A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection

• URL: http://arxiv.org/abs/2101.03545v1
• Date: Sun, 10 Jan 2021 13:21:08 GMT
• Title: A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection
• Authors: Sourya Dipta Das, Ayan Basak and Saikat Dutta
• Abstract summary: 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.
• Score: 5.979726271522835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world stay connected. With the COVID-19 pandemic raging, social media has become more relevant and widely used than ever before, and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe our Fake News Detection system that automatically identifies whether a tweet related to COVID-19 is "real" or "fake", as a part of CONSTRAINT COVID19 Fake News Detection in English challenge. 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 achieved an F1-score of 0.9831 against a top score of 0.9869. Post completion of the competition, we have been able to drastically improve our system by incorporating a novel heuristic algorithm based on username handles and link domains in tweets fetching an F1-score of 0.9883 and achieving state-of-the art results on the given dataset.

Related papers

• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• 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)
• Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation [83.2079454464572]
  This paper describes our approach to the Image-Text Inconsistency Detection challenge of the DARPA Semantic Forensics (SemaFor) Program. We collect Twitter-COMMs, a large-scale multimodal dataset with 884k tweets relevant to the topics of Climate Change, COVID-19, and Military Vehicles. We train our approach, based on the state-of-the-art CLIP model, leveraging automatically generated random and hard negatives.
  arXiv  Detail & Related papers  (2021-12-16T03:37:20Z)
• Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset [77.27709662210363]
  We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP. We annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds) Our results show that solidarity became increasingly salient and contested during the COVID-19 crisis.
  arXiv  Detail & Related papers  (2021-08-02T17:03:12Z)
• 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)
• 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)
• Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection [14.771202995527315]
  COVID-19 related fake news has been spreading faster than the facts. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets. We also propose Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model.
  arXiv  Detail & Related papers  (2021-02-17T18:30:43Z)
• Hostility Detection and Covid-19 Fake News Detection in Social Media [1.3499391168620467]
  We build a model that makes use of an abusive language detector and features extracted via Hindi BERT and Hindi FastText models. We also build models to identify fake news related to Covid-19 in English tweets.
  arXiv  Detail & Related papers  (2021-01-15T03:24:36Z)
• 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)
• 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)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)

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.