Improving Generalizability of Fake News Detection Methods using Propensity Score Matching

• URL: http://arxiv.org/abs/2002.00838v1
• Date: Tue, 28 Jan 2020 00:44:59 GMT
• Title: Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
• Authors: Bo Ni, Zhichun Guo, Jianing Li, Meng Jiang
• Abstract summary: We consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features.
• Score: 23.968908482333717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this paper, we consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features in order to reduce the effects of the confounding variables. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features. We investigate multiple types of fake news methods (classifiers) such as logistic regression, random forests, and support vector machines. We have consistent observations of performance improvement.

Related papers

• 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)
• 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)
• Predictive linguistic cues for fake news: a societal artificial intelligence problem [9.40467099889021]
  We present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. We use neural networks which mainly control distributional features rather than evidence. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.
  arXiv  Detail & Related papers  (2022-11-26T07:50:01Z)
• 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 Multi-Policy Framework for Deep Learning-Based Fake News Detection [0.31498833540989407]
  This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection. MPSC uses deep learning techniques to analyze a statement itself and its related news articles, predicting whether it is seemingly credible or suspicious.
  arXiv  Detail & Related papers  (2022-06-01T21:25:21Z)
• Explainable Tsetlin Machine framework for fake news detection with credibility score assessment [16.457778420360537]
  We propose a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) We use the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least $5%$ in terms of accuracy.
  arXiv  Detail & Related papers  (2021-05-19T13:18:02Z)
• 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)
• An Event Correlation Filtering Method for Fake News Detection [0.0]
  Existing deep learning models have achieved great progress to tackle the problem of fake news detection. To improve the detection performance of fake news, we take advantage of the event correlations of news. ECFM is proposed to integrate them to detect fake news in an event correlation filtering manner.
  arXiv  Detail & Related papers  (2020-12-10T07:31:07Z)
• 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)
• Fake News Detection using Temporal Features Extracted via Point Process [2.105564340986074]
  We attempt to use temporal features generated from SNS posts by using a point process algorithm to identify fake news from real news. We propose a novel multi-modal attention-based method, which includes linguistic and user features alongside temporal features.
  arXiv  Detail & Related papers  (2020-07-28T06:34:54Z)
• 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.