MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili

• URL: http://arxiv.org/abs/2408.03468v2
• Date: Mon, 12 Aug 2024 06:01:33 GMT
• Title: MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili
• Authors: Han Wang, Tan Rui Yang, Usman Naseem, Roy Ka-Wei Lee,
• Abstract summary: This study presents MultiHateClip, a novel multilingual dataset created through hate lexicons and human annotation. It aims to enhance the detection of hateful videos on platforms such as YouTube and Bilibili, including content in both English and Chinese languages.
• Score: 11.049937698021054
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hate speech is a pressing issue in modern society, with significant effects both online and offline. Recent research in hate speech detection has primarily centered on text-based media, largely overlooking multimodal content such as videos. Existing studies on hateful video datasets have predominantly focused on English content within a Western context and have been limited to binary labels (hateful or non-hateful), lacking detailed contextual information. This study presents MultiHateClip1 , an novel multilingual dataset created through hate lexicons and human annotation. It aims to enhance the detection of hateful videos on platforms such as YouTube and Bilibili, including content in both English and Chinese languages. Comprising 2,000 videos annotated for hatefulness, offensiveness, and normalcy, this dataset provides a cross-cultural perspective on gender-based hate speech. Through a detailed examination of human annotation results, we discuss the differences between Chinese and English hateful videos and underscore the importance of different modalities in hateful and offensive video analysis. Evaluations of state-of-the-art video classification models, such as VLM, GPT-4V and Qwen-VL, on MultiHateClip highlight the existing challenges in accurately distinguishing between hateful and offensive content and the urgent need for models that are both multimodally and culturally nuanced. MultiHateClip represents a foundational advance in enhancing hateful video detection by underscoring the necessity of a multimodal and culturally sensitive approach in combating online hate speech.

Related papers

• Advancing Content Moderation: Evaluating Large Language Models for Detecting Sensitive Content Across Text, Images, and Videos [0.1399948157377307]
  Governments, educators, and parents are often at odds with media platforms about how to regulate, control, and limit the spread of such content. Techniques from natural language processing and computer vision have been used widely to automatically identify and filter out sensitive content. More sophisticated algorithms for understanding the context of both text and image may open rooms for improvement in content censorship.
  arXiv  Detail & Related papers  (2024-11-26T05:29:18Z)
• MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval [57.891157692501345]
  $textbfMultiVENT 2.0$ is a large-scale, multilingual event-centric video retrieval benchmark. It features a collection of more than 218,000 news videos and 3,906 queries targeting specific world events. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task.
  arXiv  Detail & Related papers  (2024-10-15T13:56:34Z)
• Lexical Squad@Multimodal Hate Speech Event Detection 2023: Multimodal Hate Speech Detection using Fused Ensemble Approach [0.23020018305241333]
  We present our novel ensemble learning approach for detecting hate speech, by classifying text-embedded images into two labels, namely "Hate Speech" and "No Hate Speech" Our proposed ensemble model yielded promising results with 75.21 and 74.96 as accuracy and F-1 score (respectively)
  arXiv  Detail & Related papers  (2023-09-23T12:06:05Z)
• A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot [67.00455874279383]
  We propose verbalizing long videos to generate descriptions in natural language, then performing video-understanding tasks on the generated story as opposed to the original video. Our method, despite being zero-shot, achieves significantly better results than supervised baselines for video understanding. To alleviate a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science on persuasion strategy identification.
  arXiv  Detail & Related papers  (2023-05-16T19:13:11Z)
• HateMM: A Multi-Modal Dataset for Hate Video Classification [8.758311170297192]
  We build deep learning multi-modal models to classify the hate videos and observe that using all the modalities improves the overall hate speech detection performance. Our work takes the first step toward understanding and modeling hateful videos on video hosting platforms such as BitChute.
  arXiv  Detail & Related papers  (2023-05-06T03:39:00Z)
• 3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos [72.69052180249598]
  We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages. We show how the social media content in 3MASSIV is dynamic and temporal in nature, which can be used for semantic understanding tasks and cross-lingual analysis.
  arXiv  Detail & Related papers  (2022-03-28T02:47:01Z)
• Emotion Based Hate Speech Detection using Multimodal Learning [0.0]
  This paper proposes the first multimodal deep learning framework to combine the auditory features representing emotion and the semantic features to detect hateful content. Our results demonstrate that incorporating emotional attributes leads to significant improvement over text-based models in detecting hateful multimedia content.
  arXiv  Detail & Related papers  (2022-02-13T05:39:47Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training [79.88705563918413]
  We propose a novel video-language understanding framework named VICTOR, which stands for VIdeo-language understanding via Contrastive mulTimOdal pRe-training. VICTOR is trained on a large-scale Chinese video-language dataset, including over 10 million complete videos with corresponding high-quality textual descriptions.
  arXiv  Detail & Related papers  (2021-04-19T15:58:45Z)
• Leveraging Multilingual Transformers for Hate Speech Detection [11.306581296760864]
  We leverage state of the art Transformer language models to identify hate speech in a multilingual setting. With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages.
  arXiv  Detail & Related papers  (2021-01-08T20:23:50Z)
• VIOLIN: A Large-Scale Dataset for Video-and-Language Inference [103.7457132841367]
  We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text. Given a video clip with subtitles aligned as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. A new large-scale dataset, named Violin (VIdeO-and-Language INference), is introduced for this task, which consists of 95,322 video-hypothesis pairs from 15,887 video clips.
  arXiv  Detail & Related papers  (2020-03-25T20:39:05Z)

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.