Karanatsiou, Dimitra and Sermpezis, Pavlos and Gruda, Dritjon and Kafetsios, Konstantinos and Dimitriadis, Ilias and Vakali, Athena (2022) My Tweets Bring All the Traits to the Yard: Predicting Personality and Relational Traits in Online Social Networks. ACM Transactions on the Web, 16 (2). pp. 1-26. ISSN 1559-1131

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Abstract

Users in Online Social Networks (OSNs,) leave traces that reflect their personality characteristics. The study of these traces is important for several fields, such as social science, psychology, marketing, and others. Despite a marked increase in research on personality prediction based on online behavior, the focus has been heavily on individual personality traits, and by doing so, largely neglects relational facets of personality. This study aims to address this gap by providing a prediction model for holistic personality profiling in OSNs that includes socio-relational traits (attachment orientations) in combination with standard personality traits. Specifically, we first designed a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from the OSN accounts of users. Subsequently, we designed a machine learning model that predicts trait scores of users based on the extracted features. The proposed model architecture is inspired by characteristics embedded in psychology; i.e, it utilizes interrelations among personality facets and leads to increased accuracy in comparison with other state-of-the-art approaches. To demonstrate the usefulness of this approach, we applied our model on two datasets, namely regular OSN users and opinion leaders on social media, and contrast both samples’ psychological profiles. Our findings demonstrate that the two groups can be clearly separated by focusing on both Big Five personality traits and attachment orientations. The presented research provides a promising avenue for future research on OSN user characterization and classification.

Item Type:	Article
Keywords:	User profiling; personality prediction; social networks; online behavior; machine learning;
Academic Unit:	Faculty of Social Sciences > School of Business
Item ID:	17628
Identification Number:	https://doi.org/10.1145/3523749
Depositing User:	Jon Gruda
Date Deposited:	02 Oct 2023 15:02
Journal or Publication Title:	ACM Transactions on the Web
Publisher:	Association for Computing Machinery (ACM)
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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