What makes you tick? The psychology of social media engagement in space science communication. (March 2017)

• Record Type: Journal Article
• Authors: Hwong, Yi-Ling; Oliver, Carol; Van Kranendonk, Martin; Sammut, Claude; Seroussi, Yanir
• Is Part Of: Computers in human behavior. Volume 68(2017)
• Page Start: 480
• Page End: 492
• Publication Date: 2017-03
• Subjects: Science communication -- Social media -- Machine learning -- Psychometrics -- Facebook -- Twitter; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2016.11.068
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: The rise of social media has transformed the way the public engages with science organisations and scientists. 'Retweet', 'Like', 'Share' and 'Comment' are a few ways users engage with messages on Twitter and Facebook, two of the most popular social media platforms. Despite the availability of big data from these digital footprints, research into social media science communication is scant. This paper presents a novel empirical study into the features of engaging science-related social media messages, focusing on space science communications. It is hypothesised that these messages contain certain psycholinguistic features that are unique to the field of space science. We built a predictive model to forecast the engagement levels of social media posts. By using four feature sets (n-grams, psycholinguistics, grammar and social media), we were able to achieve prediction accuracies in the vicinity of 90% using three supervised learning algorithms (Naive Bayes, linear classifier and decision tree). We conducted the same experiments on social media messages from three other fields (politics, business and non-profit) and discovered several features that are exclusive to space science communications: anger, authenticity, hashtags, visual descriptions—be it visual perception-related words, or media elements—and a tentative tone. Highlights: Big data analysis to study social media engagement in space science communication. Machine learning predicted social media message engagement level with 90% accuracy. Engaging social media space science posts contain features unique to space science. These features are anger, authenticity, visual description and a tentative tone.