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

Published: 02 February 2018 Publication History

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.

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  • (2024)Ideological orientation and extremism detection in online social networking sites: A systematic reviewIntelligent Systems with Applications10.1016/j.iswa.2024.20045624(200456)Online publication date: Dec-2024
  • (2024)Dual Bi-LSTM-GRU based stance detection in tweets ordered classesNeural Computing and Applications10.1007/s00521-024-10549-9Online publication date: 6-Dec-2024
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cover image ACM Conferences
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
February 2018
821 pages
ISBN:9781450355810
DOI:10.1145/3159652
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 02 February 2018

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Author Tags

  1. combining link and content
  2. graph regularization
  3. ideology
  4. information filter bubble
  5. latent space learning
  6. manifold learning
  7. matrix factorization
  8. polarization
  9. social networks
  10. twitter

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;
Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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  • (2024)NewsGuesser: Using Curiosity to Reduce Selective ExposureProceedings of the ACM on Human-Computer Interaction10.1145/36373768:CSCW1(1-22)Online publication date: 26-Apr-2024
  • (2024)Ideological orientation and extremism detection in online social networking sites: A systematic reviewIntelligent Systems with Applications10.1016/j.iswa.2024.20045624(200456)Online publication date: Dec-2024
  • (2024)Dual Bi-LSTM-GRU based stance detection in tweets ordered classesNeural Computing and Applications10.1007/s00521-024-10549-9Online publication date: 6-Dec-2024
  • (2023)Beyond sentiment: an algorithmic strategy for identifying evaluations within large text corporaCommunication Methods and Measures10.1080/19312458.2023.2285783(1-22)Online publication date: 7-Dec-2023
  • (2023)Attribute network joint embedding based on global attentionPattern Recognition Letters10.1016/j.patrec.2023.11.012176(189-195)Online publication date: Dec-2023
  • (2022)Maximizing the Diversity of Exposure in a Social NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303871134:9(4357-4370)Online publication date: 1-Sep-2022
  • (2022)Automated generation of reaction network hypotheses for complex feedstocks2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP)10.1109/AdCONIP55568.2022.9894209(234-239)Online publication date: 7-Aug-2022
  • (2022)Hiding opinions from machine learningPNAS Nexus10.1093/pnasnexus/pgac2561:5Online publication date: 16-Nov-2022
  • (2022)Strengthening ties towards a highly-connected worldData Mining and Knowledge Discovery10.1007/s10618-021-00812-1Online publication date: 4-Jan-2022
  • (2022)Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysisArtificial Intelligence Review10.1007/s10462-022-10254-w56:6(5133-5260)Online publication date: 26-Oct-2022
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