TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multi-task multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory.

[1]  Yi Li,et al.  Opinion-aware Knowledge Graph for Political Ideology Detection , 2017, IJCAI.

[2]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[3]  Roberto Cipolla,et al.  Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Yanfang Ye,et al.  Heterogeneous Graph Attention Network , 2019, WWW.

[5]  Christopher H. Achen.  Mass Political Attitudes and the Survey Response , 1975, American Political Science Review.

[6]  Jie Tang,et al.  Representation Learning for Attributed Multiplex Heterogeneous Network , 2019, KDD.

[7]  Xiao-Ming Wu,et al.  Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.

[8]  Aron Culotta,et al.  Predicting the Demographics of Twitter Users from Website Traffic Data , 2015, AAAI.

[9]  Jianfeng Gao,et al.  Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.

[10]  Jacob Ratkiewicz,et al.  Predicting the Political Alignment of Twitter Users , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[11]  Theresa Kuhn,et al.  The national boundaries of solidarity: a survey experiment on solidarity with unemployed people in the European Union , 2019, European Political Science Review.

[12]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.

[13]  Sandeepa Kannangara,et al.  Mining Twitter for Fine-Grained Political Opinion Polarity Classification, Ideology Detection and Sarcasm Detection , 2018, WSDM.

[14]  Philip Resnik,et al.  Political Ideology Detection Using Recursive Neural Networks , 2014, ACL.

[15]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[16]  Jacob Ratkiewicz,et al.  Political Polarization on Twitter , 2011, ICWSM.

[17]  Mariano Torcal,et al.  Trust across political conflicts: Evidence from a survey experiment in divided societies , 2019 .

[18]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[19]  Danqi Chen,et al.  Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.

[20]  Lyle H. Ungar,et al.  Beyond Binary Labels: Political Ideology Prediction of Twitter Users , 2017, ACL.

[21]  K. T. Poole,et al.  A Spatial Model for Legislative Roll Call Analysis , 1985 .

[22]  Minyi Guo,et al.  SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction , 2017, WSDM.

[23]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[24]  Le Song,et al.  Heterogeneous Graph Neural Networks for Malicious Account Detection , 2018, CIKM.

[25]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[26]  Nitesh V. Chawla,et al.  Heterogeneous Graph Neural Network , 2019, KDD.

[27]  Soroush Vosoughi,et al.  Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning , 2017, ACL.

[28]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[29]  Dan Goldwasser,et al.  Identifying Stance by Analyzing Political Discourse on Twitter , 2016, NLP+CSS@EMNLP.

[30]  Joshua D. Clinton,et al.  The Statistical Analysis of Roll Call Data , 2004, American Political Science Review.

[31]  Samy Bengio,et al.  Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks , 2019, KDD.

[32]  Qiang Yang,et al.  An Overview of Multi-task Learning , 2018 .

[33]  Jure Leskovec,et al.  How Powerful are Graph Neural Networks? , 2018, ICLR.

[34]  Yupeng Gu,et al.  Ideology Detection for Twitter Users with Heterogeneous Types of Links , 2016, ArXiv.

[35]  Preslav Nakov,et al.  Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media , 2019, NAACL.

[36]  Gary Pollock,et al.  Populism, Ideology and Contradiction: Mapping Young People's Political Views , 2015 .

[37]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[38]  Yizhou Sun,et al.  Mining Heterogeneous Information Networks: Principles and Methodologies , 2012, Mining Heterogeneous Information Networks: Principles and Methodologies.

[39]  Chang Sup Park,et al.  Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement , 2013, Comput. Hum. Behav..

[40]  Philip Resnik,et al.  Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress , 2015, ACL.

[41]  Iryna Gurevych,et al.  Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks , 2019, EMNLP.

[42]  Cao Xiao,et al.  FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling , 2018, ICLR.

[43]  Jaewoo Kang,et al.  Graph Transformer Networks , 2019, NeurIPS.

[44]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.