An analysis of cognitive change in online mental health communities: A textual data analysis based on post replies of support seekers

Gu, Dongxiao; Li, Min; Yang, Xuejie; Gu, Yadi; Zhao, Yu; Liang, Changyong; Liu, Hu

doi:10.1016/j.ipm.2022.103192

Cited by 24 publications

(6 citation statements)

References 37 publications

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“…After word separation, we obtained a lexicon of 68,863 words and ranked the words according to the number of occurrences. We found that the top 10,000 words accounted for 94.83% of the total words, suggesting that many words recurred and a relatively simple word vector could effectively train the model [53]. This further confirms the efficacy of our decision to use Skip-gram as the foundational model.…”

Section: Data Setsupporting

confidence: 67%

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Gu,

Wang,

Chai

et al. 2024

J Med Internet Res

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Background Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. Objective This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? Methods This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. Results Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body’s immune system and lead to the development of allergies. Conclusions Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

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Section: Data Setsupporting

confidence: 67%

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Gu,

Wang,

Chai

et al. 2024

J Med Internet Res

Self Cite

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“…In order to verify the efficiency of the proposed method, traditional machine learning models, such as SVMs [26], KNN [27], and Naive Bayes [28], and deep learning models and their variants, including CNNs [29], LSTM [30], BERT-CNN [31], LSTM-CNN [32], and LDA-Ngram-BERT-LSTM, were used for comparison.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Building Materials Classification Model Based on Text Data Enhancement and Semantic Feature Extraction

Yan,

Jiao,

Peng

2024

Buildings

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In order to accurately extract and match carbon emission factors from the Chinese textual building materials list and construct a precise carbon emission factor database, it is crucial to accurately classify the textual building materials. In this study, a novel classification model based on text data enhancement and semantic feature extraction is proposed and applied for building materials classification. Firstly, the explanatory information on the building materials is collected and normalized to construct the original dataset. Then, the Latent Dirichlet Allocation and statistical-language-model-based hybrid ensemble data enhancement methods are explained in detail, and the semantic features closely related to the carbon emission factor are extracted by constructed composite convolutional networks and the transformed word vectors. Finally, the ensemble classification model is designed, constructed, and applied to match the carbon emission factor from the textual building materials. The experimental results show that the proposed model improves the F1Macro score by 4–12% compared to traditional machine learning and deep learning models.

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“…40 Most research uses key phrase matching and word frequency to identify psychological cognitive change. 41 Collecting social media data may be more scalable than traditional approaches. 42 Also, it allows us to reach a wider audience.…”

Section: Average Sentence Lengthmentioning

confidence: 99%

Exploring the Association Between Textual Parameters and Psychological and Cognitive Factors

Uludag

2024

PRBM

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Background Textual data analysis has become a popular method for examining complex human behavior in various fields, including psychology, psychiatry, sociology, computer science, data mining, forensic sciences, and communication studies. However, identifying the most relevant textual parameters for analyzing complex behavior is still a challenge. Goal of Study This paper aims to explore potential textual parameters that could be useful in analyzing behavior through complex textual data. Furthermore, we have examined the randomly generated text based on different textual parameters. Methods To achieve this goal, we conducted a comprehensive review of the literature on textual data analysis and identified several potential topics that could be relevant, such as sentiment analysis, discourse analysis, lexical analysis, and syntactic analysis. We discuss the theoretical background and practical implications of each parameter and provide examples of how they have been used in previous research. Furthermore, we highlight the importance of considering the context in which these parameters are applied and the need for interdisciplinary collaboration to gain a deeper understanding of complex behavior through textual data analysis. Furthermore, we have provided Python code in the Supplementary Materials to facilitate a comprehensive analysis of such behaviors. In addition, to generate the text for analysis, we utilized ChatGPT 3.5 Turbo by requesting it to generate a random text of 1000 words divided into five paragraphs. Afterwards, we applied the provided Python code to analyze the randomly generated text. Conclusion Overall, this paper provides a foundation for researchers to identify relevant textual parameters to analyze complex human behavior in their respective fields such as linguistics, sociology, psychiatry, and psychology.

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An analysis of cognitive change in online mental health communities: A textual data analysis based on post replies of support seekers

Cited by 24 publications

References 37 publications

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Building Materials Classification Model Based on Text Data Enhancement and Semantic Feature Extraction

Exploring the Association Between Textual Parameters and Psychological and Cognitive Factors

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