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Assessing symptoms of excessive sns usage based on user behavior and emotion: Analysis of data obtained by sns APIs

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Abstract

The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Excessive and compulsive use of them has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. We designed a data collection application and developed a tool for collecting data from questionnaires and SNSs by APIs. The data were collected at the Thai-Nichi Institute of Technology (TNI), Thailand from 177 volunteers. We introduce our analysis of data obtained by SNS APIs by focusing on Facebook and Twitter. We used modified IAT and BFAS to measure SNS addiction. The Facebook and Twitter results, including a combination with questionnaires, were analyzed to identify the factors associated with SNS addiction. Our analytic results identified potential candidates of the key components of SNS addiction.

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APA

Intapong, P., Charoenpit, S., Achalakul, T., & Ohkura, M. (2017). Assessing symptoms of excessive sns usage based on user behavior and emotion: Analysis of data obtained by sns APIs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10282 LNCS, pp. 71–83). Springer Verlag. https://doi.org/10.1007/978-3-319-58559-8_7

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