Browsing by Author "Naseem, Usman"

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    CHUNAV: Analyzing Hindi Hate Speech and Targeted Groups in Indian Election Discourse
    Jafri, Farhan; Rauniyar, Kritesh; Thapa, Surendrabikram; Siddiqui, Mohammad; Khushi, Matloob; Naseem, Usman (ACM, 2024)
    In the ever-evolving landscape of online discourse and political dialogue, the rise of hate speech poses a significant challenge to maintaining a respectful and inclusive digital environment. The context becomes particularly complex when considering the Hindi language—a low-resource language with limited available data. To address this pressing concern, we introduce the CHUNAV dataset—a collection of 11,457 Hindi tweets gathered during assembly elections in various states. CHUNAV is purpose-built for hate speech categorization and the identification of target groups. The dataset is a valuable resource for exploring hate speech within the distinctive socio-political context of Indian elections. The tweets within CHUNAV have been meticulously categorized into "Hate" and "Non-Hate" labels, and further subdivided to pinpoint the specific targets of hate speech, including "Individual", "Organization", and "Community" labels (as shown in Figure 1). Furthermore, this paper presents multiple benchmark models for hate speech detection, along with an innovative ensemble and oversampling-based method. The paper also delves into the results of topic modeling, all aimed at effectively addressing hate speech and target identification in the Hindi language. This contribution seeks to advance the field of hate speech analysis and foster a safer and more inclusive online space within the distinctive realm of Indian Assembly Elections. The dataset is available at https://github.com/Farhan-jafri/Chunav
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    MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation
    Naseem, Usman; Thapa, Surendrabikram; Zhang, Qi; Hu, Liang; Nasim, Mehwish (ACM, 2023-07-19)
    Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.
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    RUHate-MM: Identification of Hate Speech and Targets using Multimodal Data from Russia-Ukraine Crisis
    Thapa, Surendrabikram; Jafri, Farhan; Rauniyar, Kritesh; Nasim, Mehwish; Naseem, Usman (ACM, 2024-05-13)
    During the conflict between Ukraine and Russia, hate speech targeted toward specific groups was widespread on different social media platforms. With most social platforms allowing multimodal content, the use of multimodal content to express hate speech is widespread on the Internet. Although there has been considerable research in detecting hate speech within unimodal content, the investigation into multimodal content remains insufficient. The limited availability of annotated multimodal datasets further restricts our ability to explore new methods to interpret and identify hate speech and its targets. The availability of annotated datasets for hate speech detection during political events, such as invasions, are even limited. To fill this gap, we introduce a comprehensive multimodal dataset consisting of 20,675 posts related to the Russia- Ukraine crisis, which were manually annotated as either ‘Hate Speech’ or ‘No Hate Speech’. Additionally, we categorize the hate speech data into three targets: ‘Individual’, ‘Organization’, and ‘Community’. Our benchmarked evaluations show that there is still room for improvement in accurately identifying hate speech and its targets. We hope that the availability of this dataset and the evaluations performed on it will encourage the development of new methods for identifying hate speech and its targets during political events like invasions and wars. The dataset and resources are made available at https://github.com/Farhan-jafri/Russia-Ukraine.
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    Vision-Language Models for Biomedical Applications
    Thapa, Surendrabikram; Naseem, Usman; Zhou, Luping; Kim, Jinman (ACM, 2024-10-28)
    Vision-language models (VLMs) are transforming the landscape of biomedical research and healthcare by enabling the seamless integration and interpretation of complex multimodal data, including medical images and clinical texts. Recognizing the growing impact of these models, the first international workshop on Vision- Language Models for Biomedicine (VLM4Bio) was held in conjunction with ACM Multimedia 2024. The workshop aimed to address the critical need for advanced techniques that can leverage VLMs in applications such as medical imaging, diagnostics, and personalized treatment. As healthcare data increasingly involves both visual and textual information, VLM4Bio provided a platform for interdisciplinary collaboration between experts in natural language processing, computer vision, biomedical engineering, and AI ethics. This paper provides an overview of the inaugural edition of the VLM4Bio workshop, summarizing the key discussions, contributions, and future directions for expanding the workshop’s scope and influence in subsequent editions.