digital humanities

Proving the miraculous nature of the Qur’an as one of the methods to believe in the Qur’an as a divine text has always attracted the attention of the researchers of the Qur’an; nevertheless, the published efforts on this subject are facing numerous methodological challenges. This research attempts to introduce a new and original method exempt from the challenges of the present methods by utilizing some of the techniques in mathematical sciences, logic and statistics. This scientific case-based method rates the effective parameters in the recognition of scientific miracle for one case. Accordingly, it will calculate the certainty of each case and proceeds to combine the ratings of various cases of scientific miracle to calculate the degree of certainty of the divine nature of the book based on those results. In combining the ratings, a threshold will be determined based on human belief behavior. An advantage of this method is that it is extra-religious, data driven, quantitative, applicable to all texts, and generalizable to the entire content. 

The advent of big data marks a profound shift in our epistemological framework, introducing a new knowledge paradigm where the social landscape is shaped by data processing, perceived as both comprehensive and natural. This transformative shift challenges traditional notions of human agency in societal understanding, positioning empirical quantification at the forefront of inquiry. Beyond philosophical implications, pragmatic challenges abound in big data research—from issues of commensuration and the influence of action grammars to the dominance of correlational over causal relationships, the prevalence of everyday data over historical archives, and the pervasive impact of algorithms on data ecosystems. This manuscript undertakes a comprehensive exploration of these challenges, proposing strategies for navigating them within emerging disciplines such as Digital Humanities, Social Computing, and Cultural Analysis. Methodologically anchored in constructivist principles and critical discourse analysis (CDA), the study investigates how socio-cultural contexts shape data and knowledge production. Drawing on extensive literature and meta-analyses, it synthesizes diverse perspectives to underscore the necessity for methodological innovation and reflexivity in addressing the complexities of big data research, ensuring the integrity and depth of social inquiry amidst evolving data-driven methodologies.