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This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models.

First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared.

The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets.

In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

Delivery of safe products while meeting customer demand is a critical marketing requirement for logistics. To meet this requirement, this paper aims to develop a decision‐making model for distribution strategies in cold chain network with the real‐time flow and quality information of perishable foods.

This paper first presents a real‐time monitoring solution for cold chain distribution by integrating radio frequency identification (RFiD), sensor, and wireless communication technologies. With the enhanced visibility of product flow and quality information, a multi‐stage planning model is developed to determine optimal distribution plans so that the overall cost of the entire cold chain network is minimized.

The proposed distribution‐planning model can capture the dynamics of logistics due to frequent update of product quality information during distribution. Therefore, the distribution decision will be adjusted at sequential stages to optimally preserve the product value and meet demand. The proposed solution and model can ensure an effective cold chain logistics and thus meet the marketing requirement.

The current planning model cannot quantitatively capture all benefits, such as the social impact, due to the implementation of RFiD and other technologies.

The proposed solution to achieve complete visibility of the cold chain is innovative and addresses the urgent requirements for cold chain logistics from marketing perspective. For the first time, the economic benefits of real‐time information on product quality can be quantitatively evaluated by the multi‐stage planning model and this has been verified by a numerical case study.