TY - JOUR
T1 - My tweets bring all the traits to the yard
T2 - predicting personality and relational traits in online social networks
AU - Karanatsiou, Dimitra
AU - Sermpezis, Pavlos
AU - Gruda, Dritjon
AU - Kafetsios, Konstantinos
AU - Dimitriadis, Ilias
AU - Vakali, Athena
N1 - Funding Information:
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH - CREATE - INNOVATE (Project Code: T1EDK-03052), as well as from the H2020 Research and Innovation Programme under Grant Agreement No. 875329 and a departmental seed funding grant by the School of Business, Maynooth University, Ireland. Authors’ addresses: D. Karanatsiou, P. Sermpezis, K. Kafetsios, I. Dimitriadis, and A. Vakali, Aristotle University of Thessaloniki, Thessaloniki, Greece; emails: [email protected], [email protected], [email protected], [email protected], [email protected]. D. Gruda (corresponding author), National University Ireland Maynooth, Maynooth, Ireland; email: [email protected].
Publisher Copyright:
© 2022 Copyright held by the owner/author(s).
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
KW - Machine learning
KW - Online behavior
KW - Personality prediction
KW - Social networks
KW - User profiling
UR - http://www.scopus.com/inward/record.url?scp=85131120158&partnerID=8YFLogxK
U2 - 10.1145/3523749
DO - 10.1145/3523749
M3 - Article
AN - SCOPUS:85131120158
SN - 1559-1131
VL - 16
JO - ACM Transactions on the Web
JF - ACM Transactions on the Web
IS - 2
M1 - 10
ER -