Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
Author:
Affiliation:
1. Max Planck Institute for Informatics & Aalto University, Saarbruecken, Germany
2. Aalto University, Espoo, Finland
Funder
Academy of Finland
H2020 European Research Council
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3159652.3159669
Reference37 articles.
1. Comparing Cosponsorship and Roll-Call Ideal Points
2. Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
3. Eytan Bakshy Solomon Messing and Lada Adamic . 2015. Exposure to ideologically diverse news and opinion on Facebook. Science(2015). Eytan Bakshy Solomon Messing and Lada Adamic . 2015. Exposure to ideologically diverse news and opinion on Facebook. Science(2015).
4. Pablo Barberá . 2015. Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis. Pablo Barberá . 2015. Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis.
5. Pablo Barberá John Jost Jonathan Nagler Joshua Tucker and Richard Bonneau . 2015. Tweeting from left to right: Is online political communication more than an echo chamber? Psychological science. Pablo Barberá John Jost Jonathan Nagler Joshua Tucker and Richard Bonneau . 2015. Tweeting from left to right: Is online political communication more than an echo chamber? Psychological science.
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