Q3 Social Sciences Szociologiai Szemle Pub Date : 2021-01-01 DOI:10.51624/SZOCSZEMLE.2021.1.3
Fanni Máté
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引用次数: 1

摘要

如今,网络社区是典型的社会支持来源,尤其是对那些患有抑郁症或焦虑症的人来说,这是一个相当大的帮助。我的研究目的是调查在线抑郁和焦虑论坛上的社会支持模式,并作为自然语言处理使用的探索性研究,将评论分类为社会支持的类别。我的研究的独特之处在于在对整个数据集进行完整的定性分析的基础上进行了定量文本分析。定性分析的结论为模型的定义和评价提供了深刻的信息。这些知识对于研究自动文本分析在社会学中的潜力是很重要的。平均而言,在被调查的论坛上,五分之四的评论与社会支持有关。在支持性评论中,信息支持占59.9%,情感支持占44.7%。应用模型的准确率接近80%,这意味着它们将绝大多数评论分类到正确的类别中。结果表明,有可能建立可靠的模型,以便将评论分类到先前定义的社会支持类别中。
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Társas támogatás megjelenése egy depresszió és szorongás témájú online fórumon
Nowadays, online communities are typical sources of social support, which is a considerable help especially for those suffering from depression or anxiety. The aim of my research is to investigate the patterns of social support on an online depression and anxiety forum and to serve as an exploratory research of Natural Language Processing usage to classify comments into the categories of social support. The uniqueness of my research is the quantitative text analysis based on a complete qualitative analysis of the whole dataset. The conclusions of the qualitative analysis provide profound information for model definition, and for their evaluation. This knowledge is important for the investigation the potential of automatic text analysis in sociology. On average, four out of five comments are related to social support on the examined forum. Informational support appears in 59.9 percent of the supportive comments, while emotional support appears in 44.7 percent. The applied models’ accuracies are nearly 80 percent, which means that they classified the vast majority of comments into the right category. The results show that there is a potential in building reliable models in order to classify the comments into the previously defined categories of social support.
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来源期刊
Szociologiai Szemle
Szociologiai Szemle Social Sciences-Social Sciences (all)
CiteScore
1.10
自引率
0.00%
发文量
5
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