With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media. Hate speech on online spaces has serious manifestations, including social polarization and hate crimes. While prior works have proposed automated techniques to detect hate speech online, these techniques primarily fail to look beyond the textual content. Moreover, few attempts have been made to focus on the aspects of interpretability of such models given the social and legal implications of incorrect predictions. In this work, we propose a deep neural multi-modal model that can: (a) detect hate speech by effectively capturing the semantics of the text along with socio-cultural context in which a particular hate expression is made, and (b) provide interpretable insights into decisions of our model. By performing a thorough evaluation of different modeling techniques, we demonstrate that our model is able to outperform the existing state-of-the-art hate speech classification approaches. Finally, we show the importance of social and cultural context features towards unearthing clusters associated with different categories of hate.
HatePrototypes: Interpretable and Transferable Representations for Implicit and Explicit Hate Speech Detection
Laboratoire Hubert Curien, UMR CNRS 5516（于贝尔·居里实验室，法国国家科学研究中心5516联合研究单位）；Université Lumière Lyon 2（里昂第二大学）；Université Claude Bernard Lyon 1（克洛德·贝尔纳里昂第一大学）；ERIC, Lyon（里昂ERIC实验室）；École Centrale de Lyon（里昂中央理工学院）；LIRIS CNRS UMR 5205（LIRIS实验室，法国国家科学研究中心5205联合研究单位）
School of Computer Sciences, NISER（国家科学教育与研究院计算机科学学院）；Homi Bhabha National Institute（霍米·巴巴国家研究所）
Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models based on a particular modality of hate speech post on social media are not useful, rather, we need models like multi-modal fusion models that consider both image and text while classifying hate speech. Text-image fusion models are heavily p…
In recent years, monitoring hate speech and offensive language on social media platforms has become paramount due to its widespread usage among all age groups, races, and ethnicities. Consequently, there have been substantial research efforts towards automated detection of such content using Natural Language Processing (NLP). While successfully filtering textual data, no research has focused on detecting hateful content in multimedia data. With increased ease of data storage and the exponential growth of social media platforms, multimedia content proliferates the internet as much as text data.…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
his paper describes our techniques to detect hate speech against women and immigrants on Twitter in multilingual contexts, particularly in English and Spanish. The challenge was designed by SemEval-2019 Task 5, where the participants need to design algorithms to detect hate speech in English and Spanish language with a given target (e.g., women or immigrants). Here, we have developed two deep neural networks (Bidirectional Gated Recurrent Unit (GRU), Character-level Convolutional Neural Network (CNN)), and one machine learning model by exploiting the linguistic features. Our proposed model obt…
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；RWTH Aachen University（亚琛工业大学）；Noakhali Science and Technology University（诺亚克利科技大学）；Begum Rokeya University（贝古姆·罗凯亚大学）；The University of Alabama（阿拉巴马大学）；National University of Ireland, Galway（爱尔兰国立高威大学）；Islamic University of Technology（伊斯兰科技大学）
The exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behaviour like online harassment, cyberbullying, and hate speech. Numerous works have been proposed to utilize textual data for social and anti-social behaviour analysis, by predicting the contexts mostly for highly-resourced languages like English. However, some languages are under-resourced, e.g., South Asian languages like Bengali, that lack computational resources for accurate natural language…
The spread of hatred that was formerly limited to verbal communications has rapidly moved over the Internet. Social media and community forums that allow people to discuss and express their opinions are becoming platforms for the spreading of hate messages. Many countries have developed laws to avoid online hate speech. They hold the companies that run the social media responsible for their failure to eliminate hate speech. But as online content continues to grow, so does the spread of hate speech However, manual analysis of hate speech on online platforms is infeasible due to the huge amount …
Irvington High School（欧文顿高中）；Massachusetts Institute of Technology（麻省理工学院）
Hateful memes are an emerging method of spreading hate on the internet, relying on both images and text to convey a hateful message. We take an interpretable approach to hateful meme detection, using machine learning and simple heuristics to identify the features most important to classifying a meme as hateful. In the process, we build a gradient-boosted decision tree and an LSTM-based model that achieve comparable performance (73.8 validation and 72.7 test auROC) to the gold standard of humans and state-of-the-art transformer models on this challenging task.
TANDEM: Temporal-Aware Neural Detection for Multimodal Hate Speech
Nature-Inspired Computing & Engineering, University of Surrey（Surrey大学自然启发计算与工程系）；Surrey Centre for Cyber Security, University of Surrey（Surrey大学网络安全中心）
The rise of hate speech on online platforms has led to an urgent need for effective content moderation. However, the subjective and multi-faceted nature of hateful online content, including implicit hate speech, poses significant challenges to human moderators and content moderation systems. To address this issue, we developed ToxVis, a visually interactive and explainable tool for classifying hate speech into three categories: implicit, explicit, and non-hateful. We fine-tuned two transformer-based models using RoBERTa, XLNET, and GPT-3 and used deep learning interpretation techniques to prov…
The (((echo))) symbol -- triple parenthesis surrounding a name, made it to mainstream social networks in early 2016, with the intensification of the U.S. Presidential race. It was used by members of the alt-right, white supremacists and internet trolls to tag people of Jewish heritage -- a modern incarnation of the infamous yellow badge (Judenstern) used in Nazi-Germany. Tracking this trending meme, its meaning, and its function has proved elusive for its semantic ambiguity (e.g., a symbol for a virtual hug). In this paper we report of the construction of an appropriate dataset allowing the re…
Self-Explaining Hate Speech Detection with Moral Rationales
University of São Paulo（圣保罗大学）；University of Southern California（南加州大学）；Saarland University（萨尔兰大学）；Virginia Tech（弗吉尼亚理工大学）；University of Melbourne（墨尔本大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Technical University of Munich（慕尼黑技术大学）；Google Research（谷歌研究）；Portland State University（波特兰州立大学）
Universitat Politecnica de Catalunya - UPC（加泰罗尼亚理工大学（UPC））
This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is f…
Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI
Department of Computer Science（计算机科学系）；University of South Carolina（南卡罗来纳大学）；Department of Civil, Environmental, and Ocean Engineering（土木、环境与海洋工程系）；Stevens Institute of Technology（史蒂文斯理工学院）；School of Computing（计算学院）；University of Nebraska–Lincoln（内布拉斯加大学林肯分校）；Bowling Green State University（鲍灵格林州立大学）
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；RWTH Aachen University（亚琛工业大学）；Noakhali Science and Technology University（诺阿卡利科技大学）；University of Siegen（锡根大学）；University of Galway（戈尔韦大学）
Numerous machine learning (ML) and deep learning (DL)-based approaches have been proposed to utilize textual data from social media for anti-social behavior analysis like cyberbullying, fake news detection, and identification of hate speech mainly for highly-resourced languages such as English. However, despite having a lot of diversity and millions of native speakers, some languages like Bengali are under-resourced, which is due to a lack of computational resources for natural language processing (NLP). Similar to other languages, Bengali social media contents also include images along with t…
Indraprastha Institute of Information Technology Delhi（德里英德拉普拉斯塔信息技术学院）；Wipro AI Labs（威普罗人工智能实验室）；Birla Institute of Technology and Science, Goa（比尔拉科学技术学院果阿分院）；Indian Institute of Technology Delhi（印度理工学院德里分校）
Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications. This has isolated progress for imperative multi-modal applications that are diverse in terms of complexity and domain-affinity, such as meme analysis. Here, we introduce two self-supervised pre-training methods, namely Ext-PIE-Net and MM-SimCLR that (i) employ off-the-shelf multi-modal hate-speech data during pre-training and (ii) perform self-supervised learning by incorporating multiple specialized pretext tasks, effe…
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. M…
FernUniversität in Hagen（哈根远程大学）；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Exploiting social media to spread hate has tremendously increased over the years. Lately, multi-modal hateful content such as memes has drawn relatively more traction than uni-modal content. Moreover, the availability of implicit content payloads makes them fairly challenging to be detected by existing hateful meme detection systems. In this paper, we present a use case study to analyze such systems' vulnerabilities against external adversarial attacks. We find that even very simple perturbations in uni-modal and multi-modal settings performed by humans with little knowledge about the model ca…
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models. We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022. We observe while the text-based model MURIL performs better for N…
In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by ana…
HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse
Indian Institute of Information Technology Dharwad（印度信息技术学院达尔瓦德）；Manipal Institute of Technology, Manipal Academy of Higher Education（曼普及高等教育学院技术学院）；Indraprastha Institute of Information Technology Delhi（印度普拉斯塔信息技术学院德里）
Anti-Defamation League（反诽谤联盟）；The Alan Turing Institute（艾伦·图灵研究所）；University of Oxford（牛津大学）；Meedan
Accurate detection and classification of online hate is a difficult task. Implicit hate is particularly challenging as such content tends to have unusual syntax, polysemic words, and fewer markers of prejudice (e.g., slurs). This problem is heightened with multimodal content, such as memes (combinations of text and images), as they are often harder to decipher than unimodal content (e.g., text alone). This paper evaluates the role of semantic and multimodal context for detecting implicit and explicit hate. We show that both text- and visual- enrichment improves model performance, with the mult…
Exploring Large Language Models for Hate Speech Detection in Rioplatense Spanish
Instituto de Cs. de la Computación（计算机科学研究所）；CONICET（阿根廷科学技术研究委员会）；UBA（布宜诺斯艾利斯大学）；Instituto Gino Germani（吉诺·格尔马研究所）；FSOC（社会科学学院）；Departamento de Computación, FCEN UBA（布宜诺斯艾利斯大学精确与自然科学学院计算机系）
Singapore University of Design and Technology（新加坡设计与科技大学）；Singapore Management University（新加坡管理大学）；University of Electronic Science and Technology of China（电子科技大学）
Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multi…
MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Sichuan University（四川大学）；Northwestern University（西北大学）；Indiana University（印第安纳大学）；Emory University（埃默里大学）；Nankai University（南开大学）；MountainLion Research（MountainLion研究机构）；Xi’an University of Electronic Science and Technology（西安电子科技大学）；Kyoto University（京都大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Nanjing university（南京大学）；Tsinghua University（清华大学）；University of Toronto（多伦多大学）
