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Wild pigs (Sus scrofa) are a non-native, invasive species that cause considerable damage and transmit a variety of diseases to livestock, people, and wildlife. We explored Twitter, the most popular social media micro-blogging platform, to demonstrate how social media data can be leveraged to investigate social identity and sentiment toward wild pigs. In doing so, we employed a sophisticated machine learning approach to investigate: (1) the overall sentiment associated with the dataset, (2) online identities via user profile descriptions, and (3) the extent to which sentiment varied by online identity. Results indicated that the largest groups of online identity represented in our dataset were females and people whose occupation was in journalism and media communication. While the majority of our data indicated a negative sentiment toward wild pigs and other related search terms, users who identified with agriculture-related occupations had more favorable sentiment. Overall, this article is an important starting point for further investigation of the use of social media data and social identity in the context of wild pigs and other invasive species.
Jaebker, Lauren M.; McLean, Hailey E.; Shwiff, Stephanie A.; Carlisle, Keith M.; Teel, Tara L.; Bright, Alan D.; and Anderson, Aaron M.
"Machine Learning as a Tool for Wildlife Management and Research: The Case of Wild Pig-Related Content on Twitter,"
Human–Wildlife Interactions: Vol. 15
, Article 16.
Available at: https://digitalcommons.usu.edu/hwi/vol15/iss1/16
Additional FilesAppendix 1.docx (17 kB)
Appendix 2.docx (19 kB)
Online identity categories