"Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing
Sandeep Singamsetty, Nishtha Madaan, Sameep Mehta, Varad Bhatnagar,, Pushpak Bhattacharyya

TL;DR
This paper introduces a pipeline combining half-truth detection and claim editing using a T5 model, significantly improving detection accuracy and enabling effective debunking of deceptive statements on the internet.
Contribution
It presents the first integrated approach for detecting and debunking half-truths, utilizing controlled claim editing with a T5 model and extending datasets for better performance.
Findings
Achieved an F1 score of 82% in half-truth detection.
T5-based claim editing outperforms GPT2, RoBERTa, PEGASUS, and Tailor.
Edited claims received an 85% disinfo-debunk score.
Abstract
The prevalence of half-truths, which are statements containing some truth but that are ultimately deceptive, has risen with the increasing use of the internet. To help combat this problem, we have created a comprehensive pipeline consisting of a half-truth detection model and a claim editing model. Our approach utilizes the T5 model for controlled claim editing; "controlled" here means precise adjustments to select parts of a claim. Our methodology achieves an average BLEU score of 0.88 (on a scale of 0-1) and a disinfo-debunk score of 85% on edited claims. Significantly, our T5-based approach outperforms other Language Models such as GPT2, RoBERTa, PEGASUS, and Tailor, with average improvements of 82%, 57%, 42%, and 23% in disinfo-debunk scores, respectively. By extending the LIAR PLUS dataset, we achieve an F1 score of 82% for the half-truth detection model, setting a new benchmark in…
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Taxonomy
TopicsTopic Modeling · Misinformation and Its Impacts · Spam and Phishing Detection
MethodsGated Linear Unit · Multi-Head Attention · Attention Is All You Need · Byte Pair Encoding · Linear Layer · SentencePiece · Inverse Square Root Schedule · Layer Normalization · Softmax · Adam
