This paper is in the following e-collection/theme issue:

Information Retrieval (287) Quality/Credibility of eHealth Information and Trust Issues (531) Prevention and Health Promotion (987) Information Seeking, Information Needs (465) Crowdsourcing and Mechanical Turks (98) Vaccination and Immunization in the Digital Age (374) Validity, Reliability, and Bias (93)

Published on in Vol 18, No 6 (2016): Jun

Manipulating Google’s Knowledge Graph Box to Counter Biased Information Processing During an Online Search on Vaccination: Application of a Technological Debiasing Strategy

Manipulating Google’s Knowledge Graph Box to Counter Biased Information Processing During an Online Search on Vaccination: Application of a Technological Debiasing Strategy

Manipulating Google’s Knowledge Graph Box to Counter Biased Information Processing During an Online Search on Vaccination: Application of a Technological Debiasing Strategy

Authors of this article:

Ramona Ludolph1 Author Orcid Image ;   Ahmed Allam2 Author Orcid Image ;   Peter J Schulz1 Author Orcid Image
Article Authors Cited by (38) Tweetations (22) Metrics

Journals

  1. Behimehr S, Jamali H. Relations between Cognitive Biases and Some Concepts of Information Behavior. Data and Information Management 2020;4(2):109 View
  2. Ludolph R, Schulz P. Debiasing Health-Related Judgments and Decision Making: A Systematic Review. Medical Decision Making 2018;38(1):3 View
  3. MacFarlane D, Hurlstone M, Ecker U. Protecting consumers from fraudulent health claims: A taxonomy of psychological drivers, interventions, barriers, and treatments. Social Science & Medicine 2020;259:112790 View
  4. Hussein E, Juneja P, Mitra T. Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube. Proceedings of the ACM on Human-Computer Interaction 2020;4(CSCW1):1 View
  5. Schulz P, Rothenfluh F. Influence of Health Literacy on Effects of Patient Rating Websites: Survey Study Using a Hypothetical Situation and Fictitious Doctors. Journal of Medical Internet Research 2020;22(4):e14134 View
  6. Kinkead L, Allam A, Krauthammer M. AutoDiscern: rating the quality of online health information with hierarchical encoder attention-based neural networks. BMC Medical Informatics and Decision Making 2020;20(1) View
  7. Gao R, Shah C. Toward creating a fairer ranking in search engine results. Information Processing & Management 2020;57(1):102138 View
  8. Epstein R, Robertson R, Lazer D, Wilson C. Suppressing the Search Engine Manipulation Effect (SEME). Proceedings of the ACM on Human-Computer Interaction 2017;1(CSCW):1 View
  9. Fadda M, Galimberti E, Fiordelli M, Schulz P. Evaluation of a Mobile Phone–Based Intervention to Increase Parents’ Knowledge About the Measles-Mumps-Rubella Vaccination and Their Psychological Empowerment: Mixed-Method Approach. JMIR mHealth and uHealth 2018;6(3):e59 View
  10. O’Gara P, Parakh K, Rumsfeld J. The ACC and Google. Journal of the American College of Cardiology 2016;68(8):868 View
  11. Allam A, Sak G, Diviani N, Schulz P. Do quality markers for health websites affect the perception of vaccination webpages?. Computers in Human Behavior 2017;67:273 View
  12. Chen T. A systematic integrative review of cognitive biases in consumer health information seeking: emerging perspective of behavioral information research. Journal of Documentation 2021;77(3):798 View
  13. Whitehead H, French C, Caldwell D, Letley L, Mounier-Jack S. A systematic review of communication interventions for countering vaccine misinformation. Vaccine 2023;41(5):1018 View
  14. Kobayashi T, Taka F, Suzuki T, Richey S. Can “Googling” correct misbelief? Cognitive and affective consequences of online search. PLOS ONE 2021;16(9):e0256575 View
  15. Fu C, Lyu X, Mi M. Collective Value Promotes the Willingness to Share Provaccination Messages on Social Media in China: Randomized Controlled Trial. JMIR Formative Research 2022;6(10):e35744 View
  16. Epstein R, Lee V, Mohr R, Zankich V, Saikia L. The Answer Bot Effect (ABE): A powerful new form of influence made possible by intelligent personal assistants and search engines. PLOS ONE 2022;17(6):e0268081 View
  17. Abroms L, Yom-Tov E. The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data. JMIR Infodemiology 2022;2(2):e37286 View
  18. Reyero Lobo P, Daga E, Alani H, Fernandez M, Gromann D. Semantic Web technologies and bias in artificial intelligence: A systematic literature review. Semantic Web 2023;14(4):745 View
  19. Malik A, Ahmed N, Shafiq M, Elharake J, James E, Nyhan K, Paintsil E, Melchinger H, Team Y, Malik F, Omer S. Behavioral interventions for vaccination uptake: A systematic review and meta-analysis. Health Policy 2023;137:104894 View
  20. Epstein R, Tyagi C, Wang H, Vellucci P. What would happen if twitter sent consequential messages to only a strategically important subset of users? A quantification of the Targeted Messaging Effect (TME). PLOS ONE 2023;18(7):e0284495 View
  21. Epstein R, Li J. Can Biased Search Results Change People’s Opinions About Anything at All? A Close Replication of the Search Engine Manipulation Effect (SEME). SSRN Electronic Journal 2023 View
  22. Qi J, Han Z, Zhong H. Behavioural economics theories in information-seeking behaviour research: A systematic review. Journal of Librarianship and Information Science 2023 View
  23. Epstein R, Lothringer M, Zankich V. How a Daily Regimen of Operant Conditioning Might Explain the Power of the Search Engine Manipulation Effect (SEME). Behavior and Social Issues 2024;33(1):82 View
  24. Epstein R, Li J, Saikia L. Can biased search results change people’s opinions about anything at all? a close replication of the Search Engine Manipulation Effect (SEME). PLOS ONE 2024;19(3):e0300727 View
  25. Paradise Vit A, Magid A. Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions. Journal of Medical Internet Research 2024;26:e55151 View
  26. Knight S, Bowdler I, Ford H, Zhou J. A visual scoping review of how knowledge graphs and search engine results page designs represent uncertainty and disagreement. Information and Learning Sciences 2024;125(11/12):1030 View

Books/Policy Documents

  1. Schulz P, Hartung U. Gesundheitskompetenz. View
  2. Draws T, Roy N, Inel O, Rieger A, Hada R, Yalcin M, Timmermans B, Tintarev N. Advances in Information Retrieval. View
  3. Schulz P, Hartung U. Gesundheitskompetenz. View
  4. Rieger A, Draws T, Mattis N, Maxwell D, Elsweiler D, Gadiraju U, McKay D, Bozzon A, Pera M. Advances in Information Retrieval. View