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.
| Original language | English |
|---|---|
| Pages (from-to) | 480-492 |
| Number of pages | 13 |
| Journal | Computers in Human Behavior |
| Volume | 68 |
| DOIs | |
| Publication status | Published - Mar 1 2017 |
| Externally published | Yes |
Fingerprint
Keywords
- Machine learning
- Psychometrics
- Science communication
- Social media
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Human-Computer Interaction
- Psychology(all)
Cite this
What makes you tick? The psychology of social media engagement in space science communication. / Hwong, Yi Ling; Oliver, Carol; Van Kranendonk, Martin; Sammut, Claude; Seroussi, Yanir.
In: Computers in Human Behavior, Vol. 68, 01.03.2017, p. 480-492.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - What makes you tick? The psychology of social media engagement in space science communication
AU - Hwong, Yi Ling
AU - Oliver, Carol
AU - Van Kranendonk, Martin
AU - Sammut, Claude
AU - Seroussi, Yanir
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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.
AB - 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.
KW - Facebook
KW - Machine learning
KW - Psychometrics
KW - Science communication
KW - Social media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85002963717&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85002963717&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2016.11.068
DO - 10.1016/j.chb.2016.11.068
M3 - Article
AN - SCOPUS:85002963717
VL - 68
SP - 480
EP - 492
JO - Computers in Human Behavior
JF - Computers in Human Behavior
SN - 0747-5632
ER -