Abstract
Social research serves as a crucial instrument in understanding human behaviour, societal transformations, and policy-making processes. Over the past decade, the field has undergone a profound evolution, driven by technological advancements and the increasing complexity of social issues. The integration of big data, artificial intelligence (AI), and machine learning has revolutionized research methodologies, enabling the analysis of vast and diverse datasets with greater accuracy and efficiency. Furthermore, the adoption of mixed-methods approaches—blending qualitative and quantitative techniques—has enhanced the depth of social inquiry, providing a more holistic understanding of complex social phenomena.Despite these advancements, social research faces several pressing challenges. Ethical concerns surrounding data privacy, informed consent, and the potential misuse of sensitive information have intensified in the digital age. Algorithmic biases and the risk of perpetuating social inequalities through automated decision-making systems pose significant ethical dilemmas. Additionally, the replicability crisis, lack of transparency in research methodologies, and difficulties in maintaining interdisciplinary collaborations hinder the credibility and reliability of social research findings.This paper provides a comprehensive analysis of current trends and challenges in social research, highlighting both opportunities and obstacles in this rapidly evolving field. It explores potential solutions, such as the implementation of robust ethical guidelines, the promotion of open-access research practices, and the use of innovative methodologies to minimize bias and enhance data integrity. By critically examining these issues, this study aims to contribute to the ongoing discourse on the future of social research, ensuring its relevance, ethical soundness, and methodological rigor in an increasingly digitalized and interconnected world.