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VOL. 12, ISSUE 1 (2026)
Predicting the impact of social media campaigns on women’s socio-economic empowerment in Jammu & Kashmir using machine learning algorithms
Authors
Uzma Hamid
Abstract
Digital communication technologies have
significantly transformed the structure of social awareness campaigns,
particularly those focusing on gender equality and women’s socio-economic
empowerment. In geographically and socio-politically sensitive regions such as
Jammu and Kashmir (J&K), social media campaigns have emerged as powerful
instruments for disseminating information related to education, employment,
entrepreneurship, and social rights. However, measuring the real-world impact
of these campaigns remains a complex challenge because traditional evaluation
methods rely heavily on surveys and field reports, which are often limited in
scale and time-intensive.This study proposes a machine learning-based
predictive framework that evaluates the effectiveness of social media campaigns
in promoting women’s socio-economic empowerment in J&K. A large dataset of
social media posts related to empowerment campaigns was collected and processed
using Natural Language Processing (NLP) techniques. Sentiment analysis, engagement
analytics, and topic modeling were used as predictive indicators, while machine
learning algorithms such as Logistic Regression, Support Vector Machine (SVM),
and Random Forest were implemented to estimate campaign outcomes.The findings
indicate that positive sentiment intensity, campaign engagement levels, and the
thematic focus on education and entrepreneurship significantly influence
empowerment outcomes. The study demonstrates that machine learning-based
predictive analytics can serve as an effective decision-support tool for
policymakers, development agencies, and non-governmental organizations
designing digital empowerment initiatives.
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Pages:28-31
How to cite this article:
Uzma Hamid "Predicting the impact of social media campaigns on women’s socio-economic empowerment in Jammu & Kashmir using machine learning algorithms". International Journal of Research in Advanced Engineering and Technology, Vol 12, Issue 1, 2026, Pages 28-31
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