ItemConference Paper

Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining

Title
Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining
Creator
Anees Moidheen, M.M.; Parande, Athreya S; Jomy, Rayal; Presannakumar, Krishna; Souza, Mithun D
Description
The worldwide increase in substance abuse among teenagers and young adults has become serious concern in recent times. One way this pattern has developed is through the evolution of social media. Social media has transformed people's attitudes towards certain behaviors and has encouraged risky behavior to the point of actually causing addiction by exposing them to drug-related material. Despite the existence of preventative measures, such as education programs in schools, many children and youth have not had adequate access to educational interventions or evidence-based measures due to barriers created by geography, economic circumstances, and social factors, particularly in less developed countries. The research proposed is focusing on addressing this gap using a big data approach. This research employs a unique analytical framework that integrates multiple large data sets from a variety of sources to better identify and assess the effectiveness of interventions. This model employs an analytical approach that uses statistical learning techniques and predictive analytics to identify historical patterns and anticipate future trends, and assess the effectiveness of various interventions conducted in different countries. The results of the analysis suggest that this big data approach will provide decision-makers with clearly documented evidence related to various risk-taking behaviors as they relate to available prevention interventions, and will assist decision-makers in developing targeted prevention intervention strategies. This study demonstrates the revolutionary aspect behind the application of computational intelligence in preventing substance abuse and informing evidence-based community health interventions. 2025 IEEE.
Source
Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Addiction Prevention; Big Data Analytics; Predictive Modelling; Public Health Informatics; Risk Detection; Social Media Influence; Substance Abuse; Treatment Barriers
Coverage
Anees Moidheen M.M., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Parande A.S., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Jomy R., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Presannakumar K., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Souza M.D., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159676-7;
Format
online
Language
English
Type
Conference paper

Citation

Anees Moidheen, M.M.; Parande, Athreya S; Jomy, Rayal; Presannakumar, Krishna; Souza, Mithun D, “Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25942.

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