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<p>This bibliography contains a list of the papers that have been published using data from the Multimedia Evaluation Benchmark (MediaEval). It includes not only the papers from the proceedings of the yearly MediaEval workshop, but also conference and journal papers that have been published, as well as some theses. So far, we found 1155 papers that use data from MediaEval.</p>
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<p>This bibliography contains a list of the papers that have been published using data from the Multimedia Evaluation Benchmark (MediaEval). It includes not only the papers from the proceedings of the yearly MediaEval workshop, but also conference and journal papers that have been published, as well as some theses. So far, we found 1156 papers that use data from MediaEval.</p>
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<p>If you don’t see your paper here and would like to have it included, please get in touch with Mihai Gabriel Constantin: mihai.constantin84 (at) upb.ro.</p>
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<ulclass="bibliography"><li><spanid="fong2025theory">Fong, H., Kumar, V., & Sudhir, K. (2025). A theory-based explainable deep learning architecture for music emotion. <i>Marketing Science</i>, <i>44</i>(1), 196–219.</span></li>
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<ulclass="bibliography"><li><spanid="flaccavento:hal-04876532">Flaccavento, A., Peskine, Y., Papotti, P., Torlone, R., & Troncy, R. (2025, January). Automated Detection of Tropes In Short Texts. <i>COLING 2025, 31st International Conference on Computational Linguistics</i>. https://hal.science/hal-04876532</span></li>
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<li><spanid="fong2025theory">Fong, H., Kumar, V., & Sudhir, K. (2025). A theory-based explainable deep learning architecture for music emotion. <i>Marketing Science</i>, <i>44</i>(1), 196–219.</span></li>
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<li><spanid="wang2025fake">Wang, X., Meng, J., Zhao, D., Meng, X., & Sun, H. (2025). Fake news detection based on multi-modal domain adaptation. <i>Neural Computing and Applications</i>, <i>37</i>(7), 5781–5793.</span></li>
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<li><spanid="qiang2025mle">Qiang, R., Zhuang, Y., Singh, A., Liang, P., Zhang, C., Yang, S., & Dai, B. (2025). MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline. <i>ArXiv Preprint ArXiv:2510.07307</i>.</span></li>
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<li><spanid="shan2024fake">Shan, F., Liu, M., Zhang, M., & Wang, Z. (2024). Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network. <i>Computers, Materials & Continua</i>, <i>80</i>(1).</span></li>
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<metaproperty="twitter:title" content="FakeNews: Corona virus and 5G conspiracy" />
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<metaproperty="twitter:title" content="Emotional Mario: Believable AI agents in video games" />
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<metaproperty="twitter:title" content="Insight for Wellbeing: Multimodal personal health lifelog data analysis" />
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<metaproperty="twitter:title" content="Predicting Media Memorability" />
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<metaproperty="twitter:title" content="Emotions and Themes in Music" />
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<metaproperty="twitter:title" content="NewsImages: The role of images in online news" />
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