Prince Jha, Raghav Jain, Konika Mandal, Aman Chadha, Sriparna Saha, Pushpak Bhattacharyya. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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.

The prevalence of complaints submitted online and the sheer volume of information made available by social media platforms highlight the need for automated complaint analysis tools. In linguistic studies, complaints have been classified according to how much of personal risk the complainant is willing to take. This is crucial information for understanding the motivations of complainants and how people come up with reasonable means of reparation. Few attempts have been made to use the existing multimodal complaint model, which focuses on improving the textual mode with the help of images, to find specific visual features that help identify complaints. Our aim is to find a solution to this problem. In order to detect complaints and the severity level associated with them in a multitask setting, we propose Multimodal framEwork for Complaint and Severity-level detectIon (MECSI), a novel multimodal framework that uses local and global attributes (in both modalities) and relates them to the textual context. To do this, we add severity-level annotation to the newly released CESAMARD dataset, which is a compilation of reviews and images of products listed on the Amazon website. The experimental findings confirmed the superiority of our proposed model over the state-of-the-art model and other strong rival baselines, proving the efficacy of our proposed framework.

Complaining is an illocutionary act in which the speaker communicates his/her dissatisfaction with a set of circumstances and holds the hearer (the complainee) answerable, directly or indirectly.Considering breakthroughs in machine learning approaches, the complaint detection task has piqued the interest of the natural language processing (NLP) community.Most of the earlier studies failed to justify their findings, necessitating the adoption of interpretable models that can explain the model's output in real-time.We introduce an explainable complaint dataset, X-CI, the first benchmark dataset for explainable complaint detection.Each instance in the X-CI dataset is annotated with five labels: complaint label, emotion label, polarity label, complaint severity level, and rationale (explainability), i.e., the causal span explaining the reason for the complaint/noncomplaint label.We address the task of explainable complaint detection and propose a commonsense-aware unified generative framework by reframing the multitask problem as a text-to-text generation task.Our framework can predict the complaint cause, severity level, emotion, and polarity of the text in addition to detecting whether it is a complaint or not.We further establish the advantages of our proposed model on various evaluation metrics over the state-of-the-art models and other baselines when applied to the X-CI dataset in both full and few-shot settings 1 .

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.

Although a wide range of applications have been proposed in the field of multimodal natural language processing, very few works have been tackling multimodal relational lexical semantics. In this paper, we propose the first attempt to identify lexico-semantic relations with visual clues, which embody linguistic phenomena such as synonymy, co-hyponymy or hypernymy. While traditional methods take advantage of the paradigmatic approach or/and the distributional hypothesis, we hypothesize that visual information can supplement the textual information, relying on the apperceptum subcomponent of the semiotic textology linguistic theory. For that purpose, we automatically extend two gold-standard datasets with visual information, and develop different fusion techniques to combine textual and visual modalities following the patch-based strategy. Experimental results over the multimodal datasets show that the visual information can supplement the missing semantics of textual encodings with reliable performance improvements.

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.