Investigating User-Generated Content in an Online Drug Recovery Forum: Lessons for Successful Computer-Mediated Communication of Social Support

Ansari, Bahareh; Du, Shichao

doi:10.1109/jbhi.2022.3196631

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…LIWC 2015 “all fields” search key criteria indicates 54 studies, 16 of which with specific focus on LIWC-2015. From these, 7 are of interest for health and education ( Stanton et al, 2017 ; Chippendale and Gentile, 2021 ; Monzani et al, 2021 ; Ansari and Du, 2022 ; Yang et al, 2022 ; Sengun et al, 2023 ; Wu et al, 2023 ). Moreover, 7 studies ( Toma and D’Angelo, 2015 ; Coello-Guilarte et al, 2019 ; Kimball et al, 2019 ; Berkout et al, 2020 ; McDonnell et al, 2020 ; Dudău and Sava, 2022 ; Meyerhoff et al, 2023 ) focus on the use of LIWC in connection with depression, all of them relevant for the topic of this study.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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Fusion-based Representation Learning Model for Multimode User-generated Social Network Content

Martin

Oak

Soni

et al. 2023

J. Data and Information Quality

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As mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. Due to the multi-source heterogeneous nature of the data, big data fusion offers both promise and drawbacks. With the rise of mobile networks and applications, user-generated content (UGC), which includes multi-source heterogeneous data including ratings, marks, scores, images, and videos, has gained importance. This information is very important for improving the calibre of customized services. The key to the application's success is representational learning of fusing and vectorization on the multi-source heterogeneous UGC. Multi-source text fusion and representation learning have become the key to its application. In this regard, a fusion representation learning for multi-source text and image is proposed. The convolutional fusion technique, in contrast to splicing and fusion, may take into consideration the varied data characteristics in each size. This research proposes a new data feature fusion strategy based on the convolution operation, which was inspired by the convolutional neural network. Using Doc2vec and LDA model, the vectorized representation of multi-source text is given, and the deep convolutional network is used to obtain. Finally, the proposed algorithm is applied to Amazon's commodity data set containing UGC content based on the classification accuracy of UGC vectorized representation items and shows the feasibility and impact of the proposed algorithm.

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Investigating User-Generated Content in an Online Drug Recovery Forum: Lessons for Successful Computer-Mediated Communication of Social Support

Cited by 2 publications

References 29 publications

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

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

Fusion-based Representation Learning Model for Multimode User-generated Social Network Content

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