With the growing interconnectedness of the world and advances in transportation and communication, more and more people are travelling as independent tourists, putting together their own itineraries and activities from information researched from social media. However, many reviewers post reviews without validation, leading to the explosive growth of reviews and the proliferation of uninformative, biased or even false information. This makes it very challenging for travellers to find credible reviews. Previous work has shown that credibility assessment of sources and messages are fundamentally interlinked. Hence, there has been much work on measuring the credibility of reviewers. However, most current work investigates the factors impacting the perception of reviewer credibility without quantitative evaluation. This paper presents a method that quantifies the credibility of reviewers in TripAdvisor. An Impact Index is proposed to measure reviewer credibility by evaluating the expertise and trustworthiness based on the number of reviews posted by the reviewer and the number of helpful votes received by the reviews. Furthermore, the Impact Index is improved into the Exposure-Impact Index by considering in addition the number of destinations on which the reviewer posted reviews. Our experimental results show that both Impact Index and Exposure-Impact Index outperform the state-of-the-art method in measuring the credibility of reviewers to help travellers search for credible reviews.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||24th International Conference on Database and Expert Systems Applications, DEXA 2013|
|Period||26/08/13 → 29/08/13|
- credible review
- reviewer credibility
- Theoretical Computer Science
- Computer Science(all)