Beyond Binary Labels: Political Ideology Prediction of Twitter Users
Daniel Preoţiuc-Pietro,Ye Liu,Daniel J. Hopkins,Lyle H. Ungar +3 more
- Vol. 1, pp 729-740
TLDR
This study examines users’ political ideology using a seven-point scale which enables it to identify politically moderate and neutral users – groups which are of particular interest to political scientists and pollsters.Abstract:
Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US. This study examines users’ political ideology using a seven-point scale which enables us to identify politically moderate and neutral users – groups which are of particular interest to political scientists and pollsters. Using a novel data set with political ideology labels self-reported through surveys, our goal is two-fold: a) to characterize the groups of politically engaged users through language use on Twitter; b) to build a fine-grained model that predicts political ideology of unseen users. Our results identify differences in both political leaning and engagement and the extent to which each group tweets using political keywords. Finally, we demonstrate how to improve ideology prediction accuracy by exploiting the relationships between the user groups.read more
Citations
More filters
Journal ArticleDOI
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling.
Posted Content
Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
Journal ArticleDOI
Social media, political polarization, and political disinformation: a review of the scientific literature
Joshua A. Tucker,Andrew M. Guess,Pablo Barberá,Cristian Vaccari,Cristian Vaccari,Alexandra A. Siegel,Alexandra A. Siegel,Sergey Sanovich,Denis Stukal,Brendan Nyhan +9 more
TL;DR: The authors provide an overview of the current state of the literature on the relationship between social media; political polarization; and political "disinformation", a term used to encompass a wide range of types of information about politics found online.
Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count
Литвинова Татьяна Александровна,Литвинова Ольга Александровна,Рыжкова Екатерина Сергеевна,Бирюкова Елизавета Дмитриевна,Середин Павел Владимирович,Загоровская Ольга Владимировна +5 more
Journal ArticleDOI
A novel CNN based security guaranteed image watermarking generation scenario for smart city applications
TL;DR: A novel algorithm using synergetic neural networks for robustness and security of digital image watermarking is proposed, which obtains an optimal Peak Signal-to-noise ratio (PSNR) and can complete certain image processing operations with improved performance.
References
More filters
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).