Measuring Similarity, Credibility and Value of Information Content of Google and Generative AI Platforms
Preethi Pichappan, Pit Pichappan

Journal of Digital Information Management, Vol. 23, No. 3, 2025.
DOI: https://doi.org/10.6025/jdim/2025/23/3/149-170
Link: https://www.dline.info/fpaper/jdim/v23i3/jdimv23i3_1.pdf
Abstract:
This study examines the similarity, credibility, and value of information content generated by Google and
various Generative AI (GAI) platforms, such as ChatGPT, Qwen, Perplexity, and Poe. Using the Jaccard
Similarity Index, the study measures content overlap between Google search results and AI-generated responses,
resulting in two clusters. The first one is the low correlation between the Google and AI outputs.
Second is the consistency among AI-based models in terms of similarity. Thus, AI platforms reflect higher
internal similarity, suggesting a high degree of homogeneity among them. The research also evaluates
credibility, finding Google to have higher average credibility scores (0.82) compared to Perplexity (0.59) in
reference analysis, with GAI platforms often lacking reliable citations and traceability. Content credibility
scores, assessed using criteria such as source credibility, content accuracy, completeness, objectivity, and
verifiability, indicate that Google excels the AI platforms. Google scored 6.54, whereas Perplexity, Qwen,
ChatGPT, and Poe scored 4.72, 3.75, 2.81, and 2.26, respectively, in user evaluation. In terms of credibility,
Google surpasses generative AI platforms; while AI tools provide useful summaries, they lack the depth,
evidence base, and scholarly context found in Google search results. The study concludes that while AI
enhances communication efficiency, it raises concerns about reliability, knowledge foraging, and scientific
integrity. Future research aims to analyze content volume and length as measures of value, emphasizing the
need for transparency and human-centered AI development.