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Cigdem Aslay

Learning Ideological Embeddings from Information Cascades

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


  • Corrado Monti, Institute for Scientific Interchange Foundation
  • ,
  • Giuseppe Manco, CNR
  • ,
  • Cigdem Aslay
  • Francesco Bonchi, Institute for Scientific Interchange Foundation

Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects. In this paper we propose a stochastic model to learn the ideological leaning of each user in a multidimensional ideological space, by analyzing the way politically salient content propagates. In particular, our model assumes that information propagates from one user to another if both users are interested in the topic and ideologically aligned with each other. To infer the parameters of our model, we devise a gradient-based optimization procedure maximizing the likelihood of an observed set of information cascades. Our experiments on real-world political discussions on Twitter and Reddit confirm that our model is able to learn the political stance of the social media users in a multidimensional ideological space.

Original languageEnglish
Title of host publicationProceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM '21)
Number of pages10
Place of publicationNew York
PublisherAssociation for Computing Machinery
Publication yearOct 2021
ISBN (Electronic)9781450384469
Publication statusPublished - Oct 2021
Event30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
Duration: 1 Nov 20215 Nov 2021


Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
ByVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 ACM.

    Research areas

  • embedding, information diffusion, polarization

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ID: 226898394