AffiliationChongqing Technology and Business University
Hong Kong Shue Yan University
Rajamangala University of Technology Tawan-Ok
University of Bedfordshire
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AbstractMany studies show that unsafe behavior is the main cause of construction accidents. Safety education and training are effective means to minimise people’s unsafe behaviors. Apart from traditional face-to-face construction knowledge sharing, social media is a good tool because it is convenient, efficient, and widely used. We applied both social network analysis and sentiment analysis to investigate knowledge sharing on Twitter. Our study is a novel attempt to understand social structure of “construction safety”- related twitter networks and the opinion leaders. We selected and analyzed 6561 tweets of three users’ networks on Twitter – “construction safety”, “construction health” and “construction accident”. We found that three networks had low density and many isolated vertices, which showed that users did not actively interact with each other. The opinion leaders in this study were mostly organizations or government agencies. The top one is “cif_ireland”, the Irish construction industry’s representative body, the Construction Industry Federation. 3200 Tweets of the top opinion leader were analyzed through graph metrics calculation, cluster analysis, sentiment analysis, and correlation analysis. The opinion leader used Twitter as a medium to disseminate the latest safety news. Thus, we may use Twitter to stimulate people’s interest on construction safety topics, share construction safety knowledge, opinions and ideas. Besides, our results showed that sentiment valence had no correlation with number of favorites or retweets. Nevertheless, there was a positive correlation between favorites and retweets.
CitationYao Q, Li RYM, Song L, Crabbe MJC (2021) 'Safety knowledge sharing on Twitter: a social network analysis', Safety Science, 143 (105411)
SponsorsResearch Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS15/E01/18).
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