skip to main content
research-article
Open Access

How Do Home Computer Users Browse the Web?

Authors Info & Claims
Published:28 September 2021Publication History
Skip Abstract Section

Abstract

With the ubiquity of web tracking, information on how people navigate the internet is abundantly collected yet, due to its proprietary nature, rarely distributed. As a result, our understanding of user browsing primarily derives from small-scale studies conducted more than a decade ago. To provide an broader updated perspective, we analyze data from 257 participants who consented to have their home computer and browsing behavior monitored through the Security Behavior Observatory. Compared to previous work, we find a substantial increase in tabbed browsing and demonstrate the need to include tab information for accurate web measurements. Our results confirm that user browsing is highly centralized, with 50% of internet use spent on 1% of visited websites. However, we also find that users spend a disproportionate amount of time on low-visited websites, areas with a greater likelihood of containing risky content. We then identify the primary gateways to these sites and discuss implications for future research.

References

  1. Xueli An, Fahim Kawsar, and Utku Günay Acer. 2017. Profiling and predicting user activity on a home network. In Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous’17). ACM, New York, NY, 494–503. https://doi.org/10.1145/3144457.3145502 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Fabrício Benevenuto, Tiago Rodrigues, Meeyoung Cha, and Virgílio Almeida. 2009. Characterizing user behavior in online social networks. In Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement (IMC’09). ACM, New York, NY, 49–62. https://doi.org/10.1145/1644893.1644900 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Stephen P. Borgatti and Martin G. Everett. 2000. Models of core/periphery structures. Social Networks 21, 4 (2000), 375–395. https://doi.org/10.1016/S0378-8733(99)00019-2Google ScholarGoogle ScholarCross RefCross Ref
  4. Davide Canali, Leyla Bilge, and Davide Balzarotti. 2014. On the effectiveness of risk prediction based on users browsing behavior. In Proceedings of the 9th ACM Symposium on Information, Computer, and Communications Security (ASIA CCS’14). ACM, New York, NY, 171–182. https://doi.org/10.1145/2590296.2590347 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Carlos Castillo, Debora Donato, Aristides Gionis, Vanessa Murdock, and Fabrizio Silvestri. 2007. Know your neighbors: Web spam detection using the web topology. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). ACM, New York, NY, 423–430. https://doi.org/10.1145/1277741.1277814 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Lara D. Catledge and James E. Pitkow. 1995. Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN Systems 27, 6 (1995), 1065–1073. https://doi.org/10.1016/0169-7552(95)00043-7 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Xinran Chen, Sei-Ching Joanna Sin, Yin-Leng Theng, and Chei Sian Lee. 2015. Why do social media users share misinformation? In Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’15). ACM, New York, NY, 111–114. https://doi.org/10.1145/2756406.2756941 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Zhicong Cheng, Bin Gao, and Tie-Yan Liu. 2010. Actively predicting diverse search intent from user browsing behaviors. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, New York, NY, 221–230. https://doi.org/10.1145/1772690.1772714 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Flavio Chierichetti, Ravi Kumar, Prabhakar Raghavan, and Tamas Sarlos. 2012. Are web users really Markovian? In Proceedings of the 21st International Conference on World Wide Web (WWW’12). ACM, New York, NY, 609–618. https://doi.org/10.1145/2187836.2187919 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Clement. 2019. Projected retail e-commerce GMV share of Amazon in the United States from 2016 to 2021. Statista. Retrieved August 17, 2021 from https://www.statista.com/statistics/788109/amazon-retail-market-share-usa/.Google ScholarGoogle Scholar
  11. Yanqing Cui and Virpi Roto. 2008. How people use the web on mobile devices. In Proceedings of the 17th International Conference on World Wide Web (WWW’08). ACM, New York, NY, 905–914. https://doi.org/10.1145/1367497.1367619 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, and Walter Quattrociocchi. 2016. The spreading of misinformation online. Proceedings of the National Academy of Sciences 113, 3 (2016), 554–559. https://doi.org/10.1073/pnas.1517441113Google ScholarGoogle ScholarCross RefCross Ref
  13. DMOZ. 2020. The Directory of the Web. Retrieved August 17, 2021 from https://dmoz-odp.org/.Google ScholarGoogle Scholar
  14. Patrick Dubroy and Ravin Balakrishnan. 2010. A study of tabbed browsing among Mozilla Firefox users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10). ACM, New York, NY, 673–682. https://doi.org/10.1145/1753326.1753426 Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Serge Egelman and Eyal Peer. 2015. The myth of the average user: Improving privacy and security systems through individualization. In Proceedings of the 2015 New Security Paradigms Workshop (NSPW’15). ACM, New York, NY, 16–28. https://doi.org/10.1145/2841113.2841115 Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Miriam Fernandez and Harith Alani. 2018. Online misinformation: Challenges and future directions. In Companion Proceedings of the the Web Conference 2018 (WWW’18). 595–602. https://doi.org/10.1145/3184558.3188730 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Alain Forget, Saranga Komanduri, Alessandro Acquisti, Nicolas Christin, Lorrie Cranor, and Rahul Telang. 2014. Security Behavior Observatory: Infrastructure for Long-Term Monitoring of Client Machines. Technical Report CMU-CyLab-14-009. Carnegie Mellon University.Google ScholarGoogle Scholar
  18. Hana Habib, Jessica Colnago, Vidya Gopalakrishnan, Sarah Pearman, Jeremy Thomas, Alessandro Acquisti, Nicolas Christin, and Lorrie Faith Cranor. 2018. Away from prying eyes: Analyzing usage and understanding of private browsing. In Proceedings of the 14th USENIX Conference on Usable Privacy and Security (SOUPS’18). 159–175. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Kirstie Hawkey and Kori Inkpen. 2005. Web browsing today: The impact of changing contexts on user activity. In CHI’05 Extended Abstracts on Human Factors in Computing Systems (CHI EA’05). ACM, New York, NY, 1443–1446. https://doi.org/10.1145/1056808.1056937 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Julia Hoxha. 2012. Semantic formalization of cross-site user browsing behavior. Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence (WI’12). https://doi.org/10.1109/WI-IAT.2012.232 Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Jian Hu, Hua-Jun Zeng, Hua Li, Cheng Niu, and Zheng Chen. 2007. Demographic prediction based on user’s browsing behavior. In Proceedings of the 16th International Conference on World Wide Web (WWW’07). ACM, New York, NY, 151–160. https://doi.org/10.1145/1242572.1242594 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Jeff Huang, Thomas Lin, and Ryen W. White. 2012. No search result left behind: Branching behavior with browser tabs. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM’12). ACM, New York, NY, 203–212. https://doi.org/10.1145/2124295.2124322 Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Jeff Huang and Ryen W. White. 2010. Parallel browsing behavior on the web. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT’10). ACM, New York, NY, 13–18. https://doi.org/10.1145/1810617.1810622 Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. J. James, L. Sandhya, and C. Thomas. 2013. Detection of phishing URLs using machine learning techniques. In Proceedings of the 2013 International Conference on Control Communication and Computing (ICCC’13). 304–309. https://doi.org/10.1109/ICCC.2013.6731669Google ScholarGoogle Scholar
  25. Robert Kraut, William Scherlis, Tridas Mukhopadhyay, Jane Manning, and Sara Kiesler. 1996. The HomeNet field trial of residential internet services. Communications of the ACM 39, 12 (Dec. 1996), 55–63. https://doi.org/10.1145/240483.240493 Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ravi Kumar and Andrew Tomkins. 2010. A characterization of online browsing behavior. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, New York, NY, 561–570. https://doi.org/10.1145/1772690.1772748 Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Litmus Labs. 2020. Email Client Market Share. Retrieved August 17, 2021 from https://emailclientmarketshare.com/.Google ScholarGoogle Scholar
  28. Choudur Lakshminarayan, Ram Kosuru, and Meichun Hsu. 2016. Modeling complex clickstream data by stochastic models: Theory and methods. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW’16 Companion). 879–884. https://doi.org/10.1145/2872518.2891070 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Janette Lehmann, Mounia Lalmas, Georges Dupret, and Ricardo Baeza-Yates. 2013. Online multitasking and user engagement. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM, New York, NY, 519–528. https://doi.org/10.1145/2505515.2505543 Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. N. Leontiadis, T. Moore, and N. Christin. 2011. Measuring and analyzing search-redirection attacks in the illicit online prescription drug trade. In Proceedings of the 20th USENIX Security Symposium (USENIX Security’11). 281–298. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Fanny Lalonde Lévesque, Sonia Chiasson, Anil Somayaji, and José M. Fernandez. 2018. Technological and human factors of malware attacks: A computer security clinical trial approach. ACM Transactions on Privacy and Security 21, 4 (July 2018), Article 18, 30 pages. https://doi.org/10.1145/3210311 Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Chao Liu, Ryen W. White, and Susan Dumais. 2010. Understanding web browsing behaviors through Weibull analysis of dwell time. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’10). ACM, New York, NY, 379–386. https://doi.org/10.1145/1835449.1835513 Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Long Lu, Roberto Perdisci, and Wenke Lee. 2011. SURF: Detecting and measuring search poisoning. In Proceedings of the 18th ACM Conference on Computer and Communications Security (CCS’11). ACM, New York, NY, 467–476. https://doi.org/10.1145/2046707.2046762 Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Bonnie Ma Kay and Carolyn Watters. 2008. Exploring multi-session web tasks. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’08). ACM, New York, NY, 1187–1196. https://doi.org/10.1145/1357054.1357243 Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. J. McGahagan, D. Bhansali, M. Gratian, and M. Cukier. 2019. A comprehensive evaluation of HTTP header features for detecting malicious websites. In Proceedings of the 2019 15th European Dependable Computing Conference (EDCC’19). 75–82. https://doi.org/10.1109/EDCC.2019.00025Google ScholarGoogle ScholarCross RefCross Ref
  36. B. McKenzie and A. Cockburn. 2001. An empirical analysis of web page revisitation. In Proceedings of the 34th Annual Hawaii International Conference on System Sciences. 1–9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Rishabh Mehrotra, Prasanta Bhattacharya, and Emine Yilmaz. 2016. Uncovering task based behavioral heterogeneities in online search behavior. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16). ACM, New York, NY, 1049–1052. https://doi.org/10.1145/2911451.2914755 Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. H. Mekky, R. Torres, Z. Zhang, S. Saha, and A. Nucci. 2014. Detecting malicious HTTP redirections using trees of user browsing activity. In Proceedings of the 2014 IEEE Conference on Computer Communications (INFOCOM’14). 1159–1167. https://doi.org/10.1109/INFOCOM.2014.6848047Google ScholarGoogle ScholarCross RefCross Ref
  39. Filippo Menczer. 2016. The spread of misinformation in social media. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW’16 Companion). 717. https://doi.org/10.1145/2872518.2890092 Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. A. Narayanan and Vitaly Shmatikov. 2006. How to break anonymity of the Netflix Prize dataset. arXiv:cs/0610105.Google ScholarGoogle Scholar
  41. Terry Nelms, Roberto Perdisci, Manos Antonakakis, and Mustaque Ahamad. 2015. WebWitness: Investigating, categorizing, and mitigating malware download paths. In Proceedings of the 24th USENIX Security Symposium (USENIX Security’15). 1025–1040. https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/nelms. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Nam P. Nguyen, Guanhua Yan, My T. Thai, and Stephan Eidenbenz. 2012. Containment of misinformation spread in online social networks. In Proceedings of the 4th Annual ACM Web Science Conference (WebSci’12). ACM, New York, NY, 213–222. https://doi.org/10.1145/2380718.2380746 Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Hartmut Obendorf, Harald Weinreich, Eelco Herder, and Matthias Mayer. 2007. Web page revisitation revisited: Implications of a long-term click-stream study of browser usage. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’07). ACM, New York, NY, 597–606. https://doi.org/10.1145/1240624.1240719 Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Open PageRank. 2020. Home Page. Retrieved August 17, 2021 from https://www.domcop.com/openpagerank/.Google ScholarGoogle Scholar
  45. Sarah Pearman, Jeremy Thomas, Pardis Emami Naeini, Hana Habib, Lujo Bauer, Nicolas Christin, Lorrie Faith Cranor, Serge Egelman, and Alain Forget. 2017. Let’s go in for a closer look: Observing passwords in their natural habitat. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS’17). ACM, New York, NY, 295–310. https://doi.org/10.1145/3133956.3133973 Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Mahmood Sharif, Jumpei Urakawa, Nicolas Christin, Ayumu Kubota, and Akira Yamada. 2018. Predicting impending exposure to malicious content from user behavior. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS’18). ACM, New York, NY, 1487–1501. https://doi.org/10.1145/3243734.3243779 Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. StatCounter. 2020. Social Media Stats United States of America. Retrieved August 17, 2021 from https://gs.statcounter.com/social-media-stats/all/united-states-of-america.Google ScholarGoogle Scholar
  48. Linda Tauscher and Saul Greenberg. 1997. Revisitation patterns in World Wide Web navigation. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI’97). ACM, New York, NY, 399–406. https://doi.org/10.1145/258549.258816 Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Paul Thomas. 2014. Using interaction data to explain difficulty navigating online. ACM Transactions on the Web 8 (Nov. 2014), 1–41. https://doi.org/10.1145/2656343 Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Chad Tossell, Philip Kortum, Ahmad Rahmati, Clayton Shepard, and Lin Zhong. 2012. Characterizing web use on smartphones. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12). ACM, New York, NY, 2769–2778. https://doi.org/10.1145/2207676.2208676 Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. United States Bureau of Labor Statistics. 2019. Employment by Detailed Occupation. Retrieved August 17, 2021 from https://www.bls.gov/emp/tables/emp-by-detailed-occupation.htm.Google ScholarGoogle Scholar
  52. United States Census Bureau. 2017. ACS Demographics and Housing Estimates. Retrieved August 17, 2021 from https://data.census.gov/cedsci/table?q=ACS%20Demographics%20housing&tid=ACSDP1Y2017.DP05.Google ScholarGoogle Scholar
  53. United States Census Bureau. 2019. Educational Attainment in the United States: 2018. Retrieved August 17, 2021 from https://www.census.gov/data/tables/2018/demo/education-attainment/cps-detailed-tables.html.Google ScholarGoogle Scholar
  54. Luca Vassio, Idilio Drago, Marco Mellia, Zied Ben Houidi, and Mohamed Lamine Lamali. 2018. You, the web, and your device: Longitudinal characterization of browsing habits. ACM Transactions on the Web 12, 4 (Sept. 2018), Article 24, 30 pages. https://doi.org/10.1145/3231466 Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. W3Counter. 2020. Web Browser Market Share. Retrieved August 17, 2021 from https://www.w3counter.com/globalstats.php.Google ScholarGoogle Scholar
  56. Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, and Ben Y. Zhao. 2013. You are how you click: Clickstream analysis for Sybil detection. In Proceedings of the 22nd USENIX Security Symposium (USENIX Security’13). 241–256. https://www.usenix.org/conference/usenixsecurity13/technical-sessions/presentation/wang. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Gang Wang, Xinyi Zhang, Shiliang Tang, Christo Wilson, Haitao Zheng, and Ben Y. Zhao. 2017. Clickstream user behavior models. ACM Transactions on the Web 11, 4 (July 2017), Article 21, 37 pages. https://doi.org/10.1145/3068332 Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Gang Wang, Xinyi Zhang, Shiliang Tang, Haitao Zheng, and Ben Y. Zhao. 2016. Unsupervised clickstream clustering for user behavior analysis. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI’16). ACM, New York, NY, 225–236. https://doi.org/10.1145/2858036.2858107 Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Yuxi Wang, Martin McKee, Aleksandra Torbica, and David Stuckler. 2019. Systematic literature review on the spread of health-related misinformation on social media. Social Science & Medicine 240 (2019), 112552. https://doi.org/10.1016/j.socscimed.2019.112552Google ScholarGoogle ScholarCross RefCross Ref
  60. Yi-Min Wang, Ming Ma, Yuan Niu, and Hao Chen. 2007. Spam double-funnel: Connecting web spammers with advertisers. In Proceedings of the 16th International Conference on World Wide Web (WWW’07). ACM, New York, NY, 291–300. https://doi.org/10.1145/1242572.1242612 Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Liang Wu, Fred Morstatter, Kathleen M. Carley, and Huan Liu. 2019. Misinformation in social media: Definition, manipulation, and detection. ACM SIGKDD Explorations Newsletter 21, 2 (Nov. 2019), 80–90. https://doi.org/10.1145/3373464.3373475 Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Yunjuan Xie and Vir V. Phoha. 2001. Web user clustering from access log using belief function. In Proceedings of the 1st International Conference on Knowledge Capture (K-CAP’01). ACM, New York, NY, 202–208. https://doi.org/10.1145/500737.500768 Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Haimo Zhang and Shengdong Zhao. 2011. Measuring web page revisitation in tabbed browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). ACM, New York, NY, 1831–1834. https://doi.org/10.1145/1978942.1979207 Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Proceedings of the 28th International Conference on Neural Information Processing Systems—Volume 1 (NIPS’15). 649–657. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. How Do Home Computer Users Browse the Web?

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on the Web
          ACM Transactions on the Web  Volume 16, Issue 1
          February 2022
          173 pages
          ISSN:1559-1131
          EISSN:1559-114X
          DOI:10.1145/3484933
          Issue’s Table of Contents

          Copyright © 2021 Copyright held by the owner/author(s).

          This work is licensed under a Creative Commons Attribution-NoDerivs International 4.0 License.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 28 September 2021
          • Revised: 1 June 2021
          • Accepted: 1 June 2021
          • Received: 1 February 2021
          Published in tweb Volume 16, Issue 1

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format