Fake News Detection by means of Uncertainty Weighted Causal Graphs

• URL: http://arxiv.org/abs/2002.01065v2
• Date: Thu, 2 Apr 2020 05:12:35 GMT
• Title: Fake News Detection by means of Uncertainty Weighted Causal Graphs
• Authors: Eduardo C. Garrido-Merch\'an, Cristina Puente, Rafael Palacios
• Abstract summary: Social networks let people share news that do not necessarily be trust worthy. Fake news can influence negatively the opinion of people about certain figures, groups or ideas. It is desirable to design a system that is able to detect and classify information as fake.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Society is experimenting changes in information consumption, as new information channels such as social networks let people share news that do not necessarily be trust worthy. Sometimes, these sources of information produce fake news deliberately with doubtful purposes and the consumers of that information share it to other users thinking that the information is accurate. This transmission of information represents an issue in our society, as can influence negatively the opinion of people about certain figures, groups or ideas. Hence, it is desirable to design a system that is able to detect and classify information as fake and categorize a source of information as trust worthy or not. Current systems experiment difficulties performing this task, as it is complicated to design an automatic procedure that can classify this information independent on the context. In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs. These graphs are specific hybrid models that are built through causal relations retrieved from texts and consider the uncertainty of causal relations. We take advantage of this representation to use the probability distributions of this graph and built a fake news classifier based on the entropy and KL divergence of learned and new information. We believe that the problem of fake news is accurately tackled by this model due to its hybrid nature between a symbolic and quantitative methodology. We describe the methodology of this classifier and add empirical evidence of the usefulness of our proposed approach in the form of synthetic experiments and a real experiment involving lung cancer.

Related papers

• Epidemiology-informed Network for Robust Rumor Detection [59.89351792706995]
  We propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance. To adapt epidemiology theory to rumor detection, it is expected that each users stance toward the source information will be annotated. Our experimental results demonstrate that the proposed EIN not only outperforms state-of-the-art methods on real-world datasets but also exhibits enhanced robustness across varying tree depths.
  arXiv  Detail & Related papers  (2024-11-20T00:43:32Z)
• Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness [22.349521957987672]
  Social graph-based fake news detection aims to identify news articles containing false information by utilizing social contexts. We formalize a more realistic evaluation scheme that mimics real-world scenarios. We show that the discriminative capabilities of conventional methods decrease sharply under this new setting.
  arXiv  Detail & Related papers  (2024-11-19T05:08:00Z)
• Exposing and Explaining Fake News On-the-Fly [4.278181795494584]
  This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure.
  arXiv  Detail & Related papers  (2024-05-03T14:49:04Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• Fake News Detection Through Graph-based Neural Networks: A Survey [18.70577400440486]
  Low-quality and/or deliberately fake information can spread rapidly online. Identifying and debunking online misinformation as early as possible has become an increasingly urgent problem. We present a systematic review of fake news detection studies based on graph-based and deep learning-based techniques.
  arXiv  Detail & Related papers  (2023-07-24T09:30:30Z)
• Fake News Identification using Machine Learning Algorithms Based on Graph Features [0.0]
  This study aims to build a model for identifying fake news using graphs and machine learning algorithms. It can effectively predict fake news, prevent potential negative social impact caused by fake news, and provide a new perspective on graph feature selection for machine learning models.
  arXiv  Detail & Related papers  (2022-08-22T22:42:57Z)
• Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach [0.39146761527401425]
  Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystems reliability. This paper has analyzed the social context of fake news detection with a hybrid graph neural network based approach.
  arXiv  Detail & Related papers  (2022-07-27T12:58:33Z)
• A Bayesian Framework for Information-Theoretic Probing [51.98576673620385]
  We argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. This paper proposes a new framework to measure what we term Bayesian mutual information.
  arXiv  Detail & Related papers  (2021-09-08T18:08:36Z)
• 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)
• 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.