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Calculating trust in domain analysis: Theoretical trust model

Author

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  • Al Qundus, Jamal
  • Paschke, Adrian
  • Kumar, Sameer
  • Gupta, Shivam

Abstract

In recent decades, more information has become increasingly available on the Web. Every user can actively participate in the generation and exchange of information. Investigating the quality of user-generated content (UGC) has therefore become a necessity and an ever-increasing challenge. In collaborative environments where users collect, share and build a knowledge base, trust is an important factor. If, for example, we as users trust UGC on the Web, this influences our interaction with this content. The aim of our research is to propose a model for the evaluation of trust in UGC. Based on the available research results, we define a model for measuring trust in collaborative environments. Our approach is based on three dimensions: stability, credibility and quality. These three concerns are combined to create a trusted translator. We use a real-world data set of the social annotation platform Genius to calculate the value of our trust in an annotation. Based on this case study, we show which insights can be gained by calculating the trust in such an environment. When information has specific qualities, our approach will enable the user to better determine which information offers the highest level of trust.

Suggested Citation

  • Al Qundus, Jamal & Paschke, Adrian & Kumar, Sameer & Gupta, Shivam, 2019. "Calculating trust in domain analysis: Theoretical trust model," International Journal of Information Management, Elsevier, vol. 48(C), pages 1-11.
    • Handle: RePEc:eee:ininma:v:48:y:2019:i:c:p:1-11
    DOI: 10.1016/j.ijinfomgt.2019.01.012
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    Citations

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    Cited by:

    1. Jamal Al Qundus & Kosai Dabbour & Shivam Gupta & Régis Meissonier & Adrian Paschke, 2022. "Wireless sensor network for AI-based flood disaster detection," Annals of Operations Research, Springer, vol. 319(1), pages 697-719, December.
    2. Jamal Al Qundus & Shivam Gupta & Hesham Abusaimeh & Silvio Peikert & Adrian Paschke, 2023. "Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 235-246, June.
    3. Minh-Tri Ha & Giang-Do Nguyen & Thi Huong-Thanh Nguyen & Bich-Duyen Nguyen, 2023. "The use of dietary supplements and vitamin consumption during and after the Covid pandemic in Vietnam: a perspective of user-generated content," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.

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