Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge

• URL: http://arxiv.org/abs/2012.12975v1
• Date: Wed, 23 Dec 2020 21:09:52 GMT
• Title: Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge
• Authors: Riza Velioglu, Jewgeni Rose
• Abstract summary: The Hateful Memes Challenge is a first-of-its-kind competition which focuses on detecting hate speech in multimodal memes. We utilize VisualBERT -- which meant to be the BERT of vision and language -- that was trained multimodally on images and captions. Our approach achieves 0.811 AUROC with an accuracy of 0.765 on the challenge test set and placed third out of 3,173 participants in the Hateful Memes Challenge.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memes on the Internet are often harmless and sometimes amusing. However, by using certain types of images, text, or combinations of both, the seemingly harmless meme becomes a multimodal type of hate speech -- a hateful meme. The Hateful Memes Challenge is a first-of-its-kind competition which focuses on detecting hate speech in multimodal memes and it proposes a new data set containing 10,000+ new examples of multimodal content. We utilize VisualBERT -- which meant to be the BERT of vision and language -- that was trained multimodally on images and captions and apply Ensemble Learning. Our approach achieves 0.811 AUROC with an accuracy of 0.765 on the challenge test set and placed third out of 3,173 participants in the Hateful Memes Challenge.

Related papers

• XMeCap: Meme Caption Generation with Sub-Image Adaptability [53.2509590113364]
  Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines. We introduce the textscXMeCap framework, which adopts supervised fine-tuning and reinforcement learning. textscXMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 3.71% and 4.82%, respectively.
  arXiv  Detail & Related papers  (2024-07-24T10:51:46Z)
• MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing [53.30190591805432]
  We introduce MemeMQA, a multimodal question-answering framework to solicit accurate responses to structured questions. We also propose ARSENAL, a novel two-stage multimodal framework to address MemeMQA.
  arXiv  Detail & Related papers  (2024-05-18T07:44:41Z)
• Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations [48.82168723932981]
  We introduce em MultiBully-Ex, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. A Contrastive Language-Image Pretraining (CLIP) approach has been proposed for visual and textual explanation of a meme.
  arXiv  Detail & Related papers  (2024-01-18T11:24:30Z)
• Mapping Memes to Words for Multimodal Hateful Meme Classification [26.101116761577796]
  Some memes take a malicious turn, promoting hateful content and perpetuating discrimination. We propose a novel approach named ISSUES for multimodal hateful meme classification. Our method achieves state-of-the-art results on the Hateful Memes Challenge and HarMeme datasets.
  arXiv  Detail & Related papers  (2023-10-12T14:38:52Z)
• DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation [72.18912216025029]
  We present DisinfoMeme to help detect disinformation memes. The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism.
  arXiv  Detail & Related papers  (2022-05-25T09:54:59Z)
• Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge [10.775419935941008]
  We assess the efficacy of current state-of-the-art multimodal machine learning models toward hateful meme detection. We use two benchmark datasets comprising 12,140 and 10,567 images from 4chan's "Politically Incorrect" board (/pol/) and Facebook's Hateful Memes Challenge dataset. We conduct three experiments to determine the importance of multimodality on classification performance, the influential capacity of fringe Web communities on mainstream social platforms and vice versa.
  arXiv  Detail & Related papers  (2022-02-17T07:52:22Z)
• Detecting Harmful Memes and Their Targets [27.25262711136056]
  We present HarMeme, the first benchmark dataset, containing 3,544 memes related to COVID-19. In the first stage, we labeled a meme as very harmful, partially harmful, or harmless; in the second stage, we further annotated the type of target(s) that each harmful meme points to. The evaluation results using ten unimodal and multimodal models highlight the importance of using multimodal signals for both tasks.
  arXiv  Detail & Related papers  (2021-09-24T17:11:42Z)
• Caption Enriched Samples for Improving Hateful Memes Detection [78.5136090997431]
  The hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Both unimodal language models and multimodal vision-language models cannot reach the human level of performance.
  arXiv  Detail & Related papers  (2021-09-22T10:57:51Z)
• Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset [47.65948529524281]
  We collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild differ in two key aspects: 1) Captions must be extracted via OCR, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background.
  arXiv  Detail & Related papers  (2021-07-09T09:04:05Z)
• Multimodal Learning for Hateful Memes Detection [6.6881085567421605]
  We propose a novel method that incorporates the image captioning process into the memes detection process. Our model achieves promising results on the Hateful Memes Detection Challenge.
  arXiv  Detail & Related papers  (2020-11-25T16:49:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.