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The truth behind filter bubbles: bursting some myths

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The truth behind filter bubbles: bursting some myths

  2. 2. BACKGROUND People use services like Facebook, Twitter, Google, Apple News, etc. to get news, and some of the news people see has been selected automatically by algorithms. Selection decisions are made by algorithms using data about our interests and preference—and this could reinforce existing consumption patterns. • Echo chambers: Where we are over-exposed to news we like or agree with—distorting our perception of reality. • Filter bubbles: Where news we dislike or disagree with is automatically filtered out—narrowing what we know.
  4. 4. DIGITAL NEWS REPORT World’s largest annual survey of news audiences 38 markets, 5 continents (mostly Europe) 75,000 respondents Polling mainly by YouGov, questionnaire by us
  5. 5. DIGITAL NEWS REPORT What is people’s main source of news? (Newman et al., 2019) 46% 41% 7% 6% 0% 25% 50% Online (inc. social media) TV Print Radio Q4. You say you’ve used these sources of news in the last week , which would you say is your MAIN source of news? Base: All who used a source of news in the last week. All markets = 73,527.
  6. 6. DIGITAL NEWS REPORT What is people’s main source of news (by age)? (Newman et al., 2019) 67% 5% 5% 23% 61% 5% 6% 28% 53% 5% 7% 36% 43% 6% 7% 44% 30% 9% 7% 54% 0% 25% 50% 75% Online Print Radio TV 18-24 25-34 35-44 45-54 55+ Q4. You say you’ve used these sources of news in the last week , which would you say is your MAIN source of news? Base: All who used a source of news in the last week in all markets. 18-24 = 7,929, 25-34 = 12,836, 35-44 = 13,619, 45-54 = 13,038, 55+ = 26,105.
  7. 7. DIGITAL NEWS REPORT What proportion use social media for news (since 2013)? (Newman et al., 2019) 0% 25% 50% 75% 2013 2014 2015 2016 2017 2018 2019 German y Austria Spain UK USA Q3. Which, if any, of the following have you used in the last week as a source of news? Base: Total sample in each country in each year ~= 2,000.
  8. 8. DIGITAL NEWS REPORT What proportion use each social network for news? (Newman et al., 2019)
  9. 9. DIGITAL NEWS REPORT What is people’s main way of getting to news online? (Newman et al., 2018)
  10. 10. PERSONALIZATION Two types of personalization (Zuiderveen Borgesius et al., 2016): • Self-selected personalization • The personalization people do themselves • Selective exposure (Stroud, 2011; Iyengar & Hahn, 2009) • Pre-selected personalization • The personalization done for people (usually by algorithms) • Filter bubbles (Pariser, 2011), echo chambers (Sunstein, 2017) Very important not to compare the effect of pre-selected personalization with a hypothetical world where people do not personalize their news consumption through self-selected personalization.
  11. 11. AUDIENCE FRAGMENTATION The power of self selection offline (Fletcher & Nielsen, 2017). UK offline audiences UK online audiences
  12. 12. INCIDENTAL EXPOSURE What is the effect of social media use on people’s news diets? • Social media combines self- selected personalization with pre-selected personalization. • Algorithms might hide news from people who are not interested in it
  13. 13. INCIDENTAL EXPOSURE Compared news diets of people who do not use social media at all with those who use it but not for news (Fletcher & Nielsen, 2018a) UK, USA, Italy, Australia Studied the effect on different demographic groups and different social networks
  14. 14. INCIDENTAL EXPOSURE Social media boosts people’s news increasing diversity of sources. • People who use social media for other reasons are incidentally exposed to news. • The boost is stronger for young people are those uninterested in news • Effect stronger for YouTube and Twitter than for Facebook
  15. 15. INCIDENTAL EXPOSURE • Most people are not that interested in the news • The web is a high-choice media environment, and its easy to opt out of news (Prior, 2005) • But social media “incidentally exposes” people to news– shows people news even when they are not looking for it.
  16. 16. AUTOMATED SERENDIPITY What about using search engines for news? • Different from social media because people are using search engines to intentionally find news. • Will search engines only show right- wing sources to right-wing users, and vice-versa?
  17. 17. AUTOMATED SERENDIPITY Compared news diets of people who search for news with people who do not (Fletcher & Nielsen, 2018b) UK, USA, Germany, Spain Studied news diets in terms of diversity and balance
  18. 18. AUTOMATED SERENDIPITY Automated serendipity diversifies people’s news use • People who use search engines use more sources • … are more likely to use both left- and right- leaning sources • … and have news diets that are more balanced between left and right
  19. 19. AUTOMATED SERENDIPITY “Users with different political leanings from different states were recommended very similar news, challenging the assumption that algorithms necessarily encourage echo chambers.” (Nechushtai & Lewis 2019)
  20. 20. WORK IN PROGRESS Can be backed up with UK web tracking data (unpublished work in progress).
  21. 21. OTHER STUDIES No/weak evidence of filter bubbles/echo chambers: Dubois & Blank, 2018; Messing & Westwood, 2014; Barbera et al., 2015; Fletcher & Nielsen, 2017, 2018 Mixed evidence: Bakshy et al., 2015; Flaxman et al., 2016 Strong evidence: …
  22. 22. POLARIZATION But there might be different problems with social media news use… Some evidence that exposure to diverse views on Twitter is leading to the polarization of political attitudes (Bail et al., 2018)
  24. 24. POLARIZATION Online news audience polarization higher than offline in most cases (Fletcher, Cornia & Nielsen, 2019)
  25. 25. FUNDING JOURNALISM There might be more pressing problems… “news itself has never been financially viable as a market-based good [and] has always been primarily financed by arrangements based on income derived from sources other than selling news to consumers” (Picard, 2014, p. 51)
  27. 27. ONLINE ONLY? What happened to the Independent after going online only? (Thurman & Fletcher, 2018)
  28. 28. NEWS ATTRIBUTION Do people remember where they get their news? (Kalogeropoulos, Fletcher & Neilsen 2019)
  29. 29. PAYING FOR ONLINE NEWS Growth in the number of paywalls (Simon & Graves 2019). Only a minority pay for online news (Newman et al., 2019). Slight increases in online news payment since 2014. Nordic countries are doing quite well. UK and Germany not doing so well.
  30. 30. PAYING FOR OTHER MEDIA Millions of global paid subscribers (2018)
  31. 31. TRUST IN THE NEWS In most countries fewer than half trust most news most of the time (Newman et al., 2019)
  32. 32. TRUST IN THE NEWS … and the proportion that trust most news most of the time is falling in many countries (Newman et al., 2019).
  33. 33. CONCLUSION At the moment the evidence suggests that online news use on search and social media is more diverse, but this diversity might be polarizing. In some ways this is the opposite of what the filter bubble hypothesis predicted. We should continue to critically examine the effects of algorithmic selection on news use. But we should not let this prevent us from properly confronting the deeper causes of divisions in politics and society (Bruns, 2019).
  34. 34. REFERENCES • Bail, Christopher A., Lisa P. Argyle, Taylor W. Brown, John P. Bumpus, Haohen Chen, M. B. Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. 2018. ‘Exposure to Opposing Views on Social Media Can Increase Political Polarization’. Proceedings of the National Academy of Sciences in the United States of America 0 (0): 1–6. • Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. 2015. ‘Exposure to Ideologically Diverse News and Opinion on Facebook’. Science 348: 1130–32. • Barberá, Pablo, John T. Jost, Jonathan Nagler, Joshua A. Tucker, and Richard Bonneau. 2015. ‘Tweeting from Left to Right: Is Online Political Communication More Than An Echo Chamber’. Psychological Science 26: 1531–42. • Bruns, Axel. 2019. Are Filter Bubbles Real? Cambridge: Polity. • 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): 729–45. • Flaxman, Seth, Sharad Goel, and Justin M. Rao. 2016. ‘Filter Bubbles, Echo Chambers, and Online News Consumption’. Public Opinion Quarterly 80: 298–320. • Fletcher, Richard, and Rasmus Kleis Nielsen. 2017. ‘Are News Audiences Increasingly Fragmented? A Cross‐National Comparative Analysis of Cross‐Platform News Audience Fragmentation and Duplication’. Journal of Communication 67 (4): 476–98. • ———. 2018a. ‘Are People Incidentally Exposed to News on Social Media? A Comparative Analysis’. New Media & Society 20 (7): 2450–68. • ———. 2018b. ‘Automated Serendipity: The Effect of Using Search Engines on the Diversity and Balance of News Repertoires’. Digital Journalism 8 (6): 976–89.
  35. 35. REFERENCES • Iyengar, Shanto, and Kyu S. Hahn. 2009. ‘Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use’. Journal of Communication 59 (1): 19–39. • Kalogeropoulos, Antonis, Richard Fletcher, and Rasmus Kleis Nielsen. 2019. ‘News Brand Attribution in Distributed Environments: Do People Know Where They Get Their News?’ New Media & Society 21 (3): 583–601. • Messing, Solomon, and Sean J. Westwood. 2014. ‘Selective Exposure in the Age of Social Media: Endorsements Trump Partisan Source Affiliation When Selecting News Online’. Communication Research 41: 1042–63. • Nechushtai, Efrat, and Seth C. Lewis. 2018. ‘What Kind of Gatekeepers Do We Want Machines to Be? Filter Bubbles, Fragmentation, and the Normative Dimensions of Algorithmic Recommendations’. Computers in Human Behavior 0 (0): 1–33. • Newman, Nic, Richard Fletcher, Antonis Kalogeropoulos, David A. L. Levy, and Rasmus Kleis Nielsen. 2018. ‘Reuters Institute Digital News Report 2018’. Oxford: Reuters Institute for the Study of Journalism. • Newman, Nic, Richard Fletcher, Antonis Kalogeropoulos, and Rasmus Kleis Nielsen. 2019. ‘Reuters Institute Digital News Report 2019’. Oxford: Reuters Institute for the Study of Journalism. • Pariser, Eli. 2011. Filter Bubbles: What the Internet Is Hiding from You. London: Penguin.
  36. 36. REFERENCES • Picard, Robert G. 2014. ‘State Support for News: Why Subsidies? Why Now? What Kinds?’ In State Aid for Newspapers: Theories, Cases, Actions, edited by Paul Murschetz. Berlin: Springer. • Prior, Markus. 2005. ‘News vs. Entertainment: How Increasing Media Choice Widens Gaps in Political Knowledge and Turnout’. American Journal of Political Science 49: 577–92. • Simon, Felix M., and Lucas Graves. 2019. ‘Pay Models for Online News in the US and Europe: 2019 Update’. Oxford: Reuters Institute for the Study of Journalism. • Stroud, Natalie Jomini. 2011. Niche News: The Politics of News Choice. Oxford: Oxford University Press. • Sunstein, Cass R. 2017. #republic: Divided Democracy in the Age of Social Media. Princeton: Princeton University Press. • Thurman, Neil, and Richard Fletcher. 2018. ‘Are Newspapers Heading Toward Post-Print Obscurity? A Case Study of The Independent’s Transition to Online-Only’. Digital Journalism 6 (8): 1003–17. • Zuiderveen Borgesius, Frederick J., Damian Trilling, Judith Möller, Balázs Bodó, Claes H. de Vreese, and Natali Helberger. 2016. ‘Should We Worry about Filter Bubbles?’ Internet Policy Review 5 (1): 1– 16.

Editor's Notes

  • Thank you for the introduction and for inviting me to give this lecture.

    I’m very glad to be here Salzburg to speak to you all.

    I’m going to try to describe how news use is changing in many countries across the world. What I really mean by this is how digital, online access is changing how we get news.

    But I also want to leave you with my overall assessment of the situation.