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Uncovering Cross-Platform Spreading Patterns of Fake News about Covid-19

11 pagesPublished: May 26, 2023


The spreading of fake news or misinformation on social media is a serious threat to modern societies, making more and more people susceptible to being unfairly influenced in their decision-making, be it in elections or other democratic processes. We contribute to the body of work in the area of fake news detection by studying cross-platform, multivariate spreading patterns of fake news on Covid-19-related topics – where existing studies have focused strongly on single platforms and/or on single metrics or indicators. Our findings show that there are several attributes that are specific to the cross-platform spreading process that become important predictors of fake news: there is e.g. a clear tendency that fake news travels faster from one platform to the other than real news. Meanwhile, although we have compiled a cross-platform corpus of fake and real news that future research may build on, data availability remains a challenge for future work.

Keyphrases: cross-platform analysis, Fake News Detection, machine learning

In: Aurona Gerber and Knut Hinkelmann (editors). Proceedings of Society 5.0 Conference 2023, vol 93, pages 141--151

BibTeX entry
  author    = {Lukas Schiesser and Hans Friedrich Witschel and Andre De La Harpe},
  title     = {Uncovering Cross-Platform Spreading Patterns of Fake News about Covid-19},
  booktitle = {Proceedings of Society 5.0 Conference 2023},
  editor    = {Aurona Gerber and Knut Hinkelmann},
  series    = {EPiC Series in Computing},
  volume    = {93},
  pages     = {141--151},
  year      = {2023},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/gkcs}}
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