Beyond Binary Judgments: A Neutrosophic Framework for Evaluating News Writing Quality through Common and Uncommon Meaning

Abstract

The quality of news writing plays a crucial role in how societies perceive truth, 
construct narratives, and react to global events. Traditional models of news evaluation often rely 
on binary assessments  labeling content as either objective or biased, factual or opinionated. 
However, this binary lens fails to capture the nuanced reality of journalistic content, where truth 
and bias can coexist within the same text. This study introduces a novel analytical framework 
grounded in neutrosophic theory, which emphasizes the identification of common parts in 
uncommon things and uncommon parts in common things [1]. Applying this concept, the 
framework captures the overlapping and divergent elements between seemingly different or 
similar news articles. By assigning neutrosophic truth (T), indeterminacy (I), and falsehood (F) 
values to textual segments, the proposed model enables a multidimensional analysis of news 
content. A case study comparing three international media outlets is used to validate the 
framework. The results show that the neutrosophic approach provides a more refined, adaptable, 
and philosophically sound method for evaluating the quality of journalistic writing. Additionally, 
we employ the Double Valued Neutrosophic Set (DVNS) to handle uncertainty in the evaluation 
process. DVNS is integrated with the MABAC method to rank the alternatives. An illustrative 
example is presented, involving eight criteria and seven alternatives. The weights of the criteria 
are also computed.  

DOI: 10.5281/zenodo.15380974