The increasing prevalence of fake online reviews jeopardizes firms' profits, consumers' well-being, and the trustworthiness of e-commerce ecosystems. We face the significant challenge of accurately detecting fake reviews. In this paper, we undertake a comprehensive investigation of traditional and state-of-the-art machine learning models in classification, based on textual features, to detect fake online reviews. We attempt to examine existing and noteworthy models for fake online review detection, in terms of the effectiveness of textual features, the efficiency of sampling methods, and their performance of detection. Adopting a quantitative and data-driven approach, we scrutinize both tree-based and transformer-based detection models. Our comparative studies evidence that transformer-based models (specifically BERT and GPT-3) outperform tree-based models (i.e., Random Forest and XGBoost), in terms of accuracy, precision, and recall metrics. We use real data from online reviews on Yelp.com for implementation. The results demonstrate that our proposed approach can identify fraudulent reviews effectively and efficiently. Synthesizing ChatGPT-3, tree-based, and transformer-based models for fake online review detection is rather new but promising, this paper highlights their potential for better detection of fake online reviews.

This paper proposes an adaptive subgroup rescue mechanism to better balance efficiency and information loss in large-scale group decision-making. We calculate the consensus for different clusters through linguistic preferences and trust relations, find the group with the lowest consensus level, and advise the group to exit from the process. A rescue process is triggered once the group has low cohesion or can propose a new idea. A trust-based feedback mechanism is designed to ensure enough decision makers in consensus reaching. Finally, we conduct an empirical study to verify the feasibility of our mechanism.

The dramatic increase in the number of students enrolled in higher education programs outside their country of citizenship during the last half-century has created a huge demand for study abroad-related information circulation. Nonetheless, media nowadays attempts to break information barriers by gathering and processing data from multiple sources, mainly focusing on university academic competency. Although important, academic competency cannot represent international students’ overall quality of life when spending their time in unfamiliar foreign cities. This research is designed to provide solutions to the problem from another perspective Utilizing the Multiple Criteria Decision Making (MCDM) method to evaluate international study destinations through various dimensions comprehensively. The research integrates various aspects such as economic development, culture inclusiveness, and personal safety into account. It then adopts the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to obtain a detailed index for each destination in the rank. Our work-Global Ranking of Study Destination (GRSD) by cities, is accomplished to contribute to the international student community. With a ranking system that considers vital facets of life in certain cities, prospective international students can make assessments and decisions better for their future.

Multiple Criteria Decision Making (MCDM) aims at giving people a knowledge recommendation concerning a set of objects evaluated from multiple preference-ordered attributes. The Superiority and Inferiority Ranking (SIR) is a generation of the well-known outranking approach-PROMETHEE, which is an efficient approach for MCDM. As the traditional MCDM approach, however, it faces the obstacle in handling uncertainties of real world. We are concerned about the issue on how to extend the traditional MCDM approach for applications in uncertain environments. This paper proposes a new Intuitionistic Fuzzy SIR (IF-SIR for short) approach and focuses on its application to supplier selection which is the important activity in supply chain management. Toward practical applications, two factors are considered here: (1) multiple decision makers and (2) decision information in the form of linguistic terms. We firstly identify these terms via Intuitionistic Fuzzy Set (IFS) which is proven to be a powerful mathematical tool in modeling uncertain information. Then, we provide the IF-SIR approach for group aggregation and decision analysis. Hereinto, a rule-based method is developed for ranking and selection of suppliers. Finally, an illustrative example is used for illustration of the proposed approach.