Experimental research on selective exposure on online platforms is generally limited by a narrow focus on specific parts of the information selection process, rather than integrating the entire sequence of user-platform interactions. The current study, focusing on online search, incorporates the entire process that stretches from formulating an initial query to finally satisfying an information need. As such, it comprehensively covers how both users and platforms exercise agency by enabling and constraining each other in progressively narrowing down the available information. During a tailored online experiment, participants are asked to search for social and political information in a fully tracked, manipulated Google Search environment. Although the results show a structural impact of varying search result rankings, users still appear to be able to tailor their information exposure to maintain their prior beliefs, hence defying that algorithmic impact. This corroborates the need to conceptually and methodologically expand online selective exposure research.
AtkinC. (1973). Instrumental utilities and information seeking. In ClarkP. (Ed.), New models for mass communication research. Sage.
2.
BakshyE.MessingS.AdamicL. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160
Bar-IlanJ.KeenoyK.LeveneM.YaariE. (2009). Presentation bias is significant in determining user preference for search results – A user study. Journal of the American Society for Information Science, 60(1), 135–149. https://doi.org/10.1002/asi
CourtoisC.SlechtenL.CoenenL. (2018). Challenging google search filter bubbles in social and political information: Disconforming evidence from a digital methods case study. Telematics and Informatics, 35(7), 2006–2015. https://doi.org/10.1016/j.tele.2018.07.004
9.
CutrellE.GuanZ. (2007). What are you looking for? An eye-tracking study of information usage in web search. In RossonM. B.GilmoreD. (Eds.), CHI 2007: Proceedings of the SIGCHI conference on human factors in computing systems (pp. 407–416). AMC Press. https://doi.org/10.1145/1240624.1240690
10.
DonsbachW. (2009). Cognitive dissonance theory-A roller coaster career: How communication research adapted the theory of cognitive dissonance. In HartmannT. (Ed.), Media choice. A theoretical and empirical overview (pp. 128–148). Routledge. https://doi.org/10.4324/9780203938652
11.
Dvir-GvirsmanS.TsfatiY.Menchen-TrevinoE. (2014). The extent and nature of ideological selective exposure online: Combining survey responses with actual web log data from the 2013 Israeli elections. New Media and Society, 18(5), 857–877. https://doi.org/10.1177/1461444814549041
12.
DylkoI. B. (2016). How technology encourages political selective exposure. Communication Theory, 6(4), 389–409. https://doi.org/10.1111/comt.12089
13.
EpsteinR.RobertsonR. E. (2015). The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings of the National Academy of Sciences, 112(33), 4512–4521. https://doi.org/10.1073/pnas.1419828112
14.
EpsteinR.RobertsonR. E.LazerD.WilsonC. (2017). Suppressing the search engine manipulation effect (SEME). Proceedings of the ACM on Human-Computer Interaction, 1, 42:1–42:22. https://doi.org/10.1145/3134677
15.
FestingerL. (1957). A theory of cognitive dissonance. Standford University Press.
16.
FisherP.JonasE.FreyD.Schulz-HardtS. (2005). Selective exposure to information: The impact of information limits. European Journal of Social Psychology, 35(4), 469–492. https://doi.org/10.1002/ejsp.264
17.
GarrettK. R. (2009). Politically motivated reinforcement seeking: Reframing the selective exposure debate. Journal of Communication, 59(4), 676–699. https://doi.org/10.1111/j.1460-2466.2009.01452.x
18.
GillespieT. (2017). Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem. Information Communication and Society, 20(1), 63–80. https://doi.org/10.1080/1369118X.2016.1199721
19.
GrankaL. A.JoachimsT.GayG. (2004). Eye-tracking analysis of user behavior in WWW search. In SandersonM.JärvelinK. (Eds.), SIGR ‘04: Proceedings of the 27th annual international ACM SIGIR conference on research end development in information retrieval (pp. 478–479). AMC Press.
HallinD. C.ManciniP. (2004). Comparing media systems: Three models of media and politics. Cambridge University Press.
22.
HastallM. R.Knobloch-WesterwickS. (2013). Caught in the act: Measuring selective exposure to experimental online stimuli. Communication Methods and Measures, 7(2), 94–105. https://doi.org/10.1080/19312458.2012.761190
23.
HiemstraD. (2009). Information retrieval models. In GökerA.DaviesJ. (Eds.), Information retrieval: Searching in the 21st century (pp. 1–19). Wiley.
24.
IyengarS.HahnK. S.KrosnickJ. A.WalkerJ. (2008). Selective exposure to campaign communication: The role of anticipated agreement and issue public membership. Journal of Politics, 70(1), 186–200. https://doi.org/10.1017/S0022381607080139
25.
JansenB. J.SpinkA.BlakelyC.KoshmanS. (2007). Defining a session on web search engines. Journal of the American Society for Information Science and Technology, 58(6), 862–871. https://doi.org/10.1002/asi.20564
26.
JansenB. J.SpinkA.PedersenJ. (2005). A temporal comparison of AltaVista web searching. Journal of the American Society for Information Science and Technology, 56(6), 559–570. https://doi.org/10.1002/asi.20145
27.
JoachimsT.GrankaL.PanB.GayG.HembrookeH. (2005). Accurately interpreting clickthrough data as implicit feedback. In Baeza-YatesR.ZivianiN. (Eds.), SIGIR ‘05: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval (pp. 154–161). AMC Press. https://doi.org/10.1145/3130332.3130334
28.
JonasE.Schulz-HardtS.FreyD.ThelenN. (2001). Confirmation bias in sequential information search after preliminary decisions: An expansion of dissonance theoretical research on selective exposure to information. Journal of Personality and Social Psychology, 80(4), 557–571. https://doi.org/10.1037/0022-3514.80.4.557
29.
Kliman-SilverC.HannakA.LazerD.WilsonC.MisloveA. (2015). Location, location, location: The impact of geolocation on web search personalization. In ChoK.FukudaK.PaiV. (Eds.), IMC ‘15: Proceedings of the 2015 internet measurement conference (pp. 121–127). AMC Press.
30.
Knobloch-WesterwickS. (2012). Selective exposure and reinforcement of attitudes and partisanship before a presidential election. Journal of Communication, 62(4), 628–642. https://doi.org/10.1111/j.1460-2466.2012.01651.x
31.
Knobloch-WesterwickS. (2015). Choice and preference in media use: Advances in selective exposure theory and research. Routledge. https://doi.org/10.4324/9781315200682-9
32.
Knobloch-WesterwickS.JohnsonB. K.WesterwickA. (2015). Confirmation bias in online searches: Impacts of selective exposure before an election on political attitude strength and shifts. Journal of Computer-Mediated Communication, 20(2), 171–187. https://doi.org/10.1111/jcc4.12105
33.
Knobloch-WesterwickS.KleinmanS. B. (2012). Preelection selective exposure: Confirmation bias versus informational utility. Communication Research, 39(2), 170–193. https://doi.org/10.1177/0093650211400597
34.
Knobloch-WesterwickS.MothesC.JohnsonB. K.WesterwickA.DonsbachW. (2015). Political online information searching in Germany and the United States: Confirmation bias, source credibility, and attitude impacts. Journal of Communication, 65(3), 489–511. https://doi.org/10.1111/jcom.12154
35.
MessingS.WestwoodS. J. (2014). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research, 41(8), 1042–1063. https://doi.org/10.1177/0093650212466406
36.
MetzgerM. J.HartsellE. H.FlanaginA. J. (2020). Cognitive dissonance or credibility? A comparison of two theoretical explanations for selective exposure to partisan news. Communication Research, 47(1), 3–28. https://doi.org/10.1177/0093650215613136
37.
NelsonJ. L.WebsterJ. G. (2017). The myth of partisan selective exposure: A portrait of the online political news audience. Social Media and Society, 3(3). https://doi.org/10.1177/2056305117729314
38.
PanB.HembrookeH.JoachimsT.LorigoL.GayG.GrankaL. (2007). In Google we trust: Users’ decisions on rank, position, and relevance. Journal of Computer-Mediated Communication, 12(3), 801–823. https://doi.org/10.1111/j.1083-6101.2007.00351.x
39.
PariserE. (2011). The filter bubble: What the internet is hiding from you. Penguin Books.
RedlawskD. P.CivettiniA. J. W.EmmersonK. M. (2010). The affective tipping point: Do motivated reasoners ever “get it”?Political Psychology, 31(4), 563–593. https://doi.org/10.1111/j.1467-9221.2010.00772.x
43.
SanzE.StančíkJ. (2014). Your search – “ontological security” – matched 111,000 documents: An empirical substantiation of the cultural dimension of online search. New Media and Society, 16(2), 252–270. https://doi.org/10.1177/1461444813481198
44.
SchmidtA. L.ZolloF.VicarioM.Del BessiA.ScalaA.CaldarelliG.StanleyH. E.QuattrociocchiW. (2017). Anatomy of news consumption on Facebook. Proceedings of the National Academy of Sciences, 114(12), 3035–3039. https://doi.org/10.1073/pnas.1617052114
45.
SmythB.CoyleM.BriggsP. (2011). Communities, collaboration and recommender systems in personalized web search. In RicciF.RokachL.ShapiraB.KantorP. (Eds.), Recommender systems handbook (pp. 579–614). Springer US.
46.
SpaCy (2019, June24). Industrial-strength natural language processing in Python. https://spacy.io
TamineL.ChouquetC. (2017). On the impact of domain expertise on query formulation, relevance assessment and retrieval performance in clinical settings. Information Processing and Management, 53(2), 332–350. https://doi.org/10.1016/j.ipm.2016.11.004
51.
ThorsonK.WellsC. (2016). Curated flows: A framework for mapping media exposure in the digital age. Communication Theory, 26(3), 309–328. https://doi.org/10.1111/comt.12087
52.
UnkelJ.HaasA. (2017). The effects of credibility cues on the selection of search engine results. Journal of the Association for Information Science and Technology, 68(8), 50–62. https://doi.org/10.1002/asi
53.
ValckeP.OmbeletP-J.LambrechtI. (2016). Media pluralism monitor 2016 monitoring risks for media pluralism in the EU and beyond. Country report: Belgium. Centre for Media Pluralism and Media Freedom. https://doi.org/10.2870/93322
54.
VaughanL.ZhangY. (2007). Equal representation by search engines? A comparison of websites across countries and domains. Journal of Computer-Mediated Communication, 12(3), 888–909. https://doi.org/10.1111/j.1083-6101.2007.00355.x
55.
VogelT.WänkeM. (2016). Attitude and attitude change (2nd ed.). Routledge.
56.
WesterwickA.JohnsonB. K.Knobloch-WesterwickS. (2017). Confirmation biases in selective exposure to political online information: Source bias vs. content bias. Communication Monographs, 84(3), 343–364. https://doi.org/10.1080/03637751.2016.1272761
57.
WesterwickA.KleinmanS. B.Knobloch-WesterwickS. (2013). Turn a blind eye if you care: Impacts of attitude consistency, importance and credibility on seeking of political information and implications for attitudes. Journal of Communication, 63(3), 432–453. https://doi.org/10.1111/jcom.12028
58.
WhiteR. W.DumaisS. T.TeevanJ. (2009). Characterizing the influence of domain expertise on web search behavior. In Baeza-YatesR.BoldiP.Ribeiro-NetoB.CambazogluB. B. (Eds.), WSDM ‘09: Proceedings of the Second ACM International Conference on Web Search and Data Mining (pp. 132–141). ACM Press. https://doi.org/10.1145/1498759.1498819
59.
ZhangX.AnghelescuH.YuanX. (2005). Domain knowledge, search behavior, and search effectiveness of engineering and science students: An exploratory study. Information Research, 10(2), 270.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
