BACKGROUND: Asbestos-linked public health problems were widely reported in Japan, in 2005. The objective is to apply text mining with network analysis to characterize these problems. METHODS: Text mining with network analysis of newspaper headlines including the word 'asbestos' published in 1987 and 2005 was conducted. Outcome measures are occurrence of the words and simultaneous occurrence of two words in the newspaper headlines. RESULTS: In 36 headlines, which contained the word 'asbestos' in 1987, the word 'pollution' (40%) appeared most frequently, followed by 'removal' (31%) and 'campaign' (29%). For combinations of words, the following occurred most frequently: 'campaign and expulsion' (26%) followed by 'removal and campaign' (14%). Of 293 headlines in 2005, the following words appeared: 'hazard' (31%), 'person' (16%) and 'death' (13%). For combinations, the following appeared: 'person and death' (9%). Asbestos pollution and removal campaigns were reported in 1987, but the death of citizens was reported in 2005. CONCLUSIONS: Text mining with network analysis, which presents one of the methods for visualization of text data, suggests the following insight. Insufficient steps against asbestos had been taken for 20 years, which is compatible with the latency period. It has resulted in widespread exposure to asbestos and more severe asbestos-related public health problems among citizens. This methodology suggests that analyzing text data by this method can serve future surveillance and efficient use of epidemiological knowledge.
BACKGROUND:Asbestos-linked public health problems were widely reported in Japan, in 2005. The objective is to apply text mining with network analysis to characterize these problems. METHODS: Text mining with network analysis of newspaper headlines including the word 'asbestos' published in 1987 and 2005 was conducted. Outcome measures are occurrence of the words and simultaneous occurrence of two words in the newspaper headlines. RESULTS: In 36 headlines, which contained the word 'asbestos' in 1987, the word 'pollution' (40%) appeared most frequently, followed by 'removal' (31%) and 'campaign' (29%). For combinations of words, the following occurred most frequently: 'campaign and expulsion' (26%) followed by 'removal and campaign' (14%). Of 293 headlines in 2005, the following words appeared: 'hazard' (31%), 'person' (16%) and 'death' (13%). For combinations, the following appeared: 'person and death' (9%). Asbestos pollution and removal campaigns were reported in 1987, but the death of citizens was reported in 2005. CONCLUSIONS: Text mining with network analysis, which presents one of the methods for visualization of text data, suggests the following insight. Insufficient steps against asbestos had been taken for 20 years, which is compatible with the latency period. It has resulted in widespread exposure to asbestos and more severe asbestos-related public health problems among citizens. This methodology suggests that analyzing text data by this method can serve future surveillance and efficient use of epidemiological knowledge.