Transformer based Automatic COVID-19 Fake News Detection System

• URL: http://arxiv.org/abs/2101.00180v3
• Date: Thu, 21 Jan 2021 15:18:40 GMT
• Title: Transformer based Automatic COVID-19 Fake News Detection System
• Authors: Sunil Gundapu, Radhika Mamidi
• Abstract summary: Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic. We report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.
• Score: 9.23545668304066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent rapid technological advancements in online social networks such as Twitter have led to a great incline in spreading false information and fake news. Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic, leading to individuals accepting bogus and potentially deleterious claims and articles. Quick detection of fake news can reduce the spread of panic and confusion among the public. For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our best approach is based on an ensemble of three transformer models (BERT, ALBERT, and XLNET) to detecting fake news. This model was trained and evaluated in the context of the ConstraintAI 2021 shared task COVID19 Fake News Detection in English. Our system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.

Related papers

• Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
  We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
  arXiv  Detail & Related papers  (2023-11-02T08:39:45Z)
• Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
  Multiverse is a new feature based on multilingual evidence that can be used for fake news detection. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
  arXiv  Detail & Related papers  (2022-11-25T18:24:17Z)
• A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models [2.0649235321315285]
  The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. The RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes.
  arXiv  Detail & Related papers  (2022-08-02T10:50:16Z)
• Cross-lingual COVID-19 Fake News Detection [54.125563009333995]
  We make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English) We propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content. Empirical results on our dataset demonstrate the effectiveness of CrossFake under the cross-lingual setting.
  arXiv  Detail & Related papers  (2021-10-13T04:44:02Z)
• User Preference-aware Fake News Detection [61.86175081368782]
  Existing fake news detection algorithms focus on mining news content for deceptive signals. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
  arXiv  Detail & Related papers  (2021-04-25T21:19:24Z)
• TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection [0.0]
  The main goal of the work was to create a model that would carry out a binary classification of messages from social media as real or fake news. The model allowed us to achieve 0.94 F1-score, which is within 5% of the best result.
  arXiv  Detail & Related papers  (2021-01-14T16:25:32Z)
• TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy [9.66022279280394]
  Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation. We present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text.
  arXiv  Detail & Related papers  (2021-01-10T11:52:17Z)
• g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection [0.0]
  We present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News Detection in English. We propose our approach using the transformer-based ensemble of COVID-Twitter-BERT (CT-BERT) models. As a result, our best model achieved the weighted F1-score of 98.69 on the test set.
  arXiv  Detail & Related papers  (2020-12-22T12:43:12Z)
• Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking [0.3441021278275805]
  We develop a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of truth in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset.
  arXiv  Detail & Related papers  (2020-11-26T11:50:45Z)
• Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
  This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
  arXiv  Detail & Related papers  (2020-07-24T05:42:29Z)
• Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News [67.53424807783414]
  Social media has greatly enabled people to participate in online activities at an unprecedented rate. This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
  arXiv  Detail & Related papers  (2020-04-03T18:26:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.