Li Ang, Jiao Dongdong, Liu Xiaoqian, Zhu Tingshao
Department of Psychology, Beijing Forestry University, Beijing, China.
Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
J Med Internet Res. 2020 Apr 21;22(4):e16470. doi: 10.2196/16470.
Stigma related to schizophrenia is considered to be the primary focus of antistigma campaigns. Accurate and efficient detection of stigma toward schizophrenia in mass media is essential for the development of targeted antistigma interventions at the population level.
The purpose of this study was to examine the psycholinguistic characteristics of schizophrenia-related stigma on social media (ie, Sina Weibo, a Chinese microblogging website), and then to explore whether schizophrenia-related stigma can be distinguished from stigma toward other mental illnesses (ie, depression-related stigma) in terms of psycholinguistic style.
A total of 19,224 schizophrenia- and 15,879 depression-related Weibo posts were collected and analyzed. First, a human-based content analysis was performed on collected posts to determine whether they reflected stigma or not. Second, by using Linguistic Inquiry and Word Count software (Simplified Chinese version), a number of psycholinguistic features were automatically extracted from each post. Third, based on selected key features, four groups of classification models were established for different purposes: (a) differentiating schizophrenia-related stigma from nonstigma, (b) differentiating a certain subcategory of schizophrenia-related stigma from other subcategories, (c) differentiating schizophrenia-related stigma from depression-related stigma, and (d) differentiating a certain subcategory of schizophrenia-related stigma from the corresponding subcategory of depression-related stigma.
In total, 26.22% of schizophrenia-related posts were labeled as stigmatizing posts. The proportion of posts indicating depression-related stigma was significantly lower than that indicating schizophrenia-related stigma (χ=2484.64, P<.001). The classification performance of the models in the four groups ranged from .71 to .92 (F measure).
The findings of this study have implications for the detection and reduction of stigma toward schizophrenia on social media.
与精神分裂症相关的污名被认为是反污名运动的主要关注点。在大众媒体中准确、高效地检测对精神分裂症的污名，对于在人群层面开展有针对性的反污名干预措施至关重要。
本研究旨在探讨社交媒体（即中国微博网站新浪微博）上与精神分裂症相关污名的心理语言学特征，然后从心理语言学风格方面探索是否能将与精神分裂症相关的污名与对其他精神疾病（即与抑郁症相关的污名）的污名区分开来。
共收集并分析了19224条与精神分裂症相关和15879条与抑郁症相关的微博帖子。首先，对收集到的帖子进行基于人工的内容分析，以确定它们是否反映污名。其次，使用语言查询与字数统计软件（简体中文版），从每条帖子中自动提取一些心理语言学特征。第三，基于选定的关键特征，为不同目的建立了四组分类模型：（a）区分与精神分裂症相关的污名和非污名；（b）区分与精神分裂症相关污名的某一亚类与其他亚类；（c）区分与精神分裂症相关的污名和与抑郁症相关的污名；（d）区分与精神分裂症相关污名的某一亚类与与抑郁症相关污名的相应亚类。
总共26.22%与精神分裂症相关的帖子被标记为污名化帖子。表明与抑郁症相关污名的帖子比例显著低于表明与精神分裂症相关污名的帖子比例（χ=2484.64，P<.001）。四组模型的分类性能范围为0.71至0.92（F值）。
本研究结果对在社交媒体上检测和减少对精神分裂症的污名具有启示意义。
