Beyond the Bubble: Assessing the Diversity of Political Search Results

Abstract Search engines are among the most popular online services used in a range of contexts, including to find information on political issues. As such, they increasingly act as powerful mediators between news organizations and their audiences. The claim that search results are politically biased, while hardly new, has also recently received fresh support from President Donald Trump, who has blamed Google for unfairly prioritizing news outlets critical of his policies. In the context of elections, search engines may serve to inform citizens and have been argued to sway the choices of undecided voters. I examine two related issues: How political parties and candidates are represented in Google Search results and how strongly results in both Google Search and Google News are personalized in the run up to the 2017 German general elections. My results suggest that some parties and candidates are able to exert greater influence over how they are represented in search results than others, through a combination of local branch websites and social media presences. I furthermore find only a small share of results which differ from the mainstream, while controlling for time, language, and location, calling into question the validity of the filter bubble concept.

[1]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

[2]  Judit Bar-Ilan,et al.  Google Bombing from a Time Perspective , 2007, J. Comput. Mediat. Commun..

[3]  S. Oates,et al.  The Google voter: search engines and elections in the new media ecology , 2018 .

[4]  Filip Radlinski,et al.  Improving personalized web search using result diversification , 2006, SIGIR.

[5]  Andreas Graefe,et al.  Burst of the Filter Bubble? , 2018 .

[6]  Dimitrios Giomelakis,et al.  Investigating Search Engine Optimization Factors in Media Websites , 2016 .

[7]  Abbe Mowshowitz,et al.  Measuring search engine bias , 2005, Inf. Process. Manag..

[8]  Sharad Goel,et al.  Filter Bubbles, Echo Chambers, and Online News Consumption , 2016 .

[9]  Susan L. Gerhart,et al.  Do Web search engines suppress controversy? , 2004, First Monday.

[10]  Kurt Hornik,et al.  ctree : Conditional Inference Trees , 2015 .

[11]  Theo Röhle Desperately seeking the consumer: Personalized search engines and the commercial exploitation of user data , 2007, First Monday.

[12]  Thorsten Joachims,et al.  In Google We Trust: Users' Decisions on Rank, Position, and Relevance , 2007, J. Comput. Mediat. Commun..

[13]  David Lazer,et al.  Location, Location, Location: The Impact of Geolocation on Web Search Personalization , 2015, Internet Measurement Conference.

[14]  Rasmus Kleis Nielsen,et al.  Dealing with digital intermediaries: A case study of the relations between publishers and platforms , 2018, New Media Soc..

[15]  Eric Goldman,et al.  Search Engine Bias and the Demise of Search Engine Utopianism , 2006 .

[16]  Tarleton Gillespie,et al.  The politics of ‘platforms’ , 2010, New Media Soc..

[17]  Jacob Ørmen,et al.  Googling the news: Opportunities and challenges in studying news events through Google Search , 2016 .

[18]  Richard Fletcher,et al.  Are people incidentally exposed to news on social media? A comparative analysis , 2018, New Media Soc..

[19]  N. Calcutt Location, Location, Location? , 2013, Diabetes.

[20]  Elizabeth Van Couvering,et al.  Is Relevance Relevant? Market, Science, and War: Discourses of Search Engine Quality , 2007, J. Comput. Mediat. Commun..

[21]  Grant Blank,et al.  The echo chamber is overstated: the moderating effect of political interest and diverse media , 2018 .

[22]  Taina Bucher,et al.  Want to be on the top? Algorithmic power and the threat of invisibility on Facebook , 2012, New Media Soc..

[23]  Abbe Mowshowitz,et al.  Assessing bias in search engines , 2002, Inf. Process. Manag..

[24]  Heather Ford,et al.  Keeping Ottawa Honest—One Tweet at a Time? Politicians, Journalists, Wikipedians and Their Twitter Bots , 2016 .

[25]  Christopher Olston,et al.  Search result diversity for informational queries , 2011, WWW.

[26]  Mike Thelwall,et al.  Search engine coverage bias: evidence and possible causes , 2004, Inf. Process. Manag..

[27]  Balachander Krishnamurthy,et al.  Measuring personalization of web search , 2013, WWW.

[28]  Michael Zimmer,et al.  The Externalities of Search 2.0: The Emerging Privacy Threats when the Drive for the Perfect Search Engine meets Web 2.0 , 2008, First Monday.

[29]  Laura A. Granka The Politics of Search: A Decade Retrospective , 2010, Inf. Soc..

[30]  Damian Trilling,et al.  Should We Worry About Filter Bubbles? , 2016 .

[31]  Pablo J. Boczkowski,et al.  The Relevance of Algorithms , 2013 .

[32]  Eli Pariser,et al.  The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think , 2012 .

[33]  Wei Chu,et al.  Modeling the impact of short- and long-term behavior on search personalization , 2012, SIGIR '12.

[34]  N. Newman,et al.  Reuters Institute Digital News Report 2019 , 2019 .

[35]  Matthew Fuller,et al.  Personal Web searching in the age of semantic capitalism: Diagnosing the mechanisms of personalisation , 2011, First Monday.

[36]  M. Carlson,et al.  The Robotic Reporter , 2015 .

[37]  Amanda Spink,et al.  A study of results overlap and uniqueness among major Web search engines , 2006, Inf. Process. Manag..

[38]  Lada A. Adamic,et al.  Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.

[39]  Eszter Hargittai,et al.  The Social, Political, Economic, and Cultural Dimensions of Search Engines: An Introduction , 2007, J. Comput. Mediat. Commun..

[40]  R. Nielsen,et al.  “I Just Google It”: Folk Theories of Distributed Discovery , 2018 .

[41]  Roberto Burguet,et al.  In Google We Trust? , 2014 .

[42]  Mark Coddington Clarifying Journalism’s Quantitative Turn , 2015 .

[43]  Bernhard Rieder,et al.  Conflicts of interest and incentives to bias: A microeconomic critique of Google’s tangled position on the Web , 2014, New Media Soc..

[44]  Helen Nissenbaum,et al.  Shaping the Web: Why the Politics of Search Engines Matters , 2000, Inf. Soc..

[45]  Christian Nuernbergk,et al.  Conversations and Campaign Dynamics in a Hybrid Media Environment: Use of Twitter by Members of the German Bundestag , 2016 .

[46]  Ronald E. Robertson,et al.  The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections , 2015, Proceedings of the National Academy of Sciences.

[47]  Rachel E. Dwyer Expanding Homes and Increasing Inequalities: U.S. Housing Development and the Residential Segregation of the Affluent , 2007 .

[48]  R. Kitchin,et al.  Thinking critically about and researching algorithms , 2014, The Social Power of Algorithms.

[49]  Ingmar Weber,et al.  Political Insights: Exploring partisanship in Web search queries , 2012, First Monday.

[50]  Natascha Just,et al.  Governance by algorithms: reality construction by algorithmic selection on the Internet , 2017 .

[51]  Abbe Mowshowitz,et al.  Bias on the web , 2002, CACM.

[52]  Karen Yeung,et al.  ‘Hypernudge’: Big Data as a mode of regulation by design , 2016, The Social Power of Algorithms.

[53]  Nicholas Diakopoulos,et al.  Algorithmic Transparency in the News Media , 2017 .

[54]  Mark P. J. van der Loo,et al.  The stringdist Package for Approximate String Matching , 2014, R J..

[55]  Mike Ananny,et al.  Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability , 2018, New Media Soc..

[56]  Veronika Karnowski,et al.  From incidental news exposure to news engagement. How perceptions of the news post and news usage patterns influence engagement with news articles encountered on Facebook , 2017, Comput. Hum. Behav..

[57]  Eli Pariser FILTER BUBBLE: Wie wir im Internet entmündigt werden , 2012 .

[58]  S. Van Leuven,et al.  Online And Newsworthy , 2018, Digital Journalism.

[59]  Engin Bozdag,et al.  Bias in algorithmic filtering and personalization , 2013, Ethics and Information Technology.