Abstract-The sheer usage of social media presents an opportunity for an automated analysis of a social media user based on his/her information, activities, or status updates. This opportunity is due to the abundant amount of information shared by the user. This fact is especially true for countries with high number of active social media users such as Indonesia. Extraction of information from social media can yield insightful results if done correctly. Recent studies have managed to leverage associations between language and personality and build a personality prediction system based on those associations. The current study attempts to build a personality prediction system based on a Twitter user's information for Bahasa Indonesia, the native language of Indonesia. The personality prediction system is built on Support Vector Machine and XGBoost trained with 329 instances (users). Evaluation results using 10-fold cross validation shows that the system managed to reach highest average accuracy of 76.2310% with Support Vector Machine and 97.9962% with XGBoost.

The wide-spread use of online social networking sites has sparked many psychological issues and research around the globe. A unique phenomenon has come into existence in which people rely on Facebook to satisfy their attachment need. It is one of the very innately psychological needs that presents throughout a life span. The present study investigated the interaction between attachment style and social networking site use. The result showed that attachment style contributed significantly to the level of social media use. It also pointed out that the attachment style distinguished significantly between active and non-active users. Future research and implications of the study were discussed.

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Amid controversy over plurality and contestation of the meanings of corruption, previous reviews and studies showed that proneness to moral emotions, i.e. shame and guilt, can predict one's corruption behavior. To give a theoretical basis for the efforts of preventing corruption that is thick with emotional nuance, this present study employs disruption to psychological contract, i.e. psychological contract breach (PCB), as a predictor of moral emotions proneness. The study involving 265 employees (169 males, 96 females; M age ¼ 32.32 years old; SD age ¼ 7.28 years) of four big private banks in Jakarta, the capital city of Indonesia, shows that PCB-with noting that, in this study, its scale operational scoring represents, reversely, the contract fulfillment-can predict Guilt-negative behavior evaluation (Guilt-NBE), Guilt-repair (Guilt-REP), and Shame-negative self-evaluation (Shame-NSE); all in negative directions, proved via simple linear regression analyses. Further analysis showed a more dynamic relationship between PCB and Guilt-NBE that fits to a cubic regression model. This study contributes to the axiological aspect of business psychology, especially in the ethical psychology of banking industry.

This dataset is a measurement of Celebrity Worship (CWS), Digital Literacy (DL), and Nostalgia (NA). The participants were (1) For CWS, N = 3,223 people (181 males, 3042 females; M age = 19.64 years old; SD age = 3.13 years), (2) For DL, N = 482 people (225 males, 257 females; M age = 25.16 years old; SD age = 8.54 years), (3) For NA, N = 658 people (140 males, 518 females; M age = 21.26 years old; SD age = 1.93 years). The data was obtained using a survey via Google Forms in June 2018 and April–July 2020 in Indonesia. The analysis techniques were confirmatory factor analyses/CFA using LISREL and test of differences using JASP. The data could be used by the Indonesian Ministry of Communication and Informatics (KEMENKOMINFO), Indonesian Ministry of Education and Culture (KEMENDIKBUD), Indonesian Ministry of Youth and Sports (KEMENPORA), as well as social marketers to map the three constructs in Indonesian youth and to prevent the adverse effects and impacts of celebrity worship, digital illiteracy, and inappropriate mode of nostalgia.