Cross-platform personality exploration system for online social networks: Facebook vs. Twitter

Article type: Research Article

Authors: Bin Tareaf, Raad^{; *} | Berger, Philipp | Hennig, Patrick | Meinel, Christoph

Affiliations: Faculty of Digital Engineering, Department of Internet Technologies and Systems, University of Potsdam – Hasso Plattner Institute, P.O. Box 90 04 60, D-14440 Potsdam, Germany. E-mails: raad.bintareaf@hpi.uni-potsdam.de, philipp.berger@hpi.uni-potsdam.de, patrick.hennig@hpi.uni-potsdam.de, christoph.meinel@hpi.uni-potsdam.de

Correspondence: [*] Corresponding author. E-mail: raad.bintareaf@hpi.uni-potsdam.de.

Abstract: Social networking sites (SNS) are a rich source of latent information about individual characteristics. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, commercial brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. Predictive evaluation on brands’ accounts reveals that Facebook platform provides a slight advantage over Twitter platform in offering more self-disclosure for users’ to express their emotions especially their demographic and psychological traits. Results also confirm the wider perspective that the same social media account carry a quite similar and comparable personality scores over different social media platforms. For evaluating our prediction results on actual brands’ accounts, we crawled the Facebook API and Twitter API respectively for 100k posts from the most valuable brands’ pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.

Keywords: Big Five model, personality prediction, brand personality, machine learning, social media analysis

DOI: 10.3233/WEB-200427

Journal: Web Intelligence, vol. 18, no. 1, pp. 35-51, 2020