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- Sivaji BandyopadhyaySuggested from co-authorship
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Works384 from public data
- 556
- Factuality challenges in the era of large language models and opportunities for fact-checking319
By analysing the factuality constraints within LLMs and their impact on fact-checking, this work aims to contribute to a path towards maintaining accuracy at a time of confluence of generative artificial intelligence and misinformation.
- Metrics for Community Analysis300
A survey of the start-of-the-art metrics used for the detection and the evaluation of community structure and a comparative analysis of these metrics in measuring the goodness of the underlying community structure is presented.
- SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)286
This work presents SpotFake+, a multimodal approach that leverages transfer learning to capture semantic and contextual information from the news articles and its associated images and achieves the better accuracy for fake news detection.
- SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!255
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes by classifying intensity of meme emotion.
- MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets231
This work proposes MOMENTA (MultimOdal framework for detecting harmful memes, a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes and outperforms several strong rivaling approaches.
- SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets208
The SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020) is presented and it is observed that BERT-like models and ensemble methods are the most common and successful approaches among the participants.
- Detecting Harmful Memes and Their Targets202
This work presents HarMeme, the first benchmark dataset, containing 3,544 memes related to COVID-19, and proposes two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
- Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection154
CTF, a large-scale COVID-19 Twitter dataset with labelled genuine and fake tweets is introduced and Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model which leverages the large amount of unlabelled data is proposed.
- Handling Bias in Toxic Speech Detection: A Survey145
A systematic study of the limitations and challenges of existing methods for mitigating bias in toxicity detection and a case study to introduce the concept of bias shift due to knowledge-based bias mitigation are conducted.
- On the permanence of vertices in network communities139
Compared to other metrics, permanence provides a more accurate estimate of a derived community structure to the ground-truth community and is more sensitive to perturbations in the network.
- We Used Neural Networks to Detect Clickbaits: You Won’t Believe What Happened Next!130
A neural network architecture based on Recurrent Neural Networks for detecting clickbaits is introduced, which relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks.
- 116
- Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts111
The findings of the shared tasks conducted at the CONSTRAINT Workshop at AAAI 2021 are presented and the most successful models were BERT or its variations.
- Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations109
A Hindi-English code-mixed dataset is developed for the multi-modal sarcasm detection and humor classification in conversational dialog, and a novel attention-rich neural architecture for the utterance classification is proposed.
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- 103
- POSIX: A Prompt Sensitivity Index For Large Language Models92
POSIX - a novel PrOmpt Sensitivity IndeX as a reliable measure of prompt sensitivity is proposed, thereby offering a more comprehensive evaluation of LLM performance and is used to measure and thereby compare prompt sensitivity of various open-source LLMs.
- Unsupervised Fake News Detection77
GTUT, a graph-based approach for unsupervised fake news detection, is developed which operates in three phases and establishes the improved effectiveness of the method over state-of-the-art techniques for the task.
- Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations71
This work develops a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations, identifies the requirement of such conversation, and proposes SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the Dialogue-Act classification.
- 63
- How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning61
This work investigates the neural sub-structures within LLMs that manifest CoT reasoning from a mechanistic point of view, and demonstrates that LLMs deploy multiple parallel pathways of answer generation for step-by-step reasoning.
- Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models58
This comprehensive survey delves into the recent strides in HS moderation, spotlighting the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) and the development of more nuanced, context-aware systems.
- Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends58
A special issue on “Exploitation of Social Media for Emergency Relief and Preparedness” was conducted for the journal Information Systems Frontiers to present a platform for dissemination of the empirical results of various technologies for extracting vital and actionable information from social media content in disaster situations.
- 56
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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