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Predicting User's Political Party Using Ideological Stances [chapter]

Swapna Gottipati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
2013 Lecture Notes in Computer Science  
In our work, we exploit users' ideological stances on controversial issues to predict political party of online users.  ...  Several political research studies on it indicate that political parties' ideological beliefs on sociopolitical issues may influence the users political leaning.  ...  We use R for our stance prediction experiments. We use users' political party information as gold truth for our party prediction experiments.  ... 
doi:10.1007/978-3-319-03260-3_16 fatcat:hz45hvedvjgizju73mbnuketpa

Cross-Cutting Political Awareness through Diverse News Recommendations [article]

Bibek Paudel, Abraham Bernstein
2019 arXiv   pre-print
Our research on recommendation diversity and political polarization helps us to develop algorithms that measure each user's reaction %to diverse viewpoints and adjust the recommendation accordingly.  ...  This limits users' exposure to diverse viewpoints and potentially increases political polarization.  ...  As a next step, we are using these insights to deploy and test them in a large-scale experiment involving multiple news producers and consumers.  ... 
arXiv:1909.01495v1 fatcat:m5nkz3tdpncslgibl4bcgvlica

Exploring the Role of Prior Beliefs for Argument Persuasion

Esin Durmus, Claire Cardie
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
religious ideology.  ...  To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader level factors: political and  ...  We also treat this as a classification task 5 using the BIGISSUES vectors for each user as features and the user's religious and political ideology as the labels to be predicted.  ... 
doi:10.18653/v1/n18-1094 dblp:conf/naacl/DurmusC18 fatcat:nq6zii6yqnbpniiziipaazblia

Fine-grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

Tiziano Fagni, Stefano Cresci
2022 The Journal of Artificial Intelligence Research  
Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of  ...  Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task.  ...  For each selected user, we use the party classifier C to predict the political relevance of all the tweets in the user's timeline.  ... 
doi:10.1613/jair.1.13112 fatcat:x3jhn7kerbebbnuev24kmxsd7m

Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets [article]

Margherita Gambini, Tiziano Fagni, Caterina Senette, Maurizio Tesconi
2022 arXiv   pre-print
The ground-truth user's stance may come from Voting Advice Applications, online tools that help citizens to identify their political leanings by comparing their political preferences with party political  ...  The work herein described focuses on a completely unsupervised stance detection framework that predicts the user's stance about specific social-political statements by exploiting content-based analysis  ...  Acknowledgments We are immensely grateful to the Observatory on Political Parties and Representation [12] for providing us the 2019 official position of the six major Italian parties about 20 political  ... 
arXiv:2204.10710v1 fatcat:7fml5khur5hcfaty2ahpwc3tjq

Towards Understanding Persuasion in Computational Argumentation [article]

Esin Durmus
2021 arXiv   pre-print
We find that the users' prior beliefs and social interactions play an essential role in predicting their success in persuasion.  ...  Finally, we explore the importance of incorporating contextual information to predict argument impact and show improvements compared to encoding only the text of the arguments.  ...  ideology, income level, education level, and the political party they support.  ... 
arXiv:2110.01078v1 fatcat:dss7or3cmbas3fdrcx25xzyxma

Political Dimensionality Estimation Using a Probabilistic Graphical Model

Yoad Lewenberg, Yoram Bachrach, Lucas Bordeaux, Pushmeet Kohli
2016 Conference on Uncertainty in Artificial Intelligence  
This paper attempts to move beyond the left-right characterization of political ideologies. We propose a trait based probabilistic model for estimating the manifold of political opinion.  ...  are enough to represent peoples' entire political opinion.  ...  When the ideology of parties spans multiple issues, representing these ideologies requires a political spectrum -a system for classifying different political positions using several geometric axes, each  ... 
dblp:conf/uai/LewenbergBBK16 fatcat:jijdhsv2qvbkndr7nrzart6bzq

An Integrated Model for User Attribute Discovery: A Case Study on Political Affiliation Identification [chapter]

Swapna Gottipati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
2014 Lecture Notes in Computer Science  
Our model achieved an accuracy of 70.1% for user party detection task.  ...  We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines.  ...  [9] proposed a system that uses linguistic analysis to generate attitude vectors on ideological datasets.  ... 
doi:10.1007/978-3-319-06608-0_36 fatcat:o6xkausj3fg5ngwq2mkimbjpna

Stance Detection on Social Media: State of the Art and Trends [article]

Abeer AlDayel, Walid Magdy
2020 arXiv   pre-print
Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be seen sub-optimal.  ...  An exhaustive review of stance detection techniques on social media ispresented, including the task definition, the different types of targets in stance detection, the features set used, and the variousmachine  ...  a user's ideological stance for an event.  ... 
arXiv:2006.03644v1 fatcat:hw3qqg2k3vbkjodef764c46ajy

Predicting Online Islamophobic Behavior after #ParisAttacks

Kareem Darwish, Walid Magdy, Afshin Rahimi, Timothy Baldwin, Norah Abokhodair
2018 Journal of Web Science  
on Twitter to build a classifier to predict post-event stance.  ...  Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event.  ...  Subsequent qualitative analysis of these features can shed light on personal, social and political attributes that are predictive of a user's stance.  ... 
doi:10.1561/106.00000013 dblp:journals/jws/DarwishMRBA18 fatcat:j6dtiw4snvfq3dpp33drfjrioe

A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches [article]

Prasad hajare, Sadia Kamal, Siddharth Krishnan, Arunkumar Bagavathi
2021 arXiv   pre-print
to predict political bias.  ...  Political bias in social media has been studied in multiple viewpoints like media bias, political ideology, echo chambers, and controversies using machine learning pipelines.  ...  political parties.  ... 
arXiv:2109.09014v1 fatcat:e6vwafinfrel7ellyygla7qfr4

The politics and analytics of health policy

Calum R. Paton
2014 International Journal of Health Policy and Management  
politics to choice amongst different tribes of technocrats using different discourse for their core party support from that which they use to the centre-ground, and which constrains their policy when  ...  ' Cold War of the 1950s, but in the sense that 'we are all capitalists now' , a stance often disguised as the seemingly more neutral statement that the scope of politics is diminished by allegedly inexorable  ... 
doi:10.15171/ijhpm.2014.26 pmid:24757685 pmcid:PMC3992783 fatcat:tfbdg2v7xjghzh6bjuristmw3i

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

Preethi Lahoti, Kiran Garimella, Aristides Gionis
2017 arXiv   pre-print
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.  ...  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  ...  We de ne ideology based on the policy dimension that articulates a user's political preference. Our de nition is inspired by work in political-science literature, such as work by Bafumi et al.  ... 
arXiv:1711.10251v1 fatcat:uvmme6ehfjcfzb2t3qb2gubicm

Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity [article]

Arkaitz Zubiaga, Bo Wang, Maria Liakata, Rob Procter
2018 arXiv   pre-print
Social media and data mining are increasingly being used to analyse political and societal issues.  ...  Citizens with common national identities can also vote for parties with different political ideologies, and their national identities can be instead motivated by cultural and linguistic backgrounds [7  ...  supporters of other parties.  ... 
arXiv:1702.08388v3 fatcat:hhgu2owgjzdn5oxksewl5ihkeq

Facilitating Diverse Political Engagement with the Living Voters Guide

Deen G. Freelon, Travis Kriplean, Jonathan Morgan, W. Lance Bennett, Alan Borning
2012 Journal of Information Technology & Politics  
In this article, we use a point view as an indicator of a user's desire to access both pros and cons. Like point expansions, point views are not a precise measure of a user's reading activity.  ...  to a politics of mobilization and ideological community-building (Farrell & Drezner, 2008; Karpf, 2008; Kerbel, 2009 ).  ... 
doi:10.1080/19331681.2012.665755 fatcat:qtvrw4uhifffzkebtl3qeswm54
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