Papers with detecting misinformation
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (2021.acl-long)
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| Challenge: | a new method for fact-checking is needed to detect disinformation on the web . a dataset COVID-Fact contains 4,086 claims concerning the COVId-19 pandemic . |
| Approach: | They propose a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVId-19 pandemic . they automatically detect true claims and their source articles and generate counter-claims using automatic methods . |
| Outcome: | The proposed method reduces the cost of building domain-specific datasets for detecting misinformation . the proposed dataset contains 4,086 claims concerning the COVID-19 pandemic . |
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues (2023.findings-acl)
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| Challenge: | Using fact verification tasks, however, can not detect subtle differences in factually consistent claims, which might bias the readers. |
| Approach: | They propose a novel dataset that primarily focuses on comparing the nuance between claim pairs. |
| Outcome: | The proposed dataset shows that human-labeled 2,941 claim pairs are weaker than baselines, showing a 19% absolute gap with the baselines. |
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 (2021.eacl-main)
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Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, Rannie Lin
| Challenge: | a global pandemic of coronavirus disease 2019 has impacted millions of people . a human annotation study reveals the utility of our models on a subset of Mega-COV . |
| Approach: | They develop powerful models to analyze tweets related to the pandemic . they use a multilingual Twitter dataset with geo-location information . |
| Outcome: | The proposed model can identify whether a tweet is related to the pandemic and detect misinformation about it. |
ECON: On the Detection and Resolution of Evidence Conflicts (2024.emnlp-main)
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Cheng Jiayang, Chunkit Chan, Qianqian Zhuang, Lin Qiu, Tianhang Zhang, Tengxiao Liu, Yangqiu Song, Yue Zhang, Pengfei Liu, Zheng Zhang
| Challenge: | Recent studies have shown that AI generated content is more likely to dominate search results, making it difficult to detect when compared to human-produced content. |
| Approach: | They propose a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. |
| Outcome: | The proposed method enables the detection of conflicting information in real-world scenarios and shows that weaker models struggle with similar answer conflicts while stronger models show robust performance. |
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning (2021.emnlp-main)
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| Challenge: | Various deep learning models have been successfully employed for this type of NLP task of text classification. |
| Approach: | They propose a mixed-domain transfer learning approach that only captures local context and exhibits poor generalization. |
| Outcome: | The proposed model captures local and global contexts, but lacks generalization . a combination of shallow network-based domain-specific models and convolutional neural networks can extract local and globally context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution. |
VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines (2022.lrec-1)
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| Challenge: | VaccineLies can detect misinformation about vaccines on Twitter without using language resources. |
| Approach: | They present a dataset of tweets propagating misinformation about two vaccines . authors propose novel methods to detect misinformation on Twitter and identify stance towards it . |
| Outcome: | VaccineLies can detect misinformation on Twitter and identify the stance towards it. |