Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Vo, Nguyen; Lee, Kyumin

doi:10.18653/v1/2020.emnlp-main.621

Cited by 49 publications

(42 citation statements)

References 57 publications

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“…In a similar way, several approaches have been developed to identify as-well-as limit the spread of (mis-)information [1,3,4,9,10,12]. [20] approach the problem of fake news spread prevention by proposing a multimodal attention network that learns to rank the fact-checking documents based on their relevancy.…”

Section: Related Workmentioning

confidence: 99%

Fighting an Infodemic: COVID-19 Fake News Dataset

Patwa

Sharma

Pykl

et al. 2021

Communications in Computer and Information Science

248

117

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Along with COVID-19 pandemic we are also fighting an 'infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We perform a binary classification task (real vs fake) and benchmark the annotated dataset with four machine learning baselines -Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.32% F1-score with SVM on the test set. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection.

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Section: Related Workmentioning

confidence: 99%

Fighting an Infodemic: COVID-19 Fake News Dataset

Patwa

Sharma

Pykl

et al. 2021

Communications in Computer and Information Science

248

117

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“…Thorne et al (2018) use Wikipedia as a fact tank and build a shared task for automatic fact-checking, while Popat et al (2018b) and ) to obtain pattern-sentence scores; z identifies k 2 key sentences by combining the two scores (here, k 2 = 2, and s i and s l are selected); { models interaction among q , s , and the nearest memory vector m for each key sentence; and | perform score-weighted aggregation and predict the claim-article relevance. Zhang et al, 2021), source credibility (Nguyen et al, 2020), user response (Shu et al, 2019) and diffusion network (Liu and Wu, 2018;Rosenfeld et al, 2020). However, these methods mainly aim at newly emerged claims and do not address those claims that have been fact-checked but continually spread.…”

Section: Related Workmentioning

confidence: 99%

“…Our work is in a new thread, detecting previously fact-checked claims. Vo and Lee (2020) models interaction between claims and FC-articles by combining GloVe (Pennington et al, 2014) and ELMo embeddings (Peters et al, 2018). Shaar et al (2020) train a RankSVM with scores from BM25 and Sentence-BERT for relevance prediction.…”

Section: Related Workmentioning

confidence: 99%

“…The Twitter 4 dataset is originated from (Vo and Lee, 2019) and processed by Vo and Lee (2020). The dataset pairs the claims (tweets) with the corresponding FC-articles from Snopes.…”

Section: Datamentioning

confidence: 99%

“…RankSVM: A pairwise RankSVM model for reranking using the scores from BM25 and sentence-BERT (mentioned above), which achieves the best results in (Shaar et al, 2020). CTM (Vo and Lee, 2020): This method leverages GloVe and ELMo to jointly represent the claims and the FC-articles for predicting the relevance scores. Its multi-modal version is not included as MTM focuses on key textual information.…”

Section: Bert(transfer)mentioning

confidence: 99%

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Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

Sheng¹,

Cao²,

Zhang³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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5469task.According to the seminal work by Shaar et al. (2020), the task is tackled by a two-stage information retrieval approach. Its typical workflow is illustrated in Figure 1(a). Given a claim as a query, in the first stage a basic searcher (e.g., BM25 Robertson and Zaragoza, 2009) searches for candidate articles from a collection of fact-checking articles (FC-articles). In the second stage, a more powerful model (e.g., BERT, Devlin et al., 2019) reranks the candidates to provide evidence for manual or automatic detection. Existing works focus on the reranking stage: Vo and Lee (2020) model the interactions between a claim and the whole candidate articles, while Shaar et al. ( 2020) extract several semantically similar sentences from FC-articles as a proxy. Nevertheless, these methods treat FCarticles as general documents and ignore characteristics of FC-articles. Figure 1(b) shows three sentences from candidate articles for the given claim. Among them, S1 is more friendly to semantic matching than S2 and S3 because the whole S1 focuses on describing its topic and does not contain tokens irrelevant to the given claim, e.g., "has spread over years" in S2. Thus, a semantic-based model does not require to have strong filtering capability. If we use only general methods on this task, the relevant S2 and S3 may be neglected while irrelevant S1 is focused. To let the model focus on key sentences (i.e., sentences as a good proxy of article-level relevance) like S2 and S3, we need to consider two characteristics of FC-articles besides semantics: C1. Claims are often quoted to describe the checked events (e.g., the underlined text in S2); C2. Event-irrelevant patterns to introduce or debunk claims are common in FC-articles (e.g., bold texts in S2 and S3).

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Mining the online infosphere: A survey

Adak

Chakraborty

Das

et al. 2022

WIREs Data Min & Knowl

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The evolution of Artificial Intelligence (AI)-based systems and applications have pervaded everyday life to make decisions that have a momentous impact on individuals and society. With the staggering growth of online data, often termed as the online infosphere, it has become paramount to monitor the infosphere to ensure social good as AI-based decisions are severely dependent. This survey aims to provide a comprehensive review of some of the most important research areas related to the infosphere, focusing on the technical challenges and potential solutions. The survey also outlines some of the important future directions. We begin by focussing on the collaborative systems that have emerged within the infosphere with a special thrust on Wikipedia. In the follow-up, we demonstrate how the infosphere has been instrumental in the growth of scientific citations and collaborations, thus fuelling interdisciplinary research. Finally, we illustrate the issues related to the governance of the infosphere, such as the tackling of the (a) rising hateful and abusive behavior and (b) bias and discrimination in different online platforms and news reporting.

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Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Cited by 49 publications

References 57 publications

Fighting an Infodemic: COVID-19 Fake News Dataset

Fighting an Infodemic: COVID-19 Fake News Dataset

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

Mining the online infosphere: A survey

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