<p>The study examines the phenomenon of hikikomori in Italy as a form of “connected withdrawal,” characterized by isolation, stigma, and family tensions, but still little explored in its everyday and relational dimensions online. The research is based on a netnography of a Telegram group observed between May and November 2023, from which approximately 90,000 messages were collected (310,168 words, 21,112 unique), with participants decreasing from 121 to 89. The corpus was analyzed using Voyant Tools, a text analysis platform that identifies frequencies and co-occurrences, and the t-SNE algorithm, which reduces the complexity of data into two dimensions while preserving semantic proximity between terms. The findings highlight the centrality of lexical nuclei related to home, family, group, work, and school, with significant correlations such as hikikomori–cry (0.94), family–hikikomori (0.94), internet–graduate (0.85), and work–happiness (0.83). The analysis identified four thematic clusters: the group’s general lexicon, everyday life, relationships and stigma, and the family and economic dimension. Alongside elements of suffering and self-stigmatization, adaptive strategies emerge based on digital practices, such as continuing education and seeking remote jobs. These results point to the need for policies aimed at reducing stigma and promoting gradual reintegration pathways that account for the cultural and social specificities of the Italian context.</p>

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Exploring hikikomori through netnography: insights from a Telegram community

  • Esposito Vincenzo

摘要

The study examines the phenomenon of hikikomori in Italy as a form of “connected withdrawal,” characterized by isolation, stigma, and family tensions, but still little explored in its everyday and relational dimensions online. The research is based on a netnography of a Telegram group observed between May and November 2023, from which approximately 90,000 messages were collected (310,168 words, 21,112 unique), with participants decreasing from 121 to 89. The corpus was analyzed using Voyant Tools, a text analysis platform that identifies frequencies and co-occurrences, and the t-SNE algorithm, which reduces the complexity of data into two dimensions while preserving semantic proximity between terms. The findings highlight the centrality of lexical nuclei related to home, family, group, work, and school, with significant correlations such as hikikomori–cry (0.94), family–hikikomori (0.94), internet–graduate (0.85), and work–happiness (0.83). The analysis identified four thematic clusters: the group’s general lexicon, everyday life, relationships and stigma, and the family and economic dimension. Alongside elements of suffering and self-stigmatization, adaptive strategies emerge based on digital practices, such as continuing education and seeking remote jobs. These results point to the need for policies aimed at reducing stigma and promoting gradual reintegration pathways that account for the cultural and social specificities of the Italian context.