Detecting Fake News Spreaders in Social Networks using Inductive\n Representation Learning Article Swipe
Bhavtosh Rath
,
Aadesh Salecha
,
Jaideep Srivastava
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.10817
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.10817
An important aspect of preventing fake news dissemination is to proactively\ndetect the likelihood of its spreading. Research in the domain of fake news\nspreader detection has not been explored much from a network analysis\nperspective. In this paper, we propose a graph neural network based approach to\nidentify nodes that are likely to become spreaders of false information. Using\nthe community health assessment model and interpersonal trust we propose an\ninductive representation learning framework to predict nodes of\ndensely-connected community structures that are most likely to spread fake\nnews, thus making the entire community vulnerable to the infection. Using\ntopology and interaction based trust properties of nodes in real-world Twitter\nnetworks, we are able to predict false information spreaders with an accuracy\nof over 90%.\n
Related Topics
Concepts
Computer science
Representation (politics)
Perspective (graphical)
Fake news
Graph
Interpersonal communication
Domain (mathematical analysis)
Feature learning
Machine learning
Artificial intelligence
Theoretical computer science
Internet privacy
Mathematics
Law
Mathematical analysis
Social psychology
Political science
Psychology
Politics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2011.10817
- https://arxiv.org/pdf/2011.10817
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287592114
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4287592114Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2011.10817Digital Object Identifier
- Title
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Detecting Fake News Spreaders in Social Networks using Inductive\n Representation LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-11-21Full publication date if available
- Authors
-
Bhavtosh Rath, Aadesh Salecha, Jaideep SrivastavaList of authors in order
- Landing page
-
https://arxiv.org/abs/2011.10817Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2011.10817Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2011.10817Direct OA link when available
- Concepts
-
Computer science, Representation (politics), Perspective (graphical), Fake news, Graph, Interpersonal communication, Domain (mathematical analysis), Feature learning, Machine learning, Artificial intelligence, Theoretical computer science, Internet privacy, Mathematics, Law, Mathematical analysis, Social psychology, Political science, Psychology, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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