M
Mert Can Çakmak Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
AI Researcher
faculty
8 h-index
27 pubs
126 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Mert Can Çakmak's research at the University of Arkansas at Little Rock focuses on analyzing biases within online platforms, particularly YouTube's recommendation algorithms. His work investigates how these systems influence content exposure, examining factors such as emotion, morality, and network dynamics, with specific studies looking at China-Uyghur content and YouTube Shorts recommendations. Çakmak also explores methods to improve the efficiency of social media research, including the adoption of parallel processing for rapid transcript generation in multimedia-rich online environments. His scholarship metrics include an h-index of 8, with 26 total publications and 122 total citations. Key collaborators include Nitin Agarwal, Diwash Poudel, Billy Spann, and Obianuju Okeke, all from the University of Arkansas at Little Rock, with whom he has co-authored multiple publications.
Metrics
- h-index: 8
- Publications: 27
- Citations: 126
Selected Publications
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Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations (2026)
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Policy-Aware Generative AI for Safe, Auditable Data Access Governance (2025)
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Investigating Algorithmic Bias in YouTube Shorts (2025)
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Beyond the Click: How YouTube Thumbnails Shape User Interaction and Algorithmic Recommendations (2025)
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Influence of symbolic content on recommendation bias: analyzing YouTube’s algorithm during Taiwan’s 2024 election (2025)
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Examining the Impact of Symbolic Content on YouTube’s Recommendation System (2025)
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Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics (2024)
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The bias beneath: analyzing drift in YouTube’s algorithmic recommendations (2024)
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Emotion Assessment of YouTube Videos using Color Theory (2024)
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Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube (2024)
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High-Speed Transcript Collection on Multimedia Platforms: Advancing Social Media Research through Parallel Processing (2024)
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Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse (2024)
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Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content (2024)
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Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm (2023)
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Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment (2023)
Collaboration Network
Top Collaborators
Nitin Agarwal
16 shared publications
In database
- Emotion Assessment of YouTube Videos using Color Theory
- Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- The bias beneath: analyzing drift in YouTube’s algorithmic recommendations
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
Showing 5 of 16 shared publications
Diwash Poudel
University of Arkansas at Little Rock
7 shared publications
In database
- Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics
- Examining the Impact of Symbolic Content on YouTube’s Recommendation System
- Beyond the Click: How YouTube Thumbnails Shape User Interaction and Algorithmic Recommendations
- PRISM: Perceptual Recognition for Identifying Standout Moments in Human-Centric Keyframe Extraction
- Investigating Algorithmic Bias in YouTube Shorts
Showing 5 of 7 shared publications
Billy Spann
6 shared publications
In database
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Toward Designing Effective Warning Labels for Health Misinformation on Social Media
- Characterizing Multimedia Adoption and its Role on Mobilization in Social Movements
Showing 5 of 6 shared publications
Obianuju Okeke
Cosmos Corporation (United States)
4 shared publications
In database
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse
Muzakkiruddin Ahmed Mohammed
4 shared publications
- Multi-Agent RAG Framework for Entity Resolution: Advancing Beyond Single-LLM Approaches with Specialized Agent Coordination
- Policy-Aware Generative AI for Safe, Auditable Data Access Governance
- Case Count Metric for Comparative Analysis of Entity Resolution Results
- Case Count Metric for Comparative Analysis of Entity Resolution Results
Mainuddin Shaik
3 shared publications
In database
- Emotion Assessment of YouTube Videos using Color Theory
- Toward Designing Effective Warning Labels for Health Misinformation on Social Media
- Characterizing Multimedia Adoption and its Role on Mobilization in Social Movements
Ugochukwu Onyepunuka
Cosmos Corporation (United States)
3 shared publications
In database
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse
S. Dağtaş
University of Arkansas at Little Rock
3 shared publications
In database
- Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics
- Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations
- Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations
Nitin Agarwal
3 shared publications
- Examining the Impact of Symbolic Content on YouTube’s Recommendation System
- Investigating Algorithmic Bias in YouTube Shorts
- A Keyframe-Based Approach for Auditing Bias in YouTube Shorts Recommendations
John R. Talburt
3 shared publications
- Policy-Aware Generative AI for Safe, Auditable Data Access Governance
- Case Count Metric for Comparative Analysis of Entity Resolution Results
- Case Count Metric for Comparative Analysis of Entity Resolution Results
Onais Khan Mohammed
2 shared publications
- Case Count Metric for Comparative Analysis of Entity Resolution Results
- Case Count Metric for Comparative Analysis of Entity Resolution Results
Khizer Syed
2 shared publications
- Case Count Metric for Comparative Analysis of Entity Resolution Results
- Case Count Metric for Comparative Analysis of Entity Resolution Results
Leon Claasssens
2 shared publications
- Case Count Metric for Comparative Analysis of Entity Resolution Results
- Case Count Metric for Comparative Analysis of Entity Resolution Results
Eren Alp
University of Southern Denmark (DK)
1 shared publication
- Covid-19 and Vaccine Tweet Analysis
Bedirhan Gergin
University of Calgary (CA)
1 shared publication
- Covid-19 and Vaccine Tweet Analysis
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