- Title
- Psychoinformatics : the behavioral analytics
- Creator
- Nimje, Sparsh; Katade, Jayesh; Dunbray, Nachiket; Mavale, Shreyas; Kulkarni, Siddhivinayak; Firmin, Sally
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190960
- Identifier
- vital:17741
- Identifier
-
https://doi.org/10.1007/978-981-16-8862-1_36
- Identifier
- ISBN:1876-1100 (ISSN); 9789811688614 (ISBN)
- Abstract
- Human behavior is very complex and cannot be explained using traditional mathematical models. Intermediate forms, such as those obtained from personality data, can be used to predict behavioral aspects of a person, creating the hypothesis that arbitrating psychological models can be drawn directly from recordings of behavior. In recent years, smartphone addiction has increased to a great extent. Since the excessive use of smartphones has negatively affected our daily life, many applications to reduce dependence on smartphones have been developed around the world. Personal attributes or personality types can be extracted from data obtained directly from smart phones without the interaction of participants who may have social or health interventions. Many people who excessively use their smartphones have an uncontrollable urge to use the Internet. Internet addiction refers to uncontrolled use of the Internet which causes hindrance in our daily life. Due to its negative impact on the education and lives of people, it is necessary to detect tendencies of people toward addictive behavior and provide them with preventative support and treatment. Similarly, the development of social media has seen rapid growth in its usage. People often find themselves overusing utilities such as virtual communication, texting, and sharing information which have also caused various behavioral problems. This study provides a summary of the various methods and studies done on these behavioral problems and to analyze different techniques, and machine learning models are used to predict addictive personality types. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 3rd International Conference on Communication, Computing and Electronics Systems, ICCCES 2021, Coimbatore, India, 28-29 October 2021, Proceedings of Third International Conference on Communication, Computing and Electronics Systems Vol. 844, p. 547-562
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s)
- Subject
- Behavioral analytics; Big five personality traits; Human behavior; Informatics; Internet addiction social media addiction; Smartphone addiction
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