Public Attitudes Toward Anxiety Disorder on Sina Weibo : Content Analysis

©Jianghong Zhu, Zepeng Li, Xiu Zhang, Zhenwen Zhang, Bin Hu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2023..

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

OBJECTIVE: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts.

METHODS: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword "anxiety disorder" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety.

RESULTS: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area "discrimination and stigma" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area "family and life" (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased.

CONCLUSIONS: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

Medienart:	E-Artikel

Erscheinungsjahr:	2023
Erschienen:	2023

Enthalten in:	Zur Gesamtaufnahme - volume:25
Enthalten in:	Journal of medical Internet research - 25(2023) vom: 04. Apr., Seite e45777

Sprache:	Englisch

Beteiligte Personen:	Zhu, Jianghong [VerfasserIn] Li, Zepeng [VerfasserIn] Zhang, Xiu [VerfasserIn] Zhang, Zhenwen [VerfasserIn] Hu, Bin [VerfasserIn]

Links:	Volltext

Themen:	Anxiety disorder Journal Article Linguistic feature Public attitude Research Support, Non-U.S. Gov't Social media Topic model

Anmerkungen:	Date Completed 06.04.2023 Date Revised 10.05.2023 published: Electronic Citation Status MEDLINE

doi:	10.2196/45777

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM355194554