Machine Learning in Social Analysis
Chapter from the book: Başarır, Ç. & Yılmaz, Ö. (eds.) 2024. The Transformative Power of Technology in Social Sciences: New Interdisciplinary Approaches.

Ömer Faruk Seymen
Sakarya University

Synopsis

Machine learning (ML) presents a significant opportunity to enhance the understanding and prediction of social behavior through the analysis of large datasets. This technology surpasses traditional research methodologies and provides innovative solutions to various social challenges, including health disparities, economic trends, voting behavior, and the detection of misinformation. By utilizing data from diverse sources such as social media, surveys, public databases, and sensors, ML facilitates the identification of social trends, sentiment analysis, and the anticipation of public needs. Social media, in particular, plays a crucial role in social analysis by generating data that reflects trends and consumer feedback. Machine learning employs a range of data sources to gain deeper insights into social behavior and emerging trends. Furthermore, technologies like the Internet of Things (IoT) support marketing and product development processes by offering real-time data insights. However, it is imperative to address the ethical concerns associated with the implementation of this technology. The opacity of algorithms and the practices related to digital tracking could pose risks to individual freedoms. Moreover, the potential for social media platforms to be utilized as vehicles for manipulation raises significant ethical questions. It is essential to approach the social ramifications of machine learning with careful consideration and rigorous analysis.

How to cite this book

Seymen, Ö. F. (2024). Machine Learning in Social Analysis. In: Başarır, Ç. & Yılmaz, Ö. (eds.), The Transformative Power of Technology in Social Sciences: New Interdisciplinary Approaches. Özgür Publications. DOI: https://doi.org/10.58830/ozgur.pub587.c2428

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Published

December 23, 2024

DOI