Abstract
When tourists search information online, personalization algorithms tend to contextually filter the vast amount of information and provide them with a subset of information to increase relevance and avoid overload. However, limited attention is paid to the dark side of these algorithms. An influential critique of personalization algorithms is the filter bubble effect, a hypothesis that people are isolated in their own information bubble based on their prior online activities, resulting in narrowed perspectives and fewer discovery of new experiences. An important question, therefore, is whether algorithmic filtering leads to filter bubbles. We empirically explore this question in an online tourist information search with the three-dimensional ‘cascade’ tourist decision-making model in a two-step experiment. We train two virtual agents with polarized YouTube videos and manipulate them to conduct travel information searches from both off-site and on-site geolocations in Google Search. The first three pages of search results are collected and analyzed with two mathematical metrics and follow-up content analysis. The results do not show significant differences between the two virtual agents with polarized prior training. However, when search geolocations change from off-site to on-site, 39–69% of the search results vary. Additionally, this difference varies between search terms. In summary, our data show that while algorithmic filtering is robust in retrieving relevant search results, it does not necessarily show evidence of filter bubbles. This study provides theoretical and methodological implications to guide future research on filter bubbles and contextual personalization in online tourist information searches. Marketing implications are discussed.
This is a preview of subscription content, log in via an institution to check access.
(adapted from Moore et al. 2012)
Similar content being viewed by others
References
Alter A (2017) Irresistible: the rise of addictive technology and the business of keeping us hooked. Penguin, London
Alvarez MD, Campo S (2014) The influence of political conflicts on country image and intention to visit: a study of Israel’s image. Tour Manag 40:70–78. https://doi.org/10.1016/j.tourman.2013.05.009
Areeb QM, Nadeem M, Sohail SS et al (2023) Filter bubbles in recommender systems: fact or fallacy—a systematic review. Wiley Interdiscip Rev Data Min Knowl Discov 13:e1512. https://doi.org/10.1002/widm.1512
Aridor G, Goncalves D, Sikdar S (2020) Deconstructing the filter bubble: User decision-making and recommender systems. In: Proceedings of the 14th ACM conference on recommender systems, pp 82–91
Atkinson P (2022) Book review: are filter bubbles real? New Media Soc 24:1950–1952. https://doi.org/10.1177/14614448221101211
Bakshy E, Messing S, Adamic LA (2015) Exposure to ideologically diverse news and opinion on Facebook. Science 348:1130–1132. https://doi.org/10.1126/science.aaa1160
Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2:263–277. https://doi.org/10.1504/IJAHUC.2007.014070
Barbe D, Pennington-Gray L, Schroeder A (2018) Destinations’ response to terrorism on Twitter. Int J Tour Cities 4:495–512. https://doi.org/10.1108/IJTC-04-2018-0027
Bozdag E (2013) Bias in algorithmic filtering and personalization. Ethics Inf Technol 15:209–227. https://doi.org/10.1007/s10676-013-9321-6
Bozdag E, Timmermans J (2011) Values in the filter bubble ethics of personalization algorithms in cloud computing. In: 1st international workshop on values in design—building bridges between RE, HCI and ethics
Bozdag E, van den Hoven J (2015) Breaking the filter bubble: democracy and design. Ethics Inf Technol 17:249–265. https://doi.org/10.1007/s10676-015-9380-y
Bruns A (2019) Are filter bubbles real? Wiley, Hoboken
Bruns A (2021) Echo chambers? Filter bubbles? The misleading metaphors that obscure the real problem. Hate Speech and Polarization in Participatory Society. Routledge, London, pp 33–48
Bucher T (2012) Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media Soc 14:1164–1180. https://doi.org/10.1177/1461444812440159
Bureau of Consular Affairs USD of S (2021) Russia travel advisory. In: Travel.State.Gov. https://travel.state.gov/content/travel/en/traveladvisories/traveladvisories/russia-travel-advisory.html. Accessed 16 Nov 2021
Cao L, Luo J, Gallagher A et al (2010) A worldwide tourism recommendation system based on geotaggedweb photos. In: 2010 IEEE international conference on acoustics, speech and signal processing, pp 2274–2277
Cho J, Ahmed S, Hilbert M et al (2020) Do search algorithms endanger democracy? An experimental investigation of algorithm effects on political polarization. J Broadcast Electron Media 64:150–172. https://doi.org/10.1080/08838151.2020.1757365
Choe Y, Fesenmaier DR, Vogt C (2017) Twenty-five years past Vogt: assessing the changing information needs of American travellers. Information and communication technologies in tourism 2017. Springer, Cham, pp 489–502
Cohen E (1972) Toward a sociology of international tourism. Soc Res 39:164–182. https://www.jstor.org/stable/40970087
Dahlgren PM (2021) A critical review of filter bubbles and a comparison with selective exposure. Nord Rev 42:15–33. https://doi.org/10.2478/nor-2021-0002
Eady G, Nagler J, Guess A et al (2019) How many people live in political bubbles on social media? Evidence from linked survey and Twitter data. SAGE Open 9(1). https://doi.org/10.1177/2158244019832705
eMarketer (2016) Most travelers use search engines when planning a trip. https://www.emarketer.com/Article/Most-Travelers-Use-Search-Engines-Planning-Trip/1013745. Accessed 27 Oct 2021
Femenia-Serra F, Gretzel U, Alzua-Sorzabal A (2022) Instagram travel influencers in# quarantine: communicative practices and roles during COVID-19. Tour Manag 89:104454. https://doi.org/10.1016/j.tourman.2021.104454
Flaxman S, Goel S, Rao JM (2016) Filter bubbles, echo chambers, and online news consumption. Public Opin Q 80:298–320. https://doi.org/10.1093/poq/nfw006
Fletcher R, Nielsen RK (2018) Automated serendipity: the effect of using search engines on news repertoire balance and diversity. Digit J 6:976–989. https://doi.org/10.1080/21670811.2018.1502045
Fodness D, Murray B (1997) Tourist information search. Ann Tour Res 24:503–523. https://doi.org/10.1016/S0160-7383(97)00009-1
Fodness D, Murray B (1999) A model of tourist information search behavior. J Travel Res 37:220–230. https://doi.org/10.1177/004728759903700302
Ghezzi P, Bannister PG, Casino G et al (2020) Online information of vaccines: information quality, not only privacy, is an ethical responsibility of search engines. Front Med 7:400. https://doi.org/10.3389/fmed.2020.00400
Goh DH, Lee CS, Ang RP, Lee CK (2010) Determining services for the mobile tourist. J Comput Inf Syst 51:31–40. https://doi.org/10.1080/08874417.2010.11645447
Google Ads Help (n.d.) Introducing things to do on Google - Google Ads Help. In: Introd. Things Google. https://support.google.com/google-ads/answer/10723429?hl=en&ref_topic=10723126. Accessed 16 Apr 2022
Google Google-My Activity. https://myactivity.google.com/activitycontrols?settings=search&utm_source=my-activity&facs=1. Accessed 17 Mar 2022
Google Privacy & Terms (2021) Privacy policy—privacy & terms—Google. https://policies.google.com/privacy?hl=en#infocollect. Accessed 6 Nov 2021
Gupta DD (2014) Assessing the website effectiveness of top ten tourist attracting nations. Inf Technol Tour 14:151–175. https://doi.org/10.1007/s40558-014-0012-x
Gursoy D, McCleary KW (2004) An integrative model of tourists’ information search behavior. Ann Tour Res 31:353–373. https://doi.org/10.1016/j.annals.2003.12.004
Haim M, Graefe A, Brosius H-B (2018) Burst of the filter bubble?: effects of personalization on the diversity of Google News. Digit J 6:330–343. https://doi.org/10.1080/21670811.2017.1338145
Han H, Wang C, Zhao Y et al (2022) SSLE: a framework for evaluating the “Filter Bubble” effect on the news aggregator and recommenders. World Wide Web 25:1169–1195. https://doi.org/10.1007/s11280-022-01031-4
Hannak A, Sapiezynski P, Molavi Kakhki A et al (2013) Measuring personalization of web search. In: Proceedings of the 22nd international conference on World Wide Web-WWW’13. ACM Press, Rio de Janeiro, Brazil, pp 527–538
Hanqin ZQ, Lam T (1999) An analysis of Mainland Chinese visitors’ motivations to visit Hong Kong. Tour Manag 20:587–594. https://doi.org/10.1016/S0261-5177(99)00028-X
Hennig-Thurau T, Gwinner KP, Walsh G, Gremler DD (2004) Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? J Interact Mark 18:38–52. https://doi.org/10.1002/dir.10073
Henzinger M (2007) Search technologies for the internet. Science 317:468–471. https://doi.org/10.1126/science.1126557
Herdin T, Egger R (2018) Beyond the digital divide: tourism, ICTs and culture—a highly promising alliance. Int J Digit Cult Electron Tour 2:322. https://doi.org/10.1504/IJDCET.2018.092182
Hirschmeier S (2019) Towards explanations of anti-recommender content in public radio. In: Adjunct publication of the 27th conference on user modeling, adaptation and personalization—UMAP’19 Adjunct. ACM Press, Larnaca, Cyprus, pp 229–233
Hjalager A-M, Jensen JM (2012) A typology of travellers based on their propensity to go online before, during and after the trip. In: Fuchs M, Ricci F, Cantoni L (eds) Information and communication technologies in tourism 2012. Springer, Vienna, pp 96–107
Hussein E, Juneja P, Mitra T (2020) Measuring misinformation in video search platforms: an audit study on YouTube. Proc ACM Hum Comput Interact 4:1–27. https://doi.org/10.1145/3392854
Jaccard P (1901) Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull Soc Vaudoise Sci Nat 37:241–272
Jalilvand MR, Samiei N (2012) The effect of electronic word of mouth on brand image and purchase intention: an empirical study in the automobile industry in Iran. Mark Intell Plan 30:460–476. https://doi.org/10.1108/02634501211231946
Jamieson KH, Cappella JN (2008) Echo chamber: Rush Limbaugh and the conservative media establishment. Oxford University Press, Oxford
Jang S (2005) The past, present, and future research of online information search. J Travel Tour Mark 17:41–47
Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, Cambridge
Jansen BJ, Ciamacca CC, Spink A (2008) An analysis of travel information searching on the web. Inf Technol Tour 10:101–118. https://doi.org/10.3727/109830508784913121
Kaakinen M, Sirola A, Savolainen I, Oksanen A (2020) Shared identity and shared information in social media: development and validation of the identity bubble reinforcement scale. Media Psychol 23:25–51. https://doi.org/10.1080/15213269.2018.1544910
Kaluža J (2022) Habitual generation of filter bubbles: why is algorithmic personalisation problematic for the democratic public sphere? Javnost Public 29:267–283. https://doi.org/10.1080/13183222.2021.2003052
Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Enhancement of the neutrality in recommendation. In: Decisions@ RecSys. pp 8–14
Kang S, Jodice LW, Norman WC (2020) How do tourists search for tourism information via smartphone before and during their trip? Tour Recreat Res 45:57–68. https://doi.org/10.1080/02508281.2019.1627076
Keipi T, Näsi M, Oksanen A, Räsänen P (2016) Online hate and harmful content: cross-national perspectives. Taylor & Francis, London
Kliman-Silver C, Hannak A, Lazer D, et al (2015) Location, location, location: The impact of geolocation on web search personalization. In: Proceedings of the 2015 internet measurement conference, pp 121–127
Krafft TD, Gamer M, Zweig KA (2018) What did you see? Personalization, regionalization and the question of the filter bubble in Google’s search engine. ArXiv Prepr arXiv:181210943
Krippendorff K (2011) Computing Krippendorff’s alpha-reliability
Kulshrestha J, Eslami M, Messias J, et al (2017) Quantifying search bias: investigating sources of bias for political searches in social media. In: Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. pp 417–432
Lamsfus C, Wang D, Alzua-Sorzabal A, Xiang Z (2015) Going mobile: defining context for on-the-go travelers. J Travel Res 54:691–701. https://doi.org/10.1177/0047287514538839
Le H, Maragh R, Ekdale B, et al (2019) Measuring political personalization of Google News search. In: The world wide web conference. Association for Computing Machinery, New York, NY, USA, pp 2957–2963
Li KG, Mithas S, Zhang Z, Tam KY (2019) Does algorithmic filtering create a filter bubble? Evidence from Sina Weibo. Acad Manag Proc 2019:14168. https://doi.org/10.5465/AMBPP.2019.14168abstract
Liang S, Schuckert M, Law R, Masiero L (2017) The relevance of mobile tourism and information technology: an analysis of recent trends and future research directions. J Travel Tour Mark 34:732–748. https://doi.org/10.1080/10548408.2016.1218403
Litvin SW, Goldsmith RE, Pan B (2008) Electronic word-of-mouth in hospitality and tourism management. Tour Manag 29:458–468. https://doi.org/10.1016/j.tourman.2007.05.011
Luckerson V (2016) A decade ago, google bought YouTube—and it was the best tech deal ever. In: The Ringer. https://www.theringer.com/2016/10/10/16042354/google-youtube-acquisition-10-years-tech-deals-69fdbe1c8a06. Accessed 5 Nov 2021
Luo Q, Zhai X (2017) “I will never go to Hong Kong again!” How the secondary crisis communication of “Occupy Central” on Weibo shifted to a tourism boycott. Tour Manag 62:159–172. https://doi.org/10.1016/j.tourman.2017.04.007
MacQueen KM, McLellan E, Kay K, Milstein B (1998) Codebook development for team-based qualitative analysis. CAM J 10:31–36. https://doi.org/10.1177/1525822X980100020301
McAlone N (2016) Why Netflix thinks its personalized recommendation engine is worth $1 billion per year. In: Bus. Insid. https://www.businessinsider.com/netflix-recommendation-engine-worth-1-billion-per-year-2016-6. Accessed 11 Apr 2022
McCombs ME, Shaw DL (1972) The agenda-setting function of mass media. Public Opin Q 36:176–187. https://doi.org/10.1086/267990
McIntosh RW, Goeldner CR (1985) Tourism: principles, practices, philosophies. Wiley, Hoboken
McKercher B, Chow So-Ming B (2001) Cultural distance and participation in cultural tourism. Pac Tour Rev 5:23–32
Meehan K, Lunney T, Curran K, McCaughey A (2013) Context-aware intelligent recommendation system for tourism. In: 2013 IEEE international conference on pervasive computing and communications workshops (PERCOM workshops). IEEE, pp 328–331
Michiels L, Leysen J, Smets A, Goethals B (2022) What are filter bubbles really? A review of the conceptual and empirical Work. In: Adjunct proceedings of the 30th ACM conference on user modeling, adaptation and personalization. pp 274–279
Money RB, Crotts JC (2003) The effect of uncertainty avoidance on information search, planning, and purchases of international travel vacations. Tour Manag 24:191–202. https://doi.org/10.1016/S0261-5177(02)00057-2
Moore K, Smallman C, Wilson J, Simmons D (2012) Dynamic in-destination decision-making: an adjustment model. Tour Manag 33:635–645. https://doi.org/10.1016/j.tourman.2011.07.005
Mozeik CK, Beldona S, Cobanoglu C, Poorani A (2009) The adoption of restaurant-based e-service. J Foodserv Bus Res 12:247–265. https://doi.org/10.1080/15378020903158525
Nagpal M, Petersen JA (2021) Keyword selection strategies in search engine optimization: how relevant is relevance? J Retail 97:746–763. https://doi.org/10.1016/j.jretai.2020.12.002
Nechushtai E, Lewis SC (2019) What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations. Comput Hum Behav 90:298–307. https://doi.org/10.1016/j.chb.2018.07.043
Nikolov D, Oliveira DFM, Flammini A, Menczer F (2015) Measuring online social bubbles. PeerJ Comput Sci 1:e38. https://doi.org/10.7717/peerj-cs.38
O’Callaghan D, Greene D, Conway M et al (2015) Down the (white) rabbit hole: the extreme right and online recommender systems. Soc Sci Comput Rev 33:459–478. https://doi.org/10.1177/0894439314555329
Oh HC, Uysal M, Weaver PA (1995) Product bundles and market segments based on travel motivations: a canonical correlation approach. Int J Hosp Manag 14:123–137. https://doi.org/10.1016/0278-4319(95)00010-A
Osti L, Turner LW, King B (2009) Cultural differences in travel guidebooks information search. J Vacat Mark 15:63–78. https://doi.org/10.1177/1356766708098172
Pan B (2015) The power of search engine ranking for tourist destinations. Tour Manag 47:79–87. https://doi.org/10.1016/j.tourman.2014.08.015
Pan B, Fesenmaier DR (2006) Online information search: vacation planning process. Ann Tour Res 33:809–832. https://doi.org/10.1016/j.annals.2006.03.006
Pan B, Hembrooke H, Joachims T et al (2007a) In Google we trust: users’ decisions on rank, position, and relevance. J Comput Mediat Commun 12:801–823. https://doi.org/10.1111/j.1083-6101.2007.00351.x
Pan B, Litvin SW, O’Donnell TE (2007b) Understanding accommodation search query formulation: the first step in putting `heads in beds’. J Vacat Mark 13:371–381. https://doi.org/10.1177/1356766707081013
Pan B, MacLaurin T, Crotts JC (2007c) Travel blogs and the implications for destination marketing. J Travel Res 46:35–45. https://doi.org/10.1177/0047287507302378
Pan B, Lin MS, Liang Y et al (2021) Social, ethical, and moral issues in smart tourism development in destinations. J Smart Tour 1:9–17. https://doi.org/10.52255/smarttourism.2021.1.1.3
Pansari A, Mayer M (2006) This is a test. This is only a test. In: Off. Google Blog. https://googleblog.blogspot.com/2006/04/this-is-test-this-is-only-test.html. Accessed 31 Mar 2022
Papadamou K, Zannettou S, Blackburn J et al (2021) “It is just a flu”: assessing the effect of watch history on YouTube’s pseudoscientific video recommendations. Proc Int AAAI Conf Web Soc Media 16:723–734. https://doi.org/10.1609/icwsm.v16i1.19329
Pariser E (2011a) The filter bubble: how the new personalized web is changing what we read and how we think. Penguin, London
Pariser E (2011b) The filter bubble: what the Internet is hiding from you. Penguin, London
Pietrobruno S (2021) Curating viking objects through customized scrolling: how search engines personalize historical narratives. Convergence. https://doi.org/10.1177/1354856520986268
Psaropoulos J (2022) Timeline: week two of Russia’s invasion of Ukraine. Al Jazeera, Doha. https://www.aljazeera.com/news/2022/3/9/timeline-week-two-of-russia-invasion-of-ukraine
Reviglio U, Agosti C (2020) Thinking outside the black-box: the case for “algorithmic sovereignty” in social media. Soc Media Soc 6:2056305120915613. https://doi.org/10.1177/2056305120915613
Rhodes SC (2022) Filter bubbles, echo chambers, and fake news: how social media conditions individuals to be less critical of political misinformation. Polit Commun 39:1–22. https://doi.org/10.1080/10584609.2021.1910887
Riffe D, Lacy S, Watson BR, Fico F (2019) Analyzing media messages: using quantitative content analysis in research, 4th edn. Routledge, New York
Röchert D, Weitzel M, Ross B (2020) The homogeneity of right-wing populist and radical content in YouTube recommendations. In: International conference on social media and society, pp 245–254
Rodriguez A (2018) YouTube’s recommendations drive 70% of what we watch. Quartz. https://qz.com/1178125/youtubes-recommendations-drive-70-of-what-we-watch
Schroeder R (2018) The internet in everyday life II: seeking information. Social theory after the internet. UCL Press, London, pp 101–125
Schroeder A, Pennington-Gray L, Kim H, Liu-Lastres B (2018) Using the media’s tweets to broaden previous conceptualizations of political travel risks. Tour Manag Perspect 26:107–117. https://doi.org/10.1016/j.tmp.2018.03.001
Shahabi C, Chen Y-S (2003) Web information personalization: challenges and approaches. In: International workshop on databases in networked information systems. Springer, pp 5–15
Sharifpour M, Walters G, Ritchie BW, Winter C (2014) Investigating the role of prior knowledge in tourist decision making: a structural equation model of risk perceptions and information search. J Travel Res 53:307–322. https://doi.org/10.1177/0047287513500390
Smets A, Montero E, Ballon P (2019) Does the bubble go beyond. In: Proceedings of the 1st workshop on the impact of recommender systems with ACM RecSys
Sönmez SF, Apostolopoulos Y, Tarlow P (1999) Tourism in crisis: managing the effects of terrorism. J Travel Res 38:13–18. https://doi.org/10.1177/004728759903800104
Southern M (2021) Google launches free search insights tools for travel marketers. Search Engine J. https://www.searchenginejournal.com/google-launches-free-tools-for-travel-marketers/413512/. Accessed 27 Oct 2021
Spink A, Jansen BJ (2004) Web search: public searching of the Web. Springer Science & Business Media, Dordrecht
Spohr D (2017) Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Bus Inf Rev 34:150–160. https://doi.org/10.1177/0266382117722446
Statista (2021) Search engine market share worldwide. In: Statista. https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/. Accessed 5 Nov 2021
Statista (2022) TikTok MAU 2021. https://www.statista.com/statistics/1267892/tiktok-global-mau/. Accessed 10 Apr 2022
Statista (2023) Global search engine desktop market share 2023. In: Statista. https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/. Accessed 23 Sep 2023
Stepchenkova S, Morrison AM (2006) The destination image of Russia: from the online induced perspective. Tour Manag 27:943–956. https://doi.org/10.1016/j.tourman.2005.10.021
Stepchenkova S, Morrison AM (2008) Russia’s destination image among American pleasure travelers: revisiting Echtner and Ritchie. Tour Manag 29:548–560. https://doi.org/10.1016/j.tourman.2007.06.003
Stepchenkova S, Shichkova E, Kim M, Rykhtik MI (2018) Do strained bilateral relations affect tourists’ desire to visit a country that is a target of animosity? J Travel Tour Mark 35:553–566. https://doi.org/10.1080/10548408.2017.1374907
Strong EK (1925) The psychology of selling and advertising. McGraw-Hill Book Company, Incorporated, New York
Stylos N (2020) Technological evolution and tourist decision-making: a perspective article. Tour Rev 75:273–278. https://doi.org/10.1108/TR-05-2019-0167
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:1–19. https://doi.org/10.1155/2009/421425
Sundar SS (2008) The MAIN model: a heuristic approach to understanding technology effects on credibility. MacArthur Foundation Digital Media and Learning Initiative, Cambridge
Sunstein CR (2018) # Republic: divided democracy in the age of social media. Princeton University Press, Princeton
Tan EM, Goh DH-L (2015) Presenting social media information on mobile devices using multiple contexts. Aslib J Inf Manag 67:182–202. https://doi.org/10.1108/AJIM-09-2014-0124
Tan GW-H, Ooi K-B (2018) Gender and age: do they really moderate mobile tourism shopping behavior? Telemat Inform 35:1617–1642. https://doi.org/10.1016/j.tele.2018.04.009
Thurnell-Read T (2017) ‘What’s on your Bucket List?’: tourism, identity and imperative experiential discourse. Ann Tour Res 67:58–66. https://doi.org/10.1016/j.annals.2017.08.003
Ulloa R, Makhortykh M, Urman A (2022) Scaling up search engine audits: practical insights for algorithm auditing. J Inf Sci. https://doi.org/10.1016/j.annals.2017.08.003
US News & World Report (2022) Russia invades Ukraine: a timeline of the crisis. In: US News World Rep. //www.usnews.com/news/best-countries/slideshows/a-timeline-of-the-russia-ukraine-conflict. Accessed 31 Mar 2022
Uysal M, McGehee NG, Loker-Murphy L (1996) The Australian international pleasure travel market: motivations from a gendered perspective. J Tour Stud 7:45–57
Van Dijck J (2013) The culture of connectivity: a critical history of social media. Oxford University Press, Oxford
Van Meteren R, Van Someren M (2000) Using content-based filtering for recommendation. In: Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, pp 47–56
Wagner A (2017) Council post: are you maximizing the use of video in your content marketing strategy? Forbes
Wall Street Journal WSJ (2021) Inside TikTok’s algorithm: a WSJ video investigation. In: WSJ. https://www.wsj.com/articles/tiktok-algorithm-video-investigation-11626877477. Accessed 10 Apr 2022
Wang L, Lee K, Lu Q (2016) Improving advertisement recommendation by enriching user browser cookie attributes. In: Proceedings of the 25th ACM international on conference on information and knowledge management, pp 2401–2404
Wengel Y, Ma L, Ma Y et al (2022) The TikTok effect on destination development: famous overnight, now what? J Outdoor Recreat Tour 37:100458. https://doi.org/10.1016/j.jort.2021.100458
Wilson J, Moore K (2018) Performance on the frontline of tourist decision making. J Travel Res 57:370–383. https://doi.org/10.1177/0047287517696982
Wolfowicz M, Weisburd D, Hasisi B (2021) Examining the interactive effects of the filter bubble and the echo chamber on radicalization. J Exp Criminol 19:119–141. https://doi.org/10.1007/s11292-021-09471-0
Xiang Z, Gretzel U (2010) Role of social media in online travel information search. Tour Manag 31:179–188. https://doi.org/10.1016/j.tourman.2009.02.016
Xiang Z, Law R (2013) Online competitive information space for hotels: an information search perspective. J Hosp Mark Manag 22:530–546. https://doi.org/10.1080/19368623.2012.671563
Xiang Z, Pan B (2011) Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations. Tour Manag 32:88–97. https://doi.org/10.1016/j.tourman.2009.12.004
Xiang Z, Gretzel U, Fesenmaier DR (2009) Semantic Representation of Tourism on the Internet. J Travel Res 47:440–453. https://doi.org/10.1177/0047287508326650
Xiang Z, Magnini VP, Fesenmaier DR (2015) Information technology and consumer behavior in travel and tourism: insights from travel planning using the internet. J Retail Consum Serv 22:244–249. https://doi.org/10.1016/j.jretconser.2014.08.005
Yesilada M, Lewandowsky S (2022) Systematic review: YouTube recommendations and problematic content. Internet Policy Rev 11:1–22. https://doi.org/10.14763/2022.1.1652
Zollo F, Bessi A, Vicario MD et al (2017) Debunking in a world of tribes. PLoS ONE 12:e0181821. https://doi.org/10.1371/journal.pone.0181821
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gong, Y., Schroeder, A., Pan, B. et al. Does algorithmic filtering lead to filter bubbles in online tourist information searches?. Inf Technol Tourism 26, 183–217 (2024). https://doi.org/10.1007/s40558-023-00279-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40558-023-00279-4