JMIR Publications

ABSTRACT

Background:

Hikikomori is a form of severe social withdrawal that is particularly prevalent in Japan. Social media posts offer insights into public perceptions of mental health conditions, and also may inform strategies to identify, engage with care, and support hard-to-reach patient populations such as individuals affected by hikikomori.

Objective:

We sought to identify types of contents prevalent on Twitter related to hikikomori in Japanese language, and to assess the users’ engagement elicited by those contents.

Methods:

We conducted a mixed-methods analysis of a random sample of 4,940 Japanese tweets from February-August 2018 with the hashtag (#hikikomori). Qualitative content analysis included examination of the text of tweets, development of a codebook, and categorization of tweets into relevant codes. For quantitative analysis (n=4,859 tweets), we used bivariate and multivariate logistic regression models, adjusted for multiple comparisons, and estimated predicted probabilities of tweets receiving engagement (likes or retweets).

Results:

Our content analysis identified nine codes relevant to tweets about hikikomori: ‘personal anecdotes’, ‘social support’, ‘marketing’, ‘advice’, ‘stigma’, ‘educational opportunities’, ‘refuge (“ibasho”)’, ‘employment opportunities’, and ‘medicine and science’. Tweets about ‘personal anecdotes’ were most common (present in 56% of the tweets), followed by ‘social support’ (18.6%) and ‘marketing’ (12.8%). In adjusted models, tweets coded as ‘stigma’ had a lower predicted probability of receiving likes (-33 percentage points; 95% CI, -42 to -23 percentage points; p < .001) and retweets (-11 percentage points; 95% CI, -18 to -4 percentage points; p <. 001), ‘personal anecdotes’ had a lower predicted probability of receiving retweets (-8 percentage points; 95% CI, -14 to -3 percentage points; p = 0.002), ‘marketing’ had lower predicted probability of receiving likes (-13 percentage points; 95% CI, -21 to -6 percentage points; p < .001), and ‘social support’ had higher predicted probability for retweets (+15 percentage points; 95% CI, +6 to +24 percentage points; p = 0.001), compared with all tweets without each of these codes.

Conclusions:

Japanese tweets about hikikomori reflect a unique array of topics, many of which have not been identified in prior research and vary in their likelihood of receiving engagement. Tweets often contain personal stories of hikikomori, suggesting the potential to identify individuals with hikikomori through Twitter.