While considerable research has delved into detecting toxic content in text-based data, the realm of video content, particularly in languages other than English, has received less attention. Prior studies have primarily focused on creating automated tools to identify online toxic speech but have often overlooked the crucial next steps of mitigating its impact and discouraging future use. We can discourage social media users from sharing such material by automatically generating interventions that explain why certain content is inappropriate. To bridge this research gap, we propose an innovative task: generating interventions for toxic videos in code-mixed languages which go beyond existing methods focusing on text and images to combat online toxicity. We are introducing a Toxic Code-Mixed Intervention Video benchmark dataset (ToxCMI), comprising 1697 code-mixed toxic video utterances sourced from YouTube. Each utterance in this dataset has been meticulously annotated for toxicity and severity, accompanied by interventions provided in Hindi-English code-mixed languages. We have developed an advanced multimodal framework ToxVI, specifically designed for the task of generating Toxic Video appropriate Interventions, leveraging Large Language Models (LLMs), which comprises three modules - Modality module, Cross-Modal Synchronization module and Generation module. Our experiments demonstrate that integrating multiple modalities from the videos significantly enhances the performance of the proposed task and outperforms all the baselines by a significant margin.

Prince Jha, Krishanu Maity, Raghav Jain, Apoorv Verma, Sriparna Saha, Pushpak Bhattacharyya. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

The escalating prevalence of online cyberbullying and trolling across various social media platforms has becomes a pressing concern. Extensive research demonstrates the detrimental impact of cyberbullying on the mental well-being of its victims. Given the sheer volume of online content, manual identification of cyberbullying instances proves unfeasible, necessitating the development of automated cyberbullying detection methods. This challenge has attracted considerable attention within the natural language processing (NLP) community, owing to advancements in machine learning techniques. However, most of the methods fail to provide reasoning for their decisions which warrants the use of interpretable models that can explain the model’s output in real-time. Interpretable models rather than black-box models with high performance are becoming popular adhering the “right to explanations” laws. Motivated by this, we create a cyberbullying corpusBullyExplainin code-mixed language, where a post has been annotated with four labels, i.e., bully, sentiment, target, and rationales (explainability). Current work addresses the task of explainable cyberbully detection and proposes a unified generative framework,BullyGenby redefining this multitask problem as a text-to-text generation task. Our framework is capable of not only detecting whether the text is a cyberbully or not but also provides reasoning by predicting rationale, target group and sentiment of the text. Experimental results illustrate the efficacy of our proposed model by outperforming the state-of-the-art and several baselines by a significant margin and conclude that text-to-text generation model could be a good alternative for multitask classification problems.

Social media platforms are a double-edged sword: on the one hand, they enable the dissemination of information; but on the other hand, they also provide an avenue for spreading online abuse and harassment, such as hate speech. While significant research efforts are being devoted to detecting online hate speech in the English language, little attention has been paid to the Thai language. In this study, we created a benchmark dataset, calledHateThaiSent, which labels each post with both hate speech and sentiment information. To detect hate speech, we created a multitask model that uses a dual-channel deep learning approach based on FastText and BERT embeddings, with an added capsule network. One channel utilizes pretrained FastText embeddings while the other uses embeddings from the BERT language model. We aimed to answer two research questions: (Q1) Does incorporating sentiment information improves the performance of hate speech detection (HD) in the Thai language? (Q2) What is the comparative effectiveness of two different approaches for sentiment-aware HD in the Thai language: feature engineering versus multitasking? Our proposed approach outperformed other baselines and state-of-the-art models on theHateThaiSentdataset, with overall accuracy/macro-F1 values of 89.67%/89.79%, and 80.92%/80.97% for hate speech and sentiment detection tasks, respectively. We concluded that multitasking is more effective than feature engineering in enhancing the performance of the main task (HD).

Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-aided cyberbullying detection (CD) from code-mixed language. The GNN helps detect unlabelled or noisy label nodes (sentences) accurately by aggregating information from similarly labeled nodes. To connect nodes, we apply cosine similarity between sentences and create a single text graph for a benchmark code-mixed cyberbullying corpus, BullySent. Experimental results illustrate that MTBullyGNN outperforms the state-of-the-art (SOTA) methods for both the single (CD) and MT (CD and sentiment) settings by up to 4.46% and 4.92% in classification accuracy, respectively. Furthermore, another benchmark Hindi–English code-mixed single-task dataset has also been considered to illustrate the robustness of our proposed model. The code will be made publicly available in the camera-ready version.

With the rise of social media and online communication, the issue of cyberbullying has gained significant prominence.While extensive research is being conducted to develop more effective models for detecting cyberbullying in monolingual languages, a significant gap exists in understanding code-mixed languages and the need for explainability in this context.To address this gap, we have introduced a novel benchmark dataset named Bully-Explain for explainable cyberbullying detection in code-mixed language.In this dataset, each post is meticulously annotated with four labels: bully, sentiment, target, and rationales, indicating the specific phrases responsible for identifying the post as a bully.Our current research presents an innovative unified generative framework, GenEx, which reimagines the multitask problem as a text-to-text generation task.Our proposed approach demonstrates its superiority across various evaluation metrics when applied to the BullyExplain dataset, surpassing other baseline models and current state-of-the-art approaches. 1

Accessibility to the internet has led to a massive increase in the usage of social networking and online communication apps over the past decade. With so much content available online, these platforms suffer from governance and monitoring problems making the users susceptible to online cyberbullying and trolling. Recently, this trolling and hate comes majorly from memes that combine text and image modalities. Many studies show how cyberbullying can harm the mental well-being of the affected individuals. Previous studies also tried to study the role of sentiment, emotions, and sarcasm in identifying hateful memes setting up the meme detection task as a multi-modal, multitask problem. In the past, meme detection models were discriminative, but more recently generative models have been used to solve non-generation tasks such as aspect-based sentiment analysis and span detection. Motivated by this, in this work, we propose a unified Multimodal Generative framework, MGex by reframing the multitasking problem of detecting cyberbullying, sentiment, emotion, and sarcasm as a multimodal text-to-text generation problem. For this purpose, we use MultiBully dataset which provides annotation for all these labels. Here, we evaluate and contrast our proposed generative framework with several multitasking baselines and state-of-the-art models. Disclaimer: The article contains offensive text and profanity. This is owing to the nature of the work and does not reflect any opinion or stand of the authors.

The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called BullySentEmo has been developed as there is no dataset available labeled with bully, sentiment, and emotion. Moreover, emoji information available with tweet texts can provide better understanding of user intention. The developed dataset consists of both modalities, tweet text, and emoji. In India, the majority of communication on different social media platforms is based on Hindi and English and language switching is a common practice in digital communication. An attention-based multimodal, adversarial multitasking framework is proposed for cyberbully detection (CBD) considering two auxiliary tasks: sentiment analysis (SA) and emotion recognition (ER). Experimental results indicate that compared to unimodal and single-task variants, the proposed framework improves the performance of the main task, i.e., CBD, by 3.59% and 2.56% in terms of accuracy and F1-Score, respectively. Furthermore, two different benchmark datasets (Twitter dataset and Aggression dataset) have been considered to show the robustness of our proposed model.

The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, and cyberbullying is one of them. In this work, we have created a benchmark corpus for cyberbullying detection in code-mixed languages. In India, most communications on different social media platforms are based on Hindi and English languages, and language switching is a common practice in digital communication. To investigate how code-mixed data can be handled effectively, BERT language model and VecMap based bilingual embedding along with a two-channel convolutional neural network model, namely BERT+VecMap-CNN, have been used. The input to one channel is the BERT language model and that to the other is the bilingual word embedding based on VecMap. As a baseline, we used standard machine learning models, as well as deep neural network models like CNN and LSTM. Our proposed model outperforms the baselines with overall accuracy and F1-measure values of 81.12%, and 81.03%, respectively. Furthermore, a different benchmark code-mixed dataset has been considered to show the robustness of our proposed model.1

Detecting cyberbullying from memes is highly challenging, because of the presence of the implicit affective content which is also often sarcastic, and multi-modality (image + text). The current work is the first attempt, to the best of our knowledge, in investigating the role of sentiment, emotion and sarcasm in identifying cyberbullying from multi-modal memes in a code-mixed language setting. As a contribution, we have created a benchmark multi-modal meme dataset called MultiBully annotated with bully, sentiment, emotion and sarcasm labels collected from open-source Twitter and Reddit platforms. Moreover, the severity of the cyberbullying posts is also investigated by adding a harmfulness score to each of the memes. The created dataset consists of two modalities, text and image. Most of the texts in our dataset are in code-mixed form, which captures the seamless transitions between languages for multilingual users. Two different multimodal multitask frameworks (BERT+ResNET-Feedback and CLIP-CentralNet) have been proposed for cyberbullying detection (CD), the three auxiliary tasks being sentiment analysis (SA), emotion recognition (ER) and sarcasm detection (SAR). Experimental results indicate that compared to uni-modal and single-task variants, the proposed frameworks improve the performance of the main task, i.e., CD, by 3.18% and 3.10% in terms of accuracy and F1 score, respectively.

To improve the generic lifestyle of a patient, remote monitoring has become an essential part of patient assistance in any health care services. Patient falls in nursing homes and hospitals are a major source of concern in any health care services. To mitigate this problem and to reduce falls, improved surveillance system which helps in activity recognition in a patient is the need of the day. This work proposes an activity recognition system using Grey Relational Analysis which considers readings from lightweight wearable sensors to detect activities in patients. To verify the feasibility of the proposed work, it has been applied on a real life data. It can be seen from the simulation results that the proposed methodology shows competitively improved results than some of the existing contemporary machine learning techniques.