Papers by Megha Chakraborty
FACTIFY3M: A benchmark for multimodal fact verification with explainability through 5W Question-Answering (2023.emnlp-main)
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Megha Chakraborty, Khushbu Pahwa, Anku Rani, Shreyas Chatterjee, Dwip Dalal, Harshit Dave, Ritvik G, Preethi Gurumurthy, Adarsh Mahor, Samahriti Mukherjee, Aditya Pakala, Ishan Paul, Janvita Reddy, Arghya Sarkar, Kinjal Sensharma, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Disinformation can cause disruption in the share market, panic and anxiety in society, and even death during crises. |
| Approach: | a new dataset is being developed to help combat disinformation . the dataset is a multimodal fake news dataset with 5W question-answering . |
| Outcome: | FACTIFY 3M is the largest dataset and benchmark for multimodal fact verification. |
Parallel Communities Across the Surface Web and the Dark Web (2025.findings-emnlp)
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Wenchao Dong, Megha Sundriyal, Seongchan Park, Jaehong Kim, Meeyoung Cha, Tanmoy Chakraborty, Wonjae Lee
| Challenge: | Sense of Community is a social motivation that is reflected in the social behavior of humans. |
| Approach: | They compile a large collection of parallel community datasets comprising over 7 million posts and comments from Reddit and 200,000 posts and comment from Dread, a dark web discussion forum, covering similar topics. |
| Outcome: | The results show that users on Reddit exhibit a stronger sense of community membership despite the dark web’s restricted accessibility. |
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking (2023.findings-emnlp)
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| Challenge: | Social media posts are noisy and pervasive, resulting in difficult to identify precise and prominent claims that require verification. |
| Approach: | They propose a task called Claim Normalization that decomposes complex and noisy social media posts into more straightforward and understandable forms, termed normalized claims. |
| Outcome: | The proposed model outperforms baselines across evaluation measures and errors. |
Counter Turing Test (CT2): AI-Generated Text Detection is Not as Easy as You May Think - Introducing AI Detectability Index (ADI) (2023.emnlp-main)
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Megha Chakraborty, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Shreya Gautam, Tanay Kumar, Krish Sharma, Niyar Barman, Chandan Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | a number of issues have arisen regarding the risk and consequences of AI-generated text detection. |
| Approach: | They propose a counter-turing test to evaluate the robustness of existing AGTD methods . they propose ADI, a quantifiable spectrum to assess detectability of LLMs . |
| Outcome: | The proposed method evaluates the robustness of existing AGTD methods . it shows that larger LLMs tend to have lower ADI, indicating they are less detectable . |
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content (2021.eacl-main)
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| Challenge: | Existing work on claim detection is built on the basis of a 'segregation' of claims across different domains. |
| Approach: | They propose a generalized generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model. |
| Outcome: | The proposed model outperforms baselines on six claim datasets by an average of 3 claim-F1 points and 2 claim-f1 points on the general-domain experiments. |
The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection (2025.emnlp-main)
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Arghodeep Nandi, Megha Sundriyal, Euna Mehnaz Khan, Jikai Sun, Emily K. Vraga, Jaideep Srivastava, Tanmoy Chakraborty
| Challenge: | a survey examines the interplay between factual accuracy and cognitive biases . misinformation is more than just the existence of incorrect information, it also entails complex relationships between the information and the entities that consume it. |
| Approach: | They examine the interplay between traditional fact-checking and psychological concepts such as cognitive biases, social dynamics, and emotional responses. |
| Outcome: | The findings highlight limitations of current methods and identify opportunities for improvement . they also outline future research directions to create more robust frameworks . |
Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter (2022.emnlp-main)
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| Challenge: | Current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of misinformation and fake news. |
| Approach: | They propose a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets and a model that automatically detects and extracts the snippets of misinformation. |
| Outcome: | The proposed model outperforms baseline systems on several evaluation metrics, improving by 1.5 points. |
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering (2023.acl-long)
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Anku Rani, S.M Towhidul Islam Tonmoy, Dwip Dalal, Shreya Gautam, Megha Chakraborty, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Contemporary fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. |
| Approach: | They propose a 5W framework for question-answer-based fact explainability that can assist human fact-checkers in asking relevant questions . they propose masked language model which generates QA pairs for claims and a baseline QA system that automatically locates those answers from evidence documents. |
| Outcome: | The proposed framework can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. |