Navigating the Digital Landscape: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Subjects: Social Issues
Contributor:
Jiadong Yu
,
D. A. Bekerian
,
Chelsee Osback

The advent of the internet has fundamentally transformed how people access and interact with information. The digital landscape offers easy access to educational materials and healthcare and can support informed decision making. It also presents significant challenges in effectively using this information. This entry discusses the multifaceted barriers that can impede individuals from fully benefiting from the internet’s information resources. Key challenges include discerning between accuracy and misinformation, the uneven distribution of digital literacy, disparities in access to technology, and motivational barriers in seeking reliable information. Additionally, the growing involvement of artificial intelligence (AI) in content curation, regulation, and moderation introduces opportunities and ethical dilemmas. These factors complicate efforts to support the accuracy and trustworthiness of the information shared through digital platforms.

  • digital literacy
  • misinformation
  • disinformation
  • digital divide
  • information seeking
  • content moderation
In the past few decades, the internet has evolved into an indispensable tool for individuals seeking information across various domains such as healthcare, education, and daily decision making [1][2][3][4]. The convenience, immediacy, and accessibility offered by digital platforms have created an unprecedented information ecosystem, where users can quickly find answers to almost any question [5][6][7]. However, the quality, accuracy, and trustworthiness of the information found online are not always assured [8][9][10][11].
This paradox of abundance and uncertainty has raised important questions about the challenges users face when attempting to use the internet as a reliable resource. These challenges include the widespread proliferation of misinformation, disparities in digital literacy, inequitable access to technology, and motivational barriers to seeking reliable information [12][13][14][15]. Ethical concerns surrounding content regulation and monitoring further complicate the issue of ensuring the reliability of online information, especially with the advent of artificial intelligence [16][17][18].
The involvement of artificial intelligence (AI) has played an increasingly prominent role in shaping how information is disseminated, moderated, and consumed online in recent years [19][20]. AI-driven algorithms now curate content, flag misinformation, and moderate user interactions, raising both opportunities and challenges in the quest for reliable online information [21][22][23][24]. AI systems can improve the efficiency of content moderation and help users navigate the vast information landscape; however, concerns remain about algorithmic biases, lack of transparency, and the ethical implications of AI-driven decision making [25][26].
This entry paper provides an overview of these challenges by summarizing the existing literature on the barriers to effective information use on the internet. It examines key obstacles, such as misinformation, digital literacy disparities, motivational issues, access to technology, and the increasing influence of AI in content regulation and moderation. This review of current research and public discourse highlights the complex factors that shape users’ ability to navigate and critically engage with online information.

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia4040109

References

  1. Eysenbach, G. What is e-health? J. Med. Internet Res. 2001, 3, e833.
  2. Flanagin, A.J.; Metzger, M.J. The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media Soc. 2007, 9, 319–342.
  3. McMullan, M. Patients using the Internet to obtain health information: How this affects the patient–health professional relationship. Patient Educ. Couns. 2006, 63, 24–28.
  4. Means, B.; Toyama, Y.; Murphy, R.; Bakia, M.; Jones, K. Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies; Centre for Learning Technology: Midhurst, UK, 2009; Available online: https://repository.alt.ac.uk/629/ (accessed on 19 September 2024).
  5. Floridi, L. The Fourth Revolution: How the Infosphere Is Reshaping Human Reality; OUP Oxford: Oxford, UK, 2014; 265p.
  6. Proferes, N.; Jones, N.; Gilbert, S.; Fiesler, C.; Zimmer, M. Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics. Soc. Media Soc. 2021, 7, 20563051211019004.
  7. Spink, A.; Cole, C. Human information behavior: Integrating diverse approaches and information use. J. Am. Soc. Inf. Sci. Technol. 2006, 57, 25–35.
  8. Cheng, J.; Bernstein, M.; Danescu-Niculescu-Mizil, C.; Leskovec, J. Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions. In Proceedings of the CSCW ’17: 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, Portland, OR, USA, 25 February–1 March 2017; Association for Computing Machinery: New York, NY, USA, 2017; pp. 1217–1230. Available online: https://dl.acm.org/doi/10.1145/2998181.2998213 (accessed on 19 September 2024).
  9. Flanagin, A.J.; Metzger, M.J. Perceptions of Internet Information Credibility. J. Mass. Commun. Q. 2000, 77, 515–540.
  10. Metzger, M.J.; Flanagin, A.J. Credibility and trust of information in online environments: The use of cognitive heuristics. J. Pragmat. 2013, 59, 210–220.
  11. Yu, J.; Gorman, B.S.; Springer, C.M. Source of Information on Intentions to Adopt Protective Behaviours During COVID-19 in China. Inq. J. Health Care Organ. Provis. Financ. 2022, 59, 00469580221090411.
  12. Dutta-Bergman, M.J. Primary Sources of Health Information: Comparisons in the Domain of Health Attitudes, Health Cognitions, and Health Behaviors. Health Commun. 2004, 16, 273–288.
  13. Eshet, Y. Digital Literacy: A Conceptual Framework for Survival Skills in the Digital era. J. Educ. Multimed. Hypermedia 2004, 13, 93–106.
  14. Hargittai, E. Digital Na(t)ives? Variation in Internet Skills and Uses among Members of the “Net Generation”. Sociol. Inq. 2010, 80, 92–113.
  15. Vosoughi, S.; Roy, D.; Aral, S. The spread of true and false news online. Science 2018, 359, 1146–1151.
  16. Gillespie, T. Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media; Yale University Press: New Haven, CT, USA, 2018; 297p.
  17. Helberger, N.; Pierson, J.; Poell, T. Governing online platforms: From contested to cooperative responsibility. Inf. Soc. 2018, 34, 1–14.
  18. Schulz, W.; Held, T. Regulated Self-Regulation as a Form of Modern Government: An Analysis of Case Studies from Media and Telecommunications Law; Indiana University Press: Bloomington, IN, USA, 2004; 120p.
  19. Bakshy, E.; Messing, S.; Adamic, L.A. Exposure to ideologically diverse news and opinion on Facebook. Science 2015, 348, 1130–1132.
  20. Mittelstadt, B.D.; Allo, P.; Taddeo, M.; Wachter, S.; Floridi, L. The ethics of algorithms: Mapping the debate. Big Data Soc. 2016, 3, 2053951716679679.
  21. Floridi, L.; Taddeo, M. What is data ethics? Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 2016, 374, 20160360.
  22. Jhaver, S.; Ghoshal, S.; Bruckman, A.; Gilbert, E. Online Harassment and Content Moderation: The Case of Blocklists. ACM Trans. Comput.-Hum. Interact. 2018, 25, 1–33.
  23. Napoli, P.M. Automated Media: An Institutional Theory Perspective on Algorithmic Media Production and Consumption. Commun. Theory 2014, 24, 340–360.
  24. Zhou, X.; Zafarani, R.; Shu, K.; Liu, H. Fake News: Fundamental Theories, Detection Strategies and Challenges. In Proceedings of the WSDM ’19: Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11–15 February 2019; Association for Computing Machinery: New York, NY, USA, 2019; pp. 836–837.
  25. Ananny, M.; Crawford, K. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 2018, 20, 973–989.
  26. Fu, R.; Huang, Y.; Singh, P.V. Artificial Intelligence and Algorithmic Bias: Source, Detection, Mitigation, and Implications. In Pushing the Boundaries: Frontiers in Impactful OR/OM Research; INFORMS TutORials in Operations Research; INFORMS: Catonsville, MD, USA, 2020; pp. 39–63. Available online: https://pubsonline.informs.org/doi/abs/10.1287/educ.2020.0215 (accessed on 19 September 2024).
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback