¹Istanbul Kultur University, Department of Computer Engineering, Bakirkoy, Istanbul, Turkey.
²Yonsei University, Department of Library and Information Science, Seoul, Republic of Korea.

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Abstract—Social networking platforms such as Facebook, Twitter, YouTube, and Instagram, which generate a vast amount of data every second, emerged dramatically within the last ten years. This huge rich data provides crucial information about social interactions and human behaviour. Therefore, it is possible to identify the personality traits of a person by extracting and analysing relevant information from the social media. Recently, researchers demonstrated that personality prediction can be performed by using historical textual features and user profiles. While cross-cultural personality research is considered as a powerful tool to observe the differences in personality psychology, machine learning researchers analysing social media data did not perform research on the development of cross-cultural personality prediction models yet. In this paper, we propose a new research topic called cross-cultural personality prediction and discuss how these kinds of models can be built in practice. In the joint research project, we investigate whether there is a cultural or language difference in personality between people in two countries namely, South Korea and Turkey in Twitter. It will be particularly interesting because both of the countries are in Asia, but located in both ends of Asia and we’ll investigate whether there is any meaningful personality difference in two countries or not. We argue that this kind of research will impact not only personality services such as IBM Personality Insight, but also cross-cultural personality psychology research area in the near future.

Index Terms—Personality prediction, regression analysis, social media mining, supervised learning.

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Cite: Cagatay Catal, Min Song, Can Muratli, Erin Hea-Jin Kim, Mestan Ali Tosuner, Yusuf Kayikci, "Cross-Cultural Personality Prediction based on Twitter Data," Journal of Software vol. 12, no. 11, pp. 882-891, 2017.