Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

17 Citations (Scopus)

Abstract

People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users» existing point of view. This phenomenon has led to polarization of opinions and intolerance towards opposing views. Thus, a key problem is to model information filter bubbles on social media and design methods to eliminate them. In this paper, we use a machine-learning approach to learn a liberal-conservative ideology space on Twitter, and show how we can use the learned latent space to tackle the filter bubble problem.

We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem. Our model incorporates the social-network structure and content-consumption information in a joint factorization problem with shared latent factors. We validate our model and solution on a real-world Twitter dataset consisting of controversial topics, and show that we are able to separate users by ideology with over 90% purity. When applied to media sources, our approach estimates ideology scores that are highly correlated(Pearson correlation 0.9) with ground-truth ideology scores. Finally, we demonstrate the utility of our model in real-world scenarios, by illustrating how the learned ideology latent space can be used to develop exploratory and interactive interfaces that can help users in diffusing their information filter bubble.
Original languageEnglish
Title of host publicationProceedings of the Eleventh ACM International Conference on Web Search and Data Mining
Place of PublicationNew York, NY, USA
PublisherACM
Pages351-359
Number of pages9
ISBN (Print)978-1-4503-5581-0
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventACM International Conference on Web Search and Data Mining - Marina Del Rey, United States
Duration: 5 Feb 20189 Feb 2018
Conference number: 11

Conference

ConferenceACM International Conference on Web Search and Data Mining
Abbreviated titleWSDM
CountryUnited States
CityMarina Del Rey
Period05/02/201809/02/2018

Keywords

  • combining link and content
  • graph regularization
  • ideology
  • information filter bubble
  • latent space learning
  • manifold learning
  • matrix factorization
  • polarization
  • social networks
  • twitter

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  • Cite this

    Lahoti, P., Garimella, K., & Gionis, A. (2018). Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (pp. 351-359). ACM. https://doi.org/10.1145/3159652.3159669