A Joint Topic and Perspective Model for Ideological Discourse

Polarizing discussions on political and social issues are common in mass and user-generated media. However, computer-based understanding of ideological discourse has been considered too difficult to undertake. In this paper we propose a statistical model for ideology discourse. By ideology we mean "a set of general beliefs socially shared by a group of people." For example, Democratic and Republican are two major political ideologies in the United States. The proposed model captures lexical variations due to an ideological text's topic and due to an author or speaker's ideological perspective. To cope with the non-conjugacy of the logistic-normal prior we derive a variational inference algorithm for the model. We evaluate the proposed model on synthetic data as well as a written and a spoken political discourse. Experimental results strongly support that ideological perspectives are reflected in lexical variations.

[1]  Michael I. Jordan,et al.  A generalized mean field algorithm for variational inference in exponential families , 2002, UAI.

[2]  J. Carbonelljr,et al.  POLITICS: Automated ideological reasoning , 1978 .

[3]  Hagai Attias,et al.  A Variational Bayesian Framework for Graphical Models , 1999 .

[4]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[5]  Trevor J. Hastie,et al.  Discriminative vs Informative Learning , 1997, KDD.

[6]  Wei-Hao Lin,et al.  Which Side are You on? Identifying Perspectives at the Document and Sentence Levels , 2006, CoNLL.

[7]  T. V. Dijk,et al.  Ideology: A Multidisciplinary Approach , 1998 .

[8]  Eric P. Xing,et al.  Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model , 2007, AISTATS.

[9]  E. Xing.  On Topic Evolution , 2005 .

[10]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine-mediated learning.

[11]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[12]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[14]  R. Abelson,et al.  Computer Simulation of Individual Belief Systems1 , 1965 .

[15]  S. Carruthers,et al.  The media at war : communication and conflict in the twentieth century , 1999 .

[16]  Amr Ahmed,et al.  On Tight Approximate Inference of the Logistic-Normal Topic Admixture Model , 2007 .