Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e45777 - e45777

Published: March 8, 2023

Anxiety disorder has become a major clinical and public health problem, causing significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, social activities of people with disorder.

Language: Английский

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Factors associated with nursing students' mental health-related stigma: A multisite cross-sectional study

Nurse Education Today, Journal Year: 2024, Volume and Issue: 142, P. 106346 - 106346

Published: Aug. 11, 2024

Language: Английский

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Can social media promote vaccination? Strategies and effectiveness of COVID-19 vaccine popularization on Chinese Weibo

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 4, 2024

The COVID-19 pandemic has shown a high severity in terms of mortality, and to mitigate the impact pandemic, great deal reliance been placed on vaccines with defensive effects. In context transmission hazardous Omicron variant strains, vaccine popularization acceptance are very important ensure world health security. Social media can spread information increase public confidence vaccines.

Language: Английский

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A bibliometric analysis of media coverage of mental disorders between 2002 and 2022

Social Psychiatry and Psychiatric Epidemiology, Journal Year: 2023, Volume and Issue: 58(11), P. 1719 - 1729

Published: April 11, 2023

Language: Английский

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Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: July 8, 2024

Background Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover develop classification model for predicting as whole, then externally validate its performance on population individual levels. Methods 938,866 Weibo posts with relevant keywords downloaded, including 737,849 updated between 2009 2014 ( 2009–2014 ), 201,017 2015 2020 2015–2020 ). (1) For development, based 10,000 randomly selected from , human-based content analysis was performed manually determine labels each post (non-negative or attitudes). Then, computer-based conducted automatically extract psycholinguistic features the same posts. Finally, features. (2) validation, level, implemented remaining 727,849 validated by comparing proportions predicted human-coded results. Besides, similar analyses 300 actual Results F1 area under ROC curve (AUC) values reached 0.93 0.97. significant differences but very small effect sizes observed whole sample χ 2 1 = 32.35, p < 0.001; Cramer’s V 0.007, 0.001), men 9.48, 0.002; 0.005, 0.002), women 25.34, 0.009, 0.001). AUC 0.76 0.74. Conclusion This study demonstrates efficiency necessity confirms that validation is essential before implementing into practice.

Language: Английский

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Mind the gap: Exploring differences in suicide literacy between cybersuicide and offline suicide

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 16, 2023

The highly public nature of cybersuicide contradicts long-held beliefs offline suicide, which may cause differences in the way people perceive and respond to both them. However, knowledge whether how suicide literacy differs between is limited.By analyzing social media data, this paper focused on livestreamed aimed compare three aspects, including false structure, extent association with stigma, linguistic expression pattern. 7,236 Sina Weibo posts relevant keywords were downloaded analyzed. First, a content analysis was performed by human coders determine each post reflected suicide-related stigma. Second, text conducted using Simplified Chinese version LIWC software automatically extract psycholinguistic features from post. Third, based selected features, classification models developed machine learning techniques differentiate that suicide.Results showed that, first, cybersuicide-related generally more than ( χ12= 255.13, p < 0.001). Significant also observed seven types. among reflecting knowledge, carried stigma χ12 = 116.77, established models, highest F1 value reached 0.70.The findings provide evidence indicate need for awareness campaigns specifically target cybersuicide.

Language: Английский

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The Threats and Current Practices of Malicious Use of Artificial Intelligence in Psychological Security in China

Springer eBooks, Journal Year: 2023, Volume and Issue: unknown, P. 335 - 375

Published: Jan. 1, 2023

Language: Английский

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