Document Type
Presentation
Publication Date
11-2021
Publication Title
Annual Meeting of the Society for Computation in Psychology (SCIP)
Abstract
The research investigated the suggestion from prior research that language and emojis use on Twitter and other social media platforms can predict users’ personality and gender (Adali et al., 2014; Golbeck et al., 2011; Li et al., 2019; Moreno et al., 2019; Raess, 2018). Some studies have also analyzed Twitter language to identify individuals with specific health conditions (e.g., alcohol recovery, Golbeck, 2012; sleep problems, Suarez et al., 2018).
If strategies to predict Twitter users’ characteristics prove to be successful, future efforts to direct persuasive messages related to recommended practices in public health and/or cybersecurity will be possible. Commercial applications of such prediction are currently underway, involving targeting users’ with twitter ads as well as ads on other platforms.
We investigated whether there emojis and words using on Twitter was related to users’ gender, Big Five personality traits, and TrueColors self-schemas. These variables have been found to be related to behavior related to cybersecurity behavior (Bakas et al., 2021; Kennison & Chan-Tin, 2020; Kennison et al., 2021).
Recommended Citation
Meckling, Maxwell; Shoup, Sarah; Chan-Tin, D. E.; and Kennison, Shelia. Tweets R Us: Predicting Personality from Language and Emoji Use on Twitter. Annual Meeting of the Society for Computation in Psychology (SCIP), , : , 2021. Retrieved from Loyola eCommons, Computer Science: Faculty Publications and Other Works,
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
Author Posting © Maxwell Meckling, Sarah Shoup, D. E. Chan-Tin, Shelia Kennison, 2021.
Comments
Author Posting © Maxwell Meckling, Sarah Shoup, D. E. Chan-Tin, Shelia Kennison, 2021. This research was funded by NSF (DGE 1918591 & 1919004).