Machine Learning Predictive Analytics for Social Media Enabled Women's Economic Empowerment in Pakistan
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arXiv
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| Format: | Preprint |
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2025
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| author | Arif, Maryam Saeed, Soban |
| author_facet | Arif, Maryam Saeed, Soban |
| contents | Our study investigates the interplay between young women's empowerment and Pakistan's economic growth, focusing on how social media use enhances their businesses and drives economic advancement. We utilize a mixed-methods research design, integrating both online and offline random sampling, for our survey of 51 respondents. We also utilized existing datasets consisting of both social media usage (n = 1000) and entrepreneurship (n = 1092). Our analysis identifies distinct social media engagement patterns via unsupervised learning and applies supervised models for entrepreneurship prediction, with logistic regression outperforming all other algorithms in terms of predictive accuracy and stability. In social media use, the cluster analysis reveals that at K=2, users form tightly packed, well-separated engagement groups. The results indicate that 39.4 percent of respondents believe social media positively impacts the economy by enabling businesses to generate increased revenue. However, only 14 percent of respondents participate in entrepreneurship, highlighting a substantial gap between digital engagement and business adoption. The analysis indicates that daily social media consumption is widespread with YouTube (66.7 percent) and WhatsApp (62.7 percent) being the most frequently used platforms. Key barriers identified are online harassment, limited digital literacy, and cultural constraints in a patriarchal society such as Pakistan. Additionally, 52.9 percent of respondents are unaware of government initiatives supporting women entrepreneurs, indicating limited policy outreach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Predictive Analytics for Social Media Enabled Women's Economic Empowerment in Pakistan Arif, Maryam Saeed, Soban General Economics Economics Our study investigates the interplay between young women's empowerment and Pakistan's economic growth, focusing on how social media use enhances their businesses and drives economic advancement. We utilize a mixed-methods research design, integrating both online and offline random sampling, for our survey of 51 respondents. We also utilized existing datasets consisting of both social media usage (n = 1000) and entrepreneurship (n = 1092). Our analysis identifies distinct social media engagement patterns via unsupervised learning and applies supervised models for entrepreneurship prediction, with logistic regression outperforming all other algorithms in terms of predictive accuracy and stability. In social media use, the cluster analysis reveals that at K=2, users form tightly packed, well-separated engagement groups. The results indicate that 39.4 percent of respondents believe social media positively impacts the economy by enabling businesses to generate increased revenue. However, only 14 percent of respondents participate in entrepreneurship, highlighting a substantial gap between digital engagement and business adoption. The analysis indicates that daily social media consumption is widespread with YouTube (66.7 percent) and WhatsApp (62.7 percent) being the most frequently used platforms. Key barriers identified are online harassment, limited digital literacy, and cultural constraints in a patriarchal society such as Pakistan. Additionally, 52.9 percent of respondents are unaware of government initiatives supporting women entrepreneurs, indicating limited policy outreach. |
| title | Machine Learning Predictive Analytics for Social Media Enabled Women's Economic Empowerment in Pakistan |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2512.12685 |