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Learning Ideological Latent space in Twitter

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Garimella, Kiran
dc.contributor.author Lahoti, Preethi
dc.date.accessioned 2017-09-04T12:58:35Z
dc.date.available 2017-09-04T12:58:35Z
dc.date.issued 2017-08-28
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/27975
dc.description.abstract People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption forces users to be confined to content that confirms with their own point of view. This has led to social phenomena like polarization of point-of-view and intolerance towards opposing views. In this thesis we study information filter bubbles from a mathematical standpoint. We use data mining techniques to learn a liberal-conservative ideology space in Twitter and presents a case study on how such a latent space can be used to tackle the filter bubble problem on social networks. We model the problem of learning liberal-conservative ideology as a constrained optimization problem. Using matrix factorization we uncover an ideological latent space for content consumption and social interaction habits of users in Twitter. We validate our model on real world Twitter dataset on three controversial topics - "Obamacare", "gun control" and "abortion". Using the proposed technique we are able to separate users by their ideology with 95% purity. Our analysis shows that there is a very high correlation (0.8 - 0.9) between the estimated ideology using machine learning and true ideology collected from various sources. Finally, we re-examine the learnt latent space, and present a case study showcasing how this ideological latent space can be used to develop exploratory and interactive interfaces that can help in diffusing the information filter bubble. Our matrix factorization based model for learning ideology latent space, along with the case studies provide a theoretically solid as well as a practical and interesting point-of-view to online polarization. Further, it provides a strong foundation and suggests several avenues for future work in multiple emerging interdisciplinary research areas, for instance, humanly interpretable and explanatory machine learning, transparent recommendations and a new field that we coin as Next Generation Social Networks. en
dc.format.extent 47 + 5
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Learning Ideological Latent space in Twitter en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword filter bubble en
dc.subject.keyword Matrix factorization en
dc.subject.keyword twitter en
dc.subject.keyword polarization en
dc.subject.keyword combining link and content en
dc.subject.keyword latent space learning en
dc.identifier.urn URN:NBN:fi:aalto-201709046874
dc.programme.major Machine Learning and Data Mining fi
dc.programme.mcode SCI3015 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Gionis, Aristides
dc.programme Master’s Programme in Computer, Communication and Information Sciences fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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