Crosslingual Depression Detection in Twitter Using Bilingual Word Alignments

Coello-Guilarte, Laritza; Ortega-Mendoza, Rosa María; Villaseñor-Pineda, Luis; Montes-y-Gómez, Manuel

doi:10.1007/978-3-030-28577-7_2

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…From the descriptions of the 119 data sets included, we found that 57.1% (68/119) of the data sets included the number of users in the data set, 79.8% (95/119) included how many tweets were in the data set, 55.5% (66/119) included the period over which the data were collected from Twitter, 69.7% (83/119) included which API or tool was used to access the Twitter data, and 90.8% (108/119) included the search strategy they used to query the API. The smallest described data set was that of Coello-Guilarte et al [ 52 ] with 200 annotated tweets, and the largest was that of Shen et al [ 53 ] with >300 million tweets from users they determined to be depressed and 10 billion control tweets.…”

Section: Resultsmentioning

confidence: 99%

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Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Cara¹,

Maggio²,

Davis³

et al. 2023

J Med Internet Res

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Background The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results. Objective This study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used. Methods A systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations. Results The 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention. Conclusions The sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs.

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Section: Resultsmentioning

confidence: 99%

“…In most cases, data sets were created specifically for the task the study was focused on. These included data sets of tweets in other languages, such as Spanish [ 52 ], Bengali [ 75 ], Japanese [ 51 ], and Arabic [ 76 ], as well as English, which was the most common language studied.…”

Section: Resultsmentioning

confidence: 99%

Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Cara¹,

Maggio²,

Davis³

et al. 2023

J Med Internet Res

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“…To our knowledge, the works closest to ours are the ones presented in [39,40]. These papers present a methodology for detecting depression based on the construction of linear transformations that are capable of aligning words in different languages.…”

Section: Related Workmentioning

confidence: 99%

“…Among the main differences from our work are the type of transformation used and the inclusion of attention mechanisms to maintain semantic properties. While in [39,40], the mapping is only between words, our methodology used knowledge distillation to find more complex mapping functions, while incorporating semantic properties.…”

Section: Related Workmentioning

confidence: 99%

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation

Pool-Cen¹,

Carlos

Hernández-Chan

et al. 2023

Healthcare

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Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.

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“…Likewise, emotional tone, pronoun rates, words related to sadness, health, and biology, and behavior activation-related LIWC categories, respectively, appear to be complementary. Another study ( Coello-Guilarte et al, 2019 ) managed to reasonably identify depression with LIWC even in the cross-linguistic context, i.e., by using an automatic translation of texts and bilingual dictionaries. Kimball et al (2019) proved that LIWC is a sensitive tool in screening for anxiety and depression in tinnitus patients, even when the self-assessment fails to indicate relevant levels of anxiety and depression symptoms.…”

Section: Introductionmentioning

confidence: 99%

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

Trifu,

Nemeș,

Herta

et al. 2024

Front. Psychol.

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IntroductionThe identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts.AimThis study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count).Materials and methodsA sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them.Assessment tools(1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 – Linguistic Inquiry and Word Count – a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use.ResultsDepressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form – not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money.ConclusionDepressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.

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Crosslingual Depression Detection in Twitter Using Bilingual Word Alignments

Cited by 10 publications

References 14 publications

Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Methodologies for Monitoring Mental Health on Twitter: Systematic Review

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

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