Mert Can Çakmak Source Confirmed

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

AI Researcher

University of Arkansas at Little Rock

faculty

8 h-index 27 pubs 122 cited

Research Areas

Natural Language Processing Techniques Misinformation and Its Impacts Media Studies and Communication Advanced Neural Network Applications Social Media and Politics Decision-Making and Behavioral Economics

Links

ORCID Lab Website

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OverviewAI-generated summary

Mert Can Çakmak is an AI researcher at the University of Arkansas at Little Rock. His work focuses on analyzing online information environments, particularly YouTube, to understand algorithmic biases and user behavior. Recent publications investigate bias in YouTube's recommendation systems, including drift, content related to China-Uyghur relations, and thumbnail recommendations on YouTube Shorts. Çakmak also studies methods for improving the efficiency of social media research, such as adopting parallel processing for rapid transcript generation in multimedia-rich environments. His research network includes frequent collaborators Nitin Agarwal, Diwash Poudel, Billy Spann, and Obianuju Okeke, all from the University of Arkansas at Little Rock, with whom he has co-authored numerous publications. Çakmak's scholarship metrics include an h-index of 8, 27 total publications, and 122 total citations.

Metrics

  • h-index: 8
  • Publications: 27
  • Citations: 122

Selected Publications

  • Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations (2026) DOI
  • Policy-Aware Generative AI for Safe, Auditable Data Access Governance (2025) DOI
  • Investigating Algorithmic Bias in YouTube Shorts (2025) DOI
  • Beyond the Click: How YouTube Thumbnails Shape User Interaction and Algorithmic Recommendations (2025) DOI
  • Influence of symbolic content on recommendation bias: analyzing YouTube’s algorithm during Taiwan’s 2024 election (2025) DOI
  • Examining the Impact of Symbolic Content on YouTube’s Recommendation System (2025) DOI
  • Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics (2024) DOI
  • The bias beneath: analyzing drift in YouTube’s algorithmic recommendations (2024) DOI
  • Emotion Assessment of YouTube Videos using Color Theory (2024) DOI
  • Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube (2024) DOI
  • High-Speed Transcript Collection on Multimedia Platforms: Advancing Social Media Research through Parallel Processing (2024) DOI
  • Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content (2024) DOI
  • Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm (2023) DOI
  • Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment (2023) DOI

Collaborators

Researchers in the database who share publications

Nitin Agarwal University of Arkansas at Little Rock 11 shared publications Diwash Poudel University of Arkansas at Little Rock 7 shared publications Billy Spann University of Arkansas at Little Rock 6 shared publications Obianuju Okeke University of Arkansas at Little Rock 4 shared publications Ugochukwu Onyepunuka University of Arkansas at Little Rock 3 shared publications Mainuddin Shaik University of Arkansas at Little Rock 3 shared publications S. Dağtaş University of Arkansas at Little Rock 3 shared publications Mariofanna Milanova University of Arkansas at Little Rock 1 shared publication

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