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Growing concerns about cyber-suicide have prompted many studies on suicide information available on the web. However, very few studies have considered non-English websites. We aimed to analyze online suicide-related information accessed through Chinese-language websites. We used Taiwan's two most popular search engines (Google and Yahoo) to explore the results returned from six suicide-related search terms in March 2016. The first three pages listing the results from each search were analyzed and rated based on the attitude towards suicide (pro-suicide, anti-suicide, neutral/mixed, not a suicide site, or error). Comparisons across different search terms were also performed. In all, 375 linked webpages were included; 16.3% of the webpages were pro-suicide and 41.3% were anti-suicide. The majority of the pro-suicide sites were user-generated webpages (96.7%). Searches using the keywords 'ways to kill yourself' (31.7%) and 'painless suicide' (28.3%) generated much larger numbers of harmful webpages than the term 'suicide' (4.3%). We conclude that collaborative efforts with internet service providers and search engines to improve the ranking of anti-suicide webpages and websites and implement online suicide reporting guidelines are highly encouraged.
Search Engine, Internet, Suicide, Attitude, Taiwan, Humans, Language
Search Engine, Internet, Suicide, Attitude, Taiwan, Humans, Language
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |