Blogs are Echo Chambers: Blogs are Echo Chambers

In the last decade, blogs have exploded in number, popularity and scope. However, many commentators and researchers speculate that blogs isolate readers in echo chambers, cutting them off from dissenting opinions. Our empirical paper tests this hypothesis. Using a hand-coded sample of over 1,000 comments from 33 of the world's top blogs, we find that agreement outnumbers disagreement in blog comments by more than 3 to 1. However, this ratio depends heavily on a blog's genre, varying between 2 to 1 and 9 to 1. Using these hand-coded blog comments as input, we also show that natural language processing techniques can identify the linguistic markers of agreement. We conclude by applying our empirical and algorithmic findings to practical implications for blogs, and discuss the many questions raised by our work.

[1]  J. Mill.  Principles of Political Economy , 2011, Forerunners of Realizable Values Accounting in Financial Reporting.

[2]  S. Schachter.  Deviation, rejection, and communication. , 1951, Journal of abnormal psychology.

[3]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[4]  H. Schachter,et al.  An application of acoustic surface wave devices to communication receivers , 1976, IEEE Transactions on Electron Devices.

[5]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[6]  Sara Kiesler,et al.  Social psychological aspects of computer-mediated communication , 1984 .

[7]  Muzafer Sherif,et al.  The Robbers Cave experiment : intergroup conflict and cooperation , 1988 .

[8]  J. Habermas,et al.  The structural transformation of the public sphere : an inquiry into a category of bourgeois society , 1989 .

[9]  Douglas G. Altman,et al.  Practical statistics for medical research , 1990 .

[10]  A. Feinstein,et al.  High agreement but low kappa: I. The problems of two paradoxes. , 1990, Journal of clinical epidemiology.

[11]  J. Habermas,et al.  The structural transformation of the public sphere : an inquiryinto a category of bourgeois society , 1991 .

[12]  L. R. Rasmussen,et al.  In information retrieval: data structures and algorithms , 1992 .

[13]  Nicholas Negroponte,et al.  Being Digital , 1995 .

[14]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[15]  Baron,et al.  Social Corroboration and Opinion Extremity , 1996, Journal of experimental social psychology.

[16]  A. Agresti,et al.  Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions , 1998 .

[17]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[18]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[19]  William Wresch,et al.  Democracy in the Digital Age: Challenges to Political Life in Cyberspace , 2001, Inf. Soc..

[20]  C. Stangor,et al.  Perceived consensus influences intergroup behavior and stereotype accessibility. , 2001, Journal of personality and social psychology.

[21]  C. Sunstein.  Republic.com , 2001 .

[22]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[23]  Barry Wellman,et al.  The Social Affordances of the Internet for Networked Individualism , 2006, J. Comput. Mediat. Commun..

[24]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[25]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[26]  Cass R. Sunstein,et al.  Why Societies Need Dissent , 2005 .

[27]  Julia Hirschberg,et al.  Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies , 2004, ACL.

[28]  Lois Ann Scheidt,et al.  Bridging the gap: a genre analysis of Weblogs , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[29]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[30]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[31]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[32]  David A. Huffaker,et al.  Gender, Identity, and Language Use in Teenage Blogs , 2006, J. Comput. Mediat. Commun..

[33]  Ido Dagan,et al.  A Probabilistic Classification Approach for Lexical Textual Entailment , 2005, AAAI.

[34]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.

[35]  Diana C. Mutz.  Hearing the Other Side: Deliberative versus Participatory Democracy , 2006 .

[36]  C. Sunstein.  Infotopia: How Many Minds Produce Knowledge , 2006 .

[37]  Patricia G. Lange.  What is your claim to flame? , 2006, First Monday.

[38]  Jerome Armstrong,et al.  Crashing the Gate: Netroots, Grassroots, and the Rise of People-Powered Politics , 2006 .

[39]  E. Hargittai,et al.  Cross-ideological discussions among conservative and liberal bloggers , 2007 .

[40]  Evgeniy Gabrilovich,et al.  Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis , 2007, IJCAI.

[41]  Dan Li,et al.  SPLOGS AND ABANDONED BLOGS: The perils of sampling bloggers and their blogs , 2008 .

[42]  Bill Bishop,et al.  The Big Sort: Why the Clustering of Like-Minded America Is Tearing Us Apart , 2008 .

[43]  Eric Baumer,et al.  Exploring the role of the reader in the activity of blogging , 2008, CHI.