Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

• URL: http://arxiv.org/abs/2212.06560v1
• Date: Tue, 13 Dec 2022 13:29:47 GMT
• Title: Exploring Fake News Detection with Heterogeneous Social Media Context Graphs
• Authors: Gregor Donabauer, Udo Kruschwitz
• Abstract summary: Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. We propose to construct heterogeneous social context graphs around news articles and reformulate the problem as a graph classification task.
• Score: 4.2177790395417745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. Recent advances have primarily focused on textbased approaches. However, it has become clear that to be effective one needs to incorporate additional, contextual information such as spreading behaviour of news articles and user interaction patterns on social media. We propose to construct heterogeneous social context graphs around news articles and reformulate the problem as a graph classification task. Exploring the incorporation of different types of information (to get an idea as to what level of social context is most effective) and using different graph neural network architectures indicates that this approach is highly effective with robust results on a common benchmark dataset.

Related papers

• Graph Neural Networks for Antisocial Behavior Detection on Twitter [0.0]
  Social media resurgence of antisocial behavior has exerted a downward spiral on stereotypical beliefs, and hateful comments towards individuals and social groups. Advances in graph neural networks employed on massive quantities of graph-structured data raise high hopes for the future of mediating communication on social media platforms. An approach based on graph convolutional data was employed to better capture the dependencies between the heterogeneous types of data.
  arXiv  Detail & Related papers  (2023-12-28T00:25:12Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• 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)
• An Interactive Framework for Profiling News Media Sources [26.386860411085053]
  We propose an interactive framework for news media profiling. It combines the strengths of graph based news media profiling models, Pre-trained Large Language Models, and human insight. With as little as 5 human interactions, our framework can rapidly detect fake and biased news media.
  arXiv  Detail & Related papers  (2023-09-14T02:03:45Z)
• 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)
• Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions [49.305189190372765]
  Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph. We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs.
  arXiv  Detail & Related papers  (2023-02-08T09:09:47Z)
• Self-supervised Hypergraph Representation Learning for Sociological Analysis [52.514283292498405]
  We propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria. First, we propose an effective hypergraph awareness and a fast line graph construction framework. Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users.
  arXiv  Detail & Related papers  (2022-12-22T01:20:29Z)
• Hetero-SCAN: Towards Social Context Aware Fake News Detection via Heterogeneous Graph Neural Network [11.145085584637744]
  We propose a novel social context aware fake news detection method, Hetero-SCAN, based on a heterogeneous graph neural network. We demonstrate that Hetero-SCAN yields significant improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency.
  arXiv  Detail & Related papers  (2021-09-13T15:21:44Z)
• Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation [56.25878966006678]
  We propose an approach of textbfPRactical textbfInference in textbfSocial rtextbfElation (PRISE) It concisely learns interactive features of persons and discriminative features of holistic scenes. PRISE achieves 6.8$%$ improvement for domain classification in PIPA dataset.
  arXiv  Detail & Related papers  (2021-07-30T04:20:13Z)
• Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks [90.3475746663728]
  Events are happening in real-world and real-time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous.
  arXiv  Detail & Related papers  (2021-04-02T02:13:10Z)
• Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection [18.847254074201953]
  We propose a novel fake news detection framework, namely Adversarial Active Learning-based Heterogeneous Graph Neural Network (AA-HGNN) AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data. Experiments with two real-world fake news datasets show that our model can outperform text-based models and other graph-based models.
  arXiv  Detail & Related papers  (2021-01-27T05:05:25Z)

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.