Network Inference from a Mixture of Diffusion Models for Fake News Mitigation

• URL: http://arxiv.org/abs/2008.03450v1
• Date: Sat, 8 Aug 2020 05:59:25 GMT
• Title: Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
• Authors: Karishma Sharma, Xinran He, Sungyong Seo, Yan Liu
• Abstract summary: The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes has become a pressing problem on social media. We focus on understanding and leveraging diffusion dynamics of false and legitimate contents in order to facilitate network interventions for fake news mitigation.
• Score: 12.229596498611837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes, has become a pressing problem on social media. Moreover, information sharing on social media facilitates diffusion of viral information cascades. In this work, we focus on understanding and leveraging diffusion dynamics of false and legitimate contents in order to facilitate network interventions for fake news mitigation. We analyze real-world Twitter datasets comprising fake and true news cascades, to understand differences in diffusion dynamics and user behaviours with regards to fake and true contents. Based on the analysis, we model the diffusion as a mixture of Independent Cascade models (MIC) with parameters $\theta_T, \theta_F$ over the social network graph; and derive unsupervised inference techniques for parameter estimation of the diffusion mixture model from observed, unlabeled cascades. Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics. Characteristics of the identified influential users reveal positive correlation between influential users identified for fake news and their relative appearance in fake news cascades. Identified influential users tend to be related to topics of more viral information cascades than less viral ones; and identified fake news influential users have relatively fewer counts of direct followers, compared to the true news influential users. Intervention analysis on nodes and edges demonstrates capacity of the inferred diffusion dynamics in supporting network interventions for mitigation.

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)
• 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)
• 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)
• Mining User-aware Multi-Relations for Fake News Detection in Large Scale Online Social Networks [25.369320307526362]
  credible users are more likely to share trustworthy news, while untrusted users have a higher probability of spreading untrustworthy news. We construct a dual-layer graph to extract multiple relations of news and users in social networks to derive rich information for detecting fake news. We propose a fake news detection model named Us-DeFake, which learns the propagation features of news in the news layer and the interaction features of users in the user layer.
  arXiv  Detail & Related papers  (2022-12-21T05:30:35Z)
• The Impact of Disinformation on a Controversial Debate on Social Media [1.299941371793082]
  We study how pervasive is the presence of disinformation in the Italian debate around immigration on Twitter. By characterising the Twitter users with an textitUntrustworthiness score, we are able to see that such bad information consumption habits are not equally distributed across the users.
  arXiv  Detail & Related papers  (2021-06-30T10:29:07Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• 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)
• 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)
• An Information Diffusion Approach to Rumor Propagation and Identification on Twitter [0.0]
  We study the dynamics of microscopic-level misinformation spread on Twitter. Our findings confirm that rumor cascades run deeper and that rumor masked as news, and messages that incite fear, will diffuse faster than other messages.
  arXiv  Detail & Related papers  (2020-02-24T20:04:54Z)

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.