TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection

• URL: http://arxiv.org/abs/2101.05701v1
• Date: Thu, 14 Jan 2021 16:25:32 GMT
• Title: TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection
• Authors: Elena Shushkevich and John Cardiff
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper is devoted to the participation of the TUDublin team in Constraint@AAAI2021 - COVID19 Fake News Detection Challenge. Today, the problem of fake news detection is more acute than ever in connection with the pandemic. The number of fake news is increasing rapidly and it is necessary to create AI tools that allow us to identify and prevent the spread of false information about COVID-19 urgently. 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 in the context of COVID-19. Our team constructed the ensemble consisting of Bidirectional Long Short Term Memory, Support Vector Machine, Logistic Regression, Naive Bayes and a combination of Logistic Regression and Naive Bayes. The model allowed us to achieve 0.94 F1-score, which is within 5\% of the best result.

Related papers

• Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks [49.29141811578359]
  We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
  arXiv  Detail & Related papers  (2022-12-24T00:19:32Z)
• Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021 [55.41644538483948]
  The goal of the shared task is to motivate the community to come up with efficient methods for solving this vital problem. The training set contains 1300 annotated news articles -- 750 real news, 550 fake news, while the testing set contains 300 news articles -- 200 real, 100 fake news. The best performing system obtained an F1-macro score of 0.679, which is lower than the past year's best result of 0.907 F1-macro.
  arXiv  Detail & Related papers  (2022-07-11T18:58:36Z)
• Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [105.20743048379387]
  We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
  arXiv  Detail & Related papers  (2022-03-10T14:24:19Z)
• 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)
• A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles [5.979726271522835]
  We describe a novel Fake News Detection system that automatically identifies whether a news item is "real" or "fake" We have used an ensemble model consisting of pre-trained models followed by a statistical feature fusion network. Our proposed framework have also quantified reliable predictive uncertainty along with proper class output confidence level for the classification task.
  arXiv  Detail & Related papers  (2021-04-05T06:35:30Z)
• A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection [5.979726271522835]
  We describe our Fake News Detection system that automatically identifies whether a tweet related to COVID-19 is "real" or "fake" We have used an ensemble model consisting of pre-trained models that has helped us achieve a joint 8th position on the leader board. We have been able to drastically improve our system by incorporating a novel algorithm based on username handles and link domains in tweets fetching an F1-score of 0.9883.
  arXiv  Detail & Related papers  (2021-01-10T13:21:08Z)
• Transformer based Automatic COVID-19 Fake News Detection System [9.23545668304066]
  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.
  arXiv  Detail & Related papers  (2021-01-01T06:49:27Z)
• 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)
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [57.9843300852526]
  We introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies.
  arXiv  Detail & Related papers  (2020-09-16T14:13:15Z)
• 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)

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.