suggest that text analysis is capable of identifying psychological states and mental issues including depression. However, this method has not been tested in the Philippines. The main purpose of this study, therefore, was to determine clues of depression and suicidal ideation in college students' writing using the online version of LIWC, a computerized text analysis. The present study was conducted at Central Luzon State University, Philippines where 159 undergraduate students participated. Using correlational analysis, several important findings were found. First, college students with a high level of depression and suicidal ideation tend to write more personal pronouns such as "I", "me", and "my" in their writing. Second, LIWC traditional dimensions such as I-words and negative emotions show a significant link with depression but only the I-words dimension was significantly associated with suicidal ideation. Third, LIWC summary variables such as clout and emotional tone also show a significant relationship with depression and suicidal ideation. Furthermore, regression analysis suggests that these LIWC domains were significant predictors of depression. However, only emotional tone was found to have a modest but significant influence on suicidal ideation. Lastly, the length of the essay was significantly correlated with depression but not with suicidal ideation. The theoretical and practical implications of the findings are discussed.

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Section: The Relationship Between Liwc Dimensions and Depression With Suicide Ideationsupporting

confidence: 92%

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

Robinson¹,

Lumontod²

2020

IJRSE

View full text Add to dashboard Cite

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“…In our analysis, the presence of negation words is more frequent in the depressed users (34.15%; 48,137/140,946) and depressive tweets (45.50%; 455/1000) datasets than in the control dataset (21.74%; 154,953/712,588), indicating that there is an increased use of negatively focused emotion language, which is typical in depressive patients and feelings [31,54,55,60].…”

Section: Discussionmentioning

confidence: 88%

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

Leis¹,

Ronzano²,

Mayer³

et al. 2019

J Med Internet Res

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Background Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people’s daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. Objective The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression. Methods This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out. Results In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 ( P <.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users ( P <.001). By contrast, the use of verbs is more common in the depressive users dataset ( P <.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset ( P <.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset ( P <.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset ( P <.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P <.001). Conclusions Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be ...

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“…Within this context, a number of works have been published on the "linguistic profile" of various clinical populations [2][3][4][5]: linguistic deficits have been reported in several neurodegenerative diseases such as dementia [6,7], where language disruption is a common finding both in the earliest stages and in full-blown pathology; alterations have been extensively described in scientific literature on dysphonia and dysarthria, especially in the hypokinetic forms resulting from damage to the basal ganglia (such as in Huntington's disease, Progressive Supranuclear Palsy or Parkinsonism) [8][9][10][11]. Some studies deal with the linguistic habits of psychopathologies, e.g., schizophrenia [12][13][14], personality disorder [15], anxiety and depression [16][17][18][19][20][21][22]. However, a very limited number of papers have been devoted to linguistic changes in patients with eating disorders [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Linguistic feature of anorexia nervosa: a prospective case–control pilot study

et al. 2021

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Purpose Attention has recently been paid to Clinical Linguistics for the detection and support of clinical conditions. Many works have been published on the “linguistic profile” of various clinical populations, but very few papers have been devoted to linguistic changes in patients with eating disorders. Patients with Anorexia Nervosa (AN) share similar psychological features such as disturbances in self-perceived body image, inflexible and obsessive thinking and anxious or depressive traits. We hypothesize that these characteristics can result in altered linguistic patterns and be detected using the Natural Language Processing tools. Methods We enrolled 51 young participants from December 2019 to February 2020 (age range: 14–18): 17 girls with a clinical diagnosis of AN, and 34 normal-weighted peers, matched by gender, age and educational level. Participants in each group were asked to produce three written texts (around 10–15 lines long). A rich set of linguistic features was extracted from the text samples and the statistical significance in pinpointing the pathological process was measured. Results Comparison between the two groups showed several linguistics indexes as statistically significant, with syntactic reduction as the most relevant trait of AN productions. In particular, the following features emerge as statistically significant in distinguishing AN girls and their normal-weighted peers: the length of the sentences, the complexity of the noun phrase, and the global syntactic complexity. This peculiar pattern of linguistic erosion may be due to the severe metabolic impairment also affecting the central nervous system in AN. Conclusion These preliminary data showed the existence of linguistic parameters as probable linguistic markers of AN. However, the analysis of a bigger cohort, still ongoing, is needed to consolidate this assumption. Level of evidence III Evidence obtained from case–control analytic studies.

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Depression, Language, and Affect

Cited by 43 publications

References 20 publications

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

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

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

Linguistic feature of anorexia nervosa: a prospective case–control pilot study

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