With the advancement of internet technology, customers increasingly rely on online reviews as a valuable source of information. The study aims to develop a marketing data analytics framework to manage online reviews, especially fake reviews, which have become a significant issue undermining the creditability of online review systems. As small and medium-sized enterprises often lack the capabilities to automatically derive customer insights from online reviews, this study proposes a cost-effective, extensible Review-Analytics-as-a-Service (RAaaS) framework that can be operated by non-data specialists to facilitate online review data analytics. We demonstrate the framework’s application by using two datasets with more than 400,000 online reviews from Yelp to simulate live platforms and demonstrate an analytic flow of review fraud detection and understanding. The findings reveal insights into the influence of fake reviews on product ranking and exposure rate. Moreover, it was found that there was a higher concentration of sadness and anger in fake reviews (vs. organic reviews). In addition, fake reviews tend to be shorter, more extreme (with the use of strong adverbs), and have different patterns of topic distribution. This study has important implications for different stakeholder groups including, but not limited to, SMEs, review platforms and customers.

Rumors are increasingly becoming a critical issue on the Web threatening democracy, economics, and society on a global scale. With the advance of social media networks, people are sharing content in an unprecedented scale. This makes social platforms such as microblogs an ideal place for spreading rumors. Although rumors may have a severe impact in the real world, there is not enough large-scale study regarding the characteristics of rumors. In this paper, by studying more than 1000 rumors with over 4 million tweets from about 3 million users, we aim to provide several insights in order to understand the distribution, correlation, and propagation of rumors, especially user behaviors, spatial and temporal characteristics. All the rumor data are publicly available.