AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media
| Autor: | Christina Youn, Yang Zhang, Yuheng Zha, Dong Wang, Yingxi Chen, Lanyu Shang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Modalities Relation (database) Computer science business.industry Deep learning Offensive Analogy ComputingMilieux_LEGALASPECTSOFCOMPUTING Library and Information Sciences Management Science and Operations Research Machine Learning (cs.LG) Computer Science Applications Human–computer interaction Media Technology Social media Artificial intelligence business Information Systems |
| Popis: | This paper focuses on an important problem of detecting offensive analogy meme on online social media where the visual content and the texts/captions of the meme together make an analogy to convey the offensive information. Existing offensive meme detection solutions often ignore the implicit relation between the visual and textual contents of the meme and are insufficient to identify the offensive analogy memes. Two important challenges exist in accurately detecting the offensive analogy memes: i) it is not trivial to capture the analogy that is often implicitly conveyed by a meme; ii) it is also challenging to effectively align the complex analogy across different data modalities in a meme. To address the above challenges, we develop a deep learning based Analogy-aware Offensive Meme Detection (AOMD) framework to learn the implicit analogy from the multi-modal contents of the meme and effectively detect offensive analogy memes. We evaluate AOMD on two real-world datasets from online social media. Evaluation results show that AOMD achieves significant performance gains compared to state-of-the-art baselines by detecting offensive analogy memes more accurately. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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