Twitter ™ on aquaculture: understanding the latent information using R

Social media networks (Twitter™, Facebook™) have significant importance in sharing knowledge and ideas among people. Data mining in these platforms provides valuable information for scholarly use in various fields of agriculture and aquaculture. The purpose of this study was to understand the latent information of twitter messages (tweets) related to the aquaculture. R programming language and the TwitteR package were used to extract and analyze the tweets (n=500). The Topic modeling approach was used to identify the key aquaculture themes that can be used to classify the tweets. Descriptive analysis of tweets indicated that Twitter users have used 17 lan-guage profiles. 372 twitter profiles have tweeted about aquaculture. Europe and North America collectively had the highest number of tweets (60%). “GAA_Aquaculture” (2.2%), “Farming Tilapia” (1.8%), “GrowAquaponics” (1.6%), “Wild4salmon” (1.2%) and “FAOfish” (1.2%) were top twitter profiles with the high-est number of tweets. Term “salmon” was significantly correlated (p<0.05) with “Wild salmon”, ‟bute fish”, “Argyll” and “fish farm get out”. Results of the Topic model classified the tweets into five key themes (Food security and sustainable aquaculture, fish nutrition, sea lice infestation in salmon aquaculture and Tilapia aquaculture). These results indicated that mining Twitter data can be effectively used for understanding the latent information about aquaculture.