Mert Can Çakmak

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

AI Researcher

UA Little Rock

Also affiliated: University of Calgary (2022); University of Southern Denmark (2022); Istanbul Medipol University (2022); Quality Research (2025); Arkansas Department of Agriculture (2025); Cosmos Corporation (United States) (2024)

Faculty Researcher

8 h-index 27 pubs 157 cited

Research Areas

Links

ORCID Lab Website

OverviewAI-generated summary

Mert Can Çakmak is an AI researcher at the University of Arkansas at Little Rock. His work focuses on analyzing bias and drift in algorithmic recommendation systems, particularly within the YouTube platform. Çakmak has investigated how factors such as emotion, morality, and network dynamics influence recommendations, with a specific study examining China-Uyghur content. He has also explored bias in YouTube Shorts recommendations, analyzing thumbnail suggestions and topic dynamics. His research includes developing methods for efficient data processing, such as adopting parallel processing for rapid transcript generation in multimedia environments. Çakmak collaborates with researchers including Nitin Agarwal, Diwash Poudel, Billy Spann, and Obianuju Okeke, with whom he has co-authored multiple publications.

Metrics

h-index: 8
Publications: 27
Citations: 157

Selected Publications

Engineering Queryable Industrial Product Knowledge Bases from Technical Documents: A Survey of Data, Knowledge, and Retrieval Pipelines (2026)

SSRN Electronic Journal DOI OpenAlex
Positive Data Control: A Secure Architecture for LLM-Mediated Data Governance (2026)

Research Square DOI OpenAlex
Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations (2026)

Lecture notes in computer science DOI OpenAlex
Policy-Aware Generative AI for Safe, Auditable Data Access Governance (2025)

2 citations DOI OpenAlex
Investigating Algorithmic Bias in YouTube Shorts (2025)

Lecture notes in social networks 3 citations DOI OpenAlex
Beyond the Click: How YouTube Thumbnails Shape User Interaction and Algorithmic Recommendations (2025)

Lecture notes in social networks 5 citations DOI OpenAlex
Influence of symbolic content on recommendation bias: analyzing YouTube’s algorithm during Taiwan’s 2024 election (2025)

Applied Network Science 3 citations DOI OpenAlex
Examining the Impact of Symbolic Content on YouTube’s Recommendation System (2025)

Studies in computational intelligence 4 citations DOI OpenAlex
Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics (2024)

Lecture notes in computer science 9 citations DOI OpenAlex
The bias beneath: analyzing drift in YouTube’s algorithmic recommendations (2024)

Social Network Analysis and Mining 23 citations DOI OpenAlex
Emotion Assessment of YouTube Videos using Color Theory (2024)

19 citations DOI OpenAlex
Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube (2024)

18 citations DOI OpenAlex
High-Speed Transcript Collection on Multimedia Platforms: Advancing Social Media Research through Parallel Processing (2024)

12 citations DOI OpenAlex
Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse (2024)

Research Square DOI OpenAlex
Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content (2024)

Studies in computational intelligence 10 citations DOI OpenAlex