An informal institution comparative study of occupational safety knowledge sharing via French and English Tweets: languaculture, weak-strong ties and AI sentiment perspectives

•Analyse English and French Tweets about “occupational safety” through social network analysis and sentiment analysis.•Reveal the dominant role of strong tie in information dissemination on Twitter.•Analyse social structure of “occupational safety” twitter networks and the attributes of English and...

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Veröffentlicht in:Safety science 2022-03, Vol.147, p.105602, Article 105602
Hauptverfasser: Song, Lingxi, Li, Rita Yi Man, Yao, Qi
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Sprache:eng
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Zusammenfassung:•Analyse English and French Tweets about “occupational safety” through social network analysis and sentiment analysis.•Reveal the dominant role of strong tie in information dissemination on Twitter.•Analyse social structure of “occupational safety” twitter networks and the attributes of English and French opinion leaders.•The number of extreme negative French tweets was more than English, negative tweets were more likely to be favorited.•Offer suggestions for policy makers, occupational safety organisations when they wish to share occupational safety knowledge via Twitter. To study the social structure of English and French Tweets of occupational safety and their sentiment distributions, this study applied NodeXL and MeaningCloud to analyse 17,147 English Tweets and 16,618 French Tweets about “occupational safety” in Twitter. We found that French and English Twitter users who are interested in this topic did not usually interact. While top English Twitter influencers were professors, top French influencers were government officers and individuals. Clusters of Twitter members interested in occupational safety had a low tendency to reach people in other groups. Most failed to make good use of weak ties to increase their impact and shared information about occupational safety outside their circle of friends. This overthrows previous research that Twitter’s social network was built based on the weak tie: Twitter users follow commentators, celebrities, and opinion leaders who do not know personally. Besides, we also conducted sentiment analysis via machine learning algorithms. We found that the more positive sentiment of an English Tweets, the more likely it will be retweeted. Yet, the more negative sentiment of a French Tweets, the more likely the Tweets will be retweeted. Thus, negative occupational safety Tweets have stronger impacts than positive ones among French but not English Tweets. While sentiment analysis results of French Tweets indicated that most Twitter users discussed occupational safety issues with a neutral tone, the number of extreme negative in French Tweets was a lot more than that of English. That reflects languaculture differences, and informal institutions impact users’ behaviours.
ISSN:0925-7535
1879-1042
DOI:10.1016/j.ssci.2021.105602