Abstract:
Social network sites such as Facebook and Twitter have significant importance in connecting people at
present. For scholarly use, information from these networks can be used for various research purposes
including data mining trend analysis etc. On the other hand, aquaculture provides one of the best
solutions for global food security and poverty alleviation. Future growth of aquaculture practices may also
depend upon the knowledge/information sharing on social media platformi. The purpose of this itudy
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. Topic
modeling approach was used to identiff the key aquaculture themes that can be used to classi1r ihe
tweets. Descriptive analysis of tweets indicated that twitter users have used 17 language profiles. 372 twitter profiles tweeted about aquaculture. "GAA Aquaculture" (2.2o/o), "Farming Til+ia; $.gVo),,,Grow Aquapincis" (1.60/o), "wild4salmon" (L.2o/o) and "FAo frsh" {L.2o/o) were top twittei profiles with the
highest number of tweets. 'Aquaculture', 'salmon', 'fish', 'sustainable' and 'lice' werl most frequent
keywords' Correlation analysis indicated that term 'salmon' was significantly correlated (p<0.0S) with
'Wild salmon', 'bute fish', 'Argyll' and 'fish farm get out'. Topic model results indicated that tweets can be
classified into five key themes (Food security and sustainable aquaculture, fish nutrition, sea Iice
infestation in salmon aquaculture and Tilapia aquaculture). Diurnal variation of the tweets indicated that
food security, sustainable aquaculture, fish nutrition and sea lice infestation were most discussed topics
among twitter community.
