Hate speech detection on social media faces challenges in both accuracy and explainability, especially for underexplored Indic languages. We propose a novel explainability-guided training framework, X-MuTeST (eXplainable Multilingual haTe Speech deTection), for hate speech detection that combines high-level semantic reasoning from large language models (LLMs) with traditional attention-enhancing techniques. We extend this research to Hindi and Telugu alongside English by providing benchmark human-annotated rationales for each word to justify the assigned class label. The X-MuTeST explainability method computes the difference between the prediction probabilities of the original text and those of unigrams, bigrams, and trigrams. Final explanations are computed as the union between LLM explanations and X-MuTeST explanations. We show that leveraging human rationales during training enhances both classification performance and the model’s explainability. Moreover, combining human rationales with our explainability method to refine the model’s attention yields further improvements. We evaluate explainability using Plausibility metrics such as Token-F1 and IOU-F1, and Faithfulness metrics such as Comprehensiveness and Sufficiency. By focusing on under-resourced languages, our work advances hate speech detection across diverse linguistic contexts. Our dataset includes token-level rationale annotations for 6,004 Hindi, 4,492 Telugu, and 6,334 English samples.

Mohammad Zia Ur Rehman, Anukriti Bhatnagar, Omkar Kabde, Shubhi Bansal, Dr. Nagendra Kumar. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues. To address the lack of resources and methods for detecting dark humor in multimodal content, we introduce a novel dataset of 4,379 Reddit memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating (mild, moderate, severe). Building on this resource, we propose a reasoning-augmented framework that first generates structured explanations for each meme using a Large Vision-Language Model (VLM). Through a Role-Reversal Self-Loop, VLM adopts the author's perspective to iteratively refine its explanations, ensuring completeness and alignment. We then extract textual features from both the OCR transcript and the self-refined reasoning via a text encoder, while visual features are obtained using a vision transformer. A Tri-stream Cross-Reasoning Network (TCRNet) fuses these three streams, text, image, and reasoning, via pairwise attention mechanisms, producing a unified representation for classification. Experimental results demonstrate that our approach outperforms strong baselines across three tasks: dark humor detection, target identification, and intensity prediction. The dataset, annotations, and code are released to facilitate further research in multimodal humor understanding and content moderation. Code and Dataset Access: https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning

Social media has emerged as a critical platform for disseminating real-time information during disasters. However, extracting actionable resource data, such as needs and availability, from this vast and unstructured content remains a significant challenge, leading to delays in identifying and allocating resources, with severe consequences for affected populations. This study addresses this challenge by investigating the potential of label and topic features, combined with text embeddings, to enhance the performance and efficiency of resource identification from social media data . We propose Crisis Resource Finder (CRFinder), a novel framework that leverages label encoding and topic features to extract richer contextual information, uncover hidden patterns, and reveal the true context of disaster resources. CRFinder incorporates novel techniques such as multi-level text-label attention and contrastive text-topic attention to capture semantic and thematic nuances within the textual data. Additionally, our approach employs topic injection and selective contextualization techniques to enhance thematic relevance and focus on critical information, which is pivotal for targeted relief efforts. Extensive experiments demonstrate the significant improvements achieved by CRFinder over existing state-of-the-art methods, with average weighted F1-score gains of 7.12%, 6.44%, and 7.89% on datasets from the Nepal earthquake, Italy earthquake, and Chennai floods, respectively. By providing timely and accurate insights into resource needs and availabilities, CRFinder has the potential to revolutionize disaster response efforts.

The recent coronavirus disease (COVID-19) has become a pandemic and has affected the entire globe. During the pandemic, we have observed a spike in cases related to mental health, such as anxiety, stress, and depression. Depression significantly influences most diseases worldwide, making it difficult to detect mental health conditions in people due to unawareness and unwillingness to consult a doctor. However, nowadays, people extensively use online social media platforms to express their emotions and thoughts. Hence, social media platforms are now becoming a large data source that can be utilized for detecting depression and mental illness. However, the existing approaches often overlook data sparsity in tweets and the multimodal aspects of social media. In this article, we propose a novel multimodal framework that combines textual, user-specific, and image analysis to detect depression among social media users. To provide enough context about the user’s emotional state, we propose the following: 1) an extrinsic feature by harnessing the URLs present in tweets and 2) extracting textual content present in images posted in tweets. We also extract five sets of features belonging to different modalities to describe a user. In addition, we introduce a deep learning model, the visual neural network (VNN), to generate embeddings of user-posted images, which are used to create the visual feature vector for prediction. We contribute a curated COVID-19 dataset of depressed and nondepressed users for research purposes and demonstrate the effectiveness of our model in detecting depression during the COVID-19 outbreak. Our model outperforms the existing state-of-the-art methods over a benchmark dataset by 2%–8% and produces promising results on the COVID-19 dataset. Our analysis highlights the impact of each modality and provides valuable insights into users’ mental and emotional states.