CoChat
Sign Up
Cited by 121Social Media and PoliticsMisinformation and Its ImpactsSentiment Analysis and Opinion Mining

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Blogs are Echo Chambers: Blogs are Echo Chambers

Éric Gilbert, Tony Bergstrom, Kyratso George Karahalios·2009·2009 42nd Hawaii International Conference on System Sciences
DOI 10.1109/hicss.2009.91
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/hicss.2009.91

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Echo chambers have been shown to exist in various forms of online media such as blogs, forums, and social media sites.

    “Echo chambers have been shown to exist in various forms of online media such as blogs (24), forums (25), and social media sites (26–28).”

    From The echo chamber effect on social media (2021) · cites this paper as [24]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 44

  1. glickman, 2005, a probabilistic classification approach for lexical textual entailment, Proc AAAI
  2. resnik, 1995, using information content to evaluate semantic similarity in a taxonomy, Proc IJCAI, 448
  3. gabrilovich, 2007, computing semantic relatedness using wikipedia-based explicit semantic analysis, Proc IJCAI, 6
  4. schachter, 1976, an application of acoustic surface wave devices to communication receivers, IEEE Transactions on Electron Devices, 23, 1053, 10.1109/T-ED.1976.18535 · DOI
  5. 10.3115/1218955.1219040 · DOI
  6. pennebaker, 1999, Linguistic Inquiry and Word Count
  7. 0, Firebug
  8. rennie, 2003, tackling the poor assumptions of naive bayes text classifiers, Proc ICML
  9. frakes, 1992, Information Retrieval Data Structures and Algorithms
  10. fellbaum, 1998, WordNet An Electronic Lexical Database, 10.7551/mitpress/7287.001.0001 · DOI
  11. 10.3115/1219840.1219885 · DOI
  12. 10.1037/0022-3514.80.4.645 · DOI
  13. facebook, 0, Politics
  14. 10.1007/s11127-007-9201-x · DOI · Paper page
  15. habermas, 1991, The Structural Transformation of the Public Sphere An Inquiry into a Category of Bourgois Society
  16. 0, Technorati Popular Top 100 blogs
  17. sunstein, 2006, Infotopia How Many Minds Produce Knowledge
  18. sunstein, 2003, Why Societies Need Dissent
  19. sunstein, 2002, Republic com
  20. witten, 2005, Data Mining Practical Machine Learning Tools and Techniques
  21. 10.3115/1073445.1073478 · DOI
  22. 10.3115/1610075.1610122 · DOI
  23. wilhelm, 2000, Democracy in the Digital Age Challenges to Political Life in Cyberspace
  24. 10.1111/j.1083-6101.2003.tb00216.x · DOI
  25. 10.1109/HICSS.2004.1265271 · DOI
  26. 10.1111/j.1083-6101.2005.tb00238.x · DOI
  27. 10.2307/2529310 · DOI · Paper page
  28. 10.5210/fm.v11i9.1393 · DOI
  29. 10.1080/13691180801947976 · DOI
  30. 10.1146/annurev.soc.27.1.415 · DOI · Paper page
  31. mill, 1848, Principles of Political Economy
  32. altman, 1990, Practical Statistics for Medical Research, 10.1201/9780429258589 · DOI · Paper page
  33. 10.2307/2685469 · DOI
  34. 10.1177/001316446002000104 · DOI · Paper page
  35. 10.1145/1134271.1134277 · DOI · Paper page
  36. 10.1017/CBO9780511617201 · DOI · Paper page
  37. bishop, 2008, The Big Sort Why the Clustering of Like-Minded America Is Tearing Us Apart
  38. 10.1145/1357054.1357228 · DOI · Paper page
  39. 10.3115/1118693.1118704 · DOI · Paper page
  40. 10.1006/jesp.1996.0024 · DOI
  41. negroponte, 1996, Being Digital
  42. armstrong, 2006, Crashing the Gate Netroots Grassroots and the Rise of People-Powered Politics
  43. 0, Chickenfoot
  44. 10.1007/BF00058655 · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Gilbert, É., Bergstrom, T., & Karahalios, K. G.
Affiliations
University of Illinois Urbana-Champaign (US)
ORCID
Éric Gilbert
Venue
2009 42nd Hawaii International Conference on System Sciences
Publisher
IEEE
Published
2009
Pages
1-10
ISSN
1530-1605 (ISSN-L)
DOI
10.1109/hicss.2009.91
OpenAlex
W2125815607
Cited by
121
References
4
Citations by year
2026: 1 · 2025: 4 · 2024: 6 · 2023: 2 · 2022: 2 · 2021: 10 · 2020: 6 · 2019: 11 · 2018: 14 · 2017: 7 · 2016: 14 · 2015: 12 · 2014: 10 · 2013: 10 · 2012: 2
FWCI
6.42
Type
conference paper
Language
en
Open access
closed
Locations
Hawaii International Conference on System Sciences (HICSS) (publishedVersion)
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
blogs, echo chambers, blog comments, agreement and disagreement, online discourse, natural language processing, blog genre

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review