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Part of
Conspiracy Theory Discourses
Edited by Massimiliano Demata, Virginia Zorzi and Angela Zottola
[Discourse Approaches to Politics, Society and Culture 98] 2022
► pp. 143–168

Chapter 7
Exploring the echo chamber concept

A linguistic perspective

ORCID logo | University of Modena and Reggio Emilia
| University of Modena and Reggio Emilia
Echo chambers have often been analyzed in social media studies as dysfunctions of communication fostering the polarization of debates and the spreading of conspiracy theories. On the other hand, from a linguistic perspective, very little research has been conducted on these themes. Our work aims to investigate the linguistic dimension of echo chambers, exploring them as ideological structures that are observable when ideological conflict occurs. Using word embedding and corpus-based discourse analysis, we investigate mediatic discourse on COVID-19 in the Coronavirus Corpus and in the Public Coronavirus Twitter Data Set. The analysis focuses on the semantic and pragmatic status of the word hoax, which emerges as a keyword characterizing the Twitter dataset. Our study shows how linguistic markers of ideological conflict can act as markers of position and affective/social identity.
Keywords: echo chambers, Twitter, corpus linguistics, word embedding, COVID-19, hoax
Article outline
  • 1.Introduction
  • 2.Background: Echo chambers
  • 3.Methods
  • 4.Corpus analysis: Preliminary overview
  • 5.Focus on hoax: Background
  • 6.Analyzing hoax in the News
  • 7.Hoax on Twitter
  • 8.Inside the inferential path: The embeddings
  • 9.Conclusions
  • Notes
  • References
Published online: 1 December 2022
DOI logo
https://doi.org/10.1075/dapsac.98.07bon
References (70)
References
Aupers, Stef, Dana Crăciun, and Andreas Őnnerfors. 2020. “Introduction”. In Routledge Handbook of Conspiracy Theories, ed by Michael Butter and Peter Knight, 387–390. London, New York: Routledge. DOI logoGoogle Scholar
Baker, Paul. 2006. Using Corpora in Discourse Analysis. London: Continuum. DOI logoGoogle Scholar
Baker, Paul, Costas Gabrielatos, Majid Khosravinik, Michal Krzyzanowski, Tony McEnery, and Ruth Wodak. 2008. “A Useful Methodological Synergy? Combining Critical Discourse Analysis and Corpus Linguistics to Examine Discourses of Refugees and Asylum Seekers in the UK Press.” Discourse and Society 19: 273–306. DOI logoGoogle Scholar
Baker, Paul, Costas Gabrielatos, and Tony McEnery. 2013. Discourse Analysis and Media Attitudes: The Representation of Islam in the British Press. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Bastos, Marco. 2021. “From Global Village to Identity Tribes: Context Collapse and the Darkest Timeline.” Media and Communication 9 (3): 16–24. DOI logoGoogle Scholar
Breeze, Ruth. 2020. “Exploring Populist Styles of Political Discourse in Twitter.” World Englishes 39 (4): 550–567. DOI logoGoogle Scholar
Bruns, Axel. 2019. “It’s not the Technology, Stupid: How the ‘Echo Chamber’ and ‘Filter Bubble’ Metaphors have Failed us.” International Association for Media and Communication Research Conference 2019.Google Scholar
Burgers, Christian, Melanie Jong Tjien Fa, and Anneke de Graaf. 2019. “A Tale of two Swamps: Transformations of a Metaphorical Frame in Online Partisan Media.” Journal of Pragmatics 141: 57–66. DOI logoGoogle Scholar
Calderón, Fernando H., Li-Kai Cheng, Ming-Jen Lin, Yen-Hao Huang, and Yi-Shin Chen. 2019. “Content-based Echo Chamber Detection on Social Media Platforms.” In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 597–600. IEEE. DOI logoGoogle Scholar
Chen, Emily, Kristina Lerman, and Emilio Ferrara. 2020. “Tracking Social Media Discourse about the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Dataset.” JMIR Public Health and Surveillance 6.2: e19273. DOI logoGoogle Scholar
Cinelli, Matteo, Emanuele Brugnoli, Ana Lucia Schmidt, Fabiana Zollo, Walter Quattrociocchi, and Scala Antonio. 2020. “Selective Exposure Shapes the Facebook News Diet.” PLoS ONE 15 (3): e0229129. DOI logoGoogle Scholar
Cinelli, Matteo, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021a. “The Echo Chamber Effect on Social Media.” Proceedings of the National Academy of Sciences 118 (9). e2023301118 DOI logoGoogle Scholar
Cinelli, Matteo, Andraž Pelicon, Igor Mozetič, Walter Quattrociocchi, Petra Kralj Novak, and Fabiana Zollo. 2021b. “Dynamics of Online Hate and Misinformation.” Scientific Reports 11 (1): 22083, 1–12. DOI logoGoogle Scholar
Conover, Michael, Jacob Ratkiewicz, Matthew Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. 2011. “Political Polarization on Twitter.” In Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, no. 1. DOI logoGoogle Scholar
Cota, Wesley, Silvio C. Ferreira, Romualdo Pastor-Satorras, and Michele Starnini. 2019. “Quantifying Echo Chamber Effects in Information Spreading over Political Communication Networks.” EPJ Data Science 8 (1): 1–13. DOI logoGoogle Scholar
Culpeper, Jonathan, Michael Haugh, and Dániel Z. Kádár (eds.). 2017. The Palgrave Handbook of Linguistic (Im)Politeness. London: Palgrave Macmillan. DOI logoGoogle Scholar
Davies, Mark. 2016. Corpus of News on the Web (NOW). Available online at [URL]
. 2019. The Coronavirus Corpus. Available online at [URL]
Del Vicario, Michela, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, Eugene, Stanley H., and Walter Quattrociocchi. 2016a. “The spreading of misinformation online.” Proceedings of the National Academy of Sciences 113 (3): 554–559. DOI logoGoogle Scholar
Del Vicario Michela, Vivaldo, Alessandro Bessi, Fabiana Zollo Fabiana, Antonio Scala Antonio, Guido Caldarelli Guido, Walter Quattrociocchi Walter. 2016b. “Echo Chambers: Emotional Contagion and Group Polarization on Facebook.” Scientific Reports. 6: 37825. DOI logoGoogle Scholar
Dubois, Elizabeth, and Grant Blank. 2018. “The Echo Chamber is Overstated: The Moderating Effect of Political Interest and Diverse Media.” Information, Communication & Society 21 (5): 729–745. DOI logoGoogle Scholar
Duseja, Nikita, and Harsh Jhamtani. 2019. “A Sociolinguistic Study of Online Echo Chambers on Twitter.” In Proceedings of the third workshop on natural language processing and computational social science, 78–83. DOI logoGoogle Scholar
Eco, Umberto. 1968. La struttura assente [The Absent Structure]. Milan, Bompiani.Google Scholar
. 1979. The Role of the Reader. Bloomington, IN: Indiana University Press.Google Scholar
Fairclough, Norman. 2010. Critical Discourse Analysis: The Critical Study of Language. London: Routledge.Google Scholar
Fernandez, Miriam, and Harith Alani. 2018. “Online Misinformation: Challenges and Future Directions.” In Companion Proceedings of the The Web Conference 2018, WWW ’18, International World Wide Web Conferences, Steering Committee, Republic and Canton of Geneva, CHE, 595–602. DOI logoGoogle Scholar
Fillmore, Charles J. 1976. “Frame Semantics and the Nature of Language.” Annals of the New York Academy of Sciences 280 (1): 20–32. DOI logoGoogle Scholar
Gabrielatos, Costas. 2018. “Keyness Analysis: Nature, Metrics and Techniques.” In Corpus Approaches to Discourse: A Critical Review ed. by Charlotte Taylor, and Anna Marchi, 225–258. London: Routledge. DOI logoGoogle Scholar
Gallacher, John D., and Marc W. Heerdink. 2019. “Measuring the effect of Russian Internet Research Agency information operations in online conversations.” Defence Strategic Communications 6: 155–198. DOI logoGoogle Scholar
Guo, Fangjian, Charles Blundell, Hanna Wallach, and Katherine Heller. 2015. “The Bayesian Echo Chamber: Modeling Social influence via Linguistic Accommodation.” In Artificial Intelligence and Statistics 38: 315–323. PMLR. Available from [URL]
Hargreaves, Eduardo, Claudio Agosti, Daniel Menasché, Giovanni Neglia, Alexandre Reiffers-Masson, and Eitan Altman. 2018. “Biases in the Facebook News Feed: A Case Study on the Italian Elections.” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 806–812. DOI logoGoogle Scholar
Hofstadter, Richard. 1964. “The Paranoid Style in American Politics.” Harper’s Magazine, 77–86. [URL] Retrieved on 31 March 2022.
Hunston, Susan. 2007. “Semantic Prosody Revisited.” International Journal of Corpus Linguistics 12.2: 249–268. DOI logoGoogle Scholar
Jacobson, Susan, Eunyoung Myung, and Steven L. Johnson. 2016. “Open Media or Echo Chamber: The Use of Links in Audience Discussions on the Facebook Pages of Partisan News Organizations.” Information, Communication & Society 19 (7): 875–891. DOI logoGoogle Scholar
Klapper, Joseph T. 1960. The Effects of Mass Communication. New York: Free Press.Google Scholar
Klein, Colin, Clutton Peter, Dunn, Adam G. 2019. “Pathways to Conspiracy: The Social and Linguistic Precursors of Involvement in Reddit’s Conspiracy Theory Forum.” PLoS ONE 14 (11): e0225098. DOI logoGoogle Scholar
Mautner, Gerlinde. 2001. “Checks and Balances: How Corpus Linguistics can Contribute to CDA.” In Methods of Critical Discourse Analysis, ed. by Ruth Wodak and Michael Meyer, 122–143. London: Sage.Google Scholar
Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. “Distributed Representations of Words and Phrases and their Compositionality.” arXiv preprint arXiv:1310.4546.Google Scholar
Nartey, Mark, and Isaac N. Mwinlaaru. 2019. “Towards a Decade of Synergising Corpus Linguistics and Critical Discourse Analysis: A Meta-analysis.” Corpora 14 (2): 203–235. DOI logoGoogle Scholar
Nguyen, C. Thi. 2020. “Echo Chambers and Epistemic Bubbles.” Episteme 17 (2): 141–161. DOI logoGoogle Scholar
Oakes, Michael P. 1998. Statistics for Corpus Linguistics. Edinburgh: Edinburgh University Press.Google Scholar
Oliveira, Ana Larissa Adorno Marciotto, and Marisa Mendonça Carneiro. 2020. “A Pragmatic View of Hashtags: The Case of Impoliteness and Offensive Verbal Behavior in the Brazilian Twitter.” Acta Scientiarum. Language and Culture 42, DOI logoGoogle Scholar
Oswald, Steve. 2016. “Conspiracy and Bias: Argumentative Features and Persuasiveness of Conspiracy Theories”. OSSA Conference Archive. 168. [URL]
Pariser, Eli. 2011. The Filter Bubble: What the Internet is Yiding from You. London: Penguin.Google Scholar
Quattrociocchi, Walter, Guido Caldarelli, and Antonio Scala. 2014. “Opinion Dynamics on Interacting Networks: Media Competition and Social Influence.” Scientific Reports 4 (1): 1–7.Google Scholar
Rogers, Richard. 2018. “Digital Traces in Context| Otherwise Engaged: Social Media from Vanity Metrics to Critical Analytics.” International Journal of Communication 12: 23.Google Scholar
Rosenblum, Nancy L., and Russell Muirhead. 2019. A Lot of People Are Saying. Princeton: Princeton University Press. DOI logoGoogle Scholar
Sanna, Leonardo, and Dario Compagno. 2020. “Implementing Eco’s Model Reader with Word Embeddings. An Experiment on Facebook Ideological Bots.” In JADT 2020: 15th International Conference on Statistical Analysis of Textual Data.Google Scholar
Sanna, Leonardo, Salvatore Romano, Giulia Corona, and Claudio Agosti. 2021. “YTTREX: Crowdsourced Analysis of YouTube’s Recommender System during COVID-19 Pandemic”. In Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, ed. by Juan-Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, and Hugo Alatrista-Salas. Vol 1410. Springer, Cham. DOI logoGoogle Scholar
Scott, Kate. 2015. “The Pragmatics of Hashtags: Inference and Conversational Style on Twitter.” Journal of Pragmatics 81: 8–20. DOI logoGoogle Scholar
Scott, Mike. 2020. WordSmith Tools (Version 8.0). Stroud: Lexical Analysis Software.Google Scholar
Sinclair, John. 1996. “The Search for Units of Meaning.” Textus 9 (1): 75–106.Google Scholar
Stelter, Brian. 2020. Hoax: Donald Trump, Fox News, and the Dangerous Distortion of Truth. New York, Simon & Schuster.Google Scholar
Stubbs, Michael. 1996. Text and Corpus Analysis: Computer-Assisted Studies of Language and Culture. Oxford: Blackwell.Google Scholar
. 2001. Words and Phrases: Corpus Studies of Lexical Semantics. Oxford: Blackwell.Google Scholar
Sunstein, Cass. 2007. Republic.com. 2.0 Princeton, NJ: Princeton University Press.Google Scholar
Tagg, Caroline, Philip Seargeant, and Amy Aisha Brown. 2017. Taking Offence on Social Media. Conviviality and Communication on Facebook. London: Palgrave. DOI logoGoogle Scholar
Törnberg, Petter. 2018. “Echo chambers and viral misinformation: Modeling fake news as complex contagion.” PLoS ONE 13 (9): e0203958. DOI logoGoogle Scholar
Törnberg, Petter, Claes Andersson, Kristian Lindgren, and Sven Banisch. 2021. “Modeling the Emergence of Affective Polarization in the Social Media Society.” PLoS ONE 16 (10): e0258259. DOI logoGoogle Scholar
Van Dijk, Teun A. 1998. Ideology: A Multidisciplinary Approach. London: Sage.Google Scholar
Van Eck, Christel W., Bob C. Mulder, and Sander van der Linden. 2021. “Echo Chamber Effects in the Climate Change Blogosphere.” Environmental Communication. DOI logoGoogle Scholar
Van Leeuwen, Theo. 2008. Discourse and Practice: New Tools for Critical Discourse Analysis. Oxford: Oxford University Press. DOI logoGoogle Scholar
Violi, Patrizia. 2000. “Prototypicality, Typicality and Context.” In Meaning and Cognition, ed. by Liliana Albertazzi, 103–123. Amsterdam: Benjamins. DOI logoGoogle Scholar
Wilson, Andrew. 2013. “Embracing Bayes Factors for Key Item Analysis in Corpus Linguistics.” In New Approaches to the Study of Linguistic Variability. Language Competence and Language Awareness in Europe, vol. 4, ed. by Markus Bieswanger, and Amei Koll-Stobbe, 3–11. Frankfurt: Peter Lang.Google Scholar
Wodak, Ruth. 2002. “Aspects of Critical Discourse Analysis.” Zeitschrift für Angewandte Linguistik 36 (10): 5–31.Google Scholar
Zappavigna, Michele. 2011. “Ambient Affiliation: A Linguistic Perspective on Twitter.” New Media & Society 13 (5): 788–806. DOI logoGoogle Scholar
. 2015. “Searchable Talk: The Linguistic Functions of Hashtags.” Social Semiotics 25 (3): 274–291. DOI logoGoogle Scholar
Zimmer, Franziska, Katrin Scheibe, Mechtild Stock, and Wolfgang G. Stock. 2019. “Fake News in Social Media: Bad Algorithms or Biased Users?” Journal of Information Science Theory and Practice 7 (2): 40–53.Google Scholar
Zollo, Fabiana, Alessandro Bessi, Michela Del Vicario, Antonio Scala, Guido Caldarelli, Louis Shekhtman, Shomo Havlin, and Walter Quattrociocchi. 2017. “Debunking in a World of Tribes.” PLoS ONE 12 (7): e0181821. DOI logoGoogle Scholar