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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

While the password-based authentication used in social networks, e-mail, e-commerce, and online banking is vulnerable to hackings, biometric-based continuous authentication systems have been used successfully to handle the rise in unauthorized accesses. In this study, an empirical evaluation of online continuous authentication (CA) and anomaly detection (AD) based on mouse clickstream data analysis is presented. This research started by gathering a set of online mouse-dynamics information from 20 participants by using software developed for collecting mouse information, extracting approximately 87 features from the raw dataset. In contrast to previous work, the efficiency of CA and AD was studied using different machine learning (ML) and deep learning (DL) algorithms, namely, decision tree classifier (DT), k-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN). User identification was determined by using three scenarios: Scenario A, a single mouse movement action, Scenario B, a single point-and-click action, and Scenario C, a set of mouse movement and point-and-click actions. The results show that each classifier is capable of distinguishing between an authentic user and a fraudulent user with a comparatively high degree of accuracy.

Using social media platforms such as Twitter has drastically increased over the past decade. It has enhanced the traditional means of communication in many aspects of life. Artificial Intelligence and Machine Learning algorithms have become popular in assessing natural disasters. During natural catastrophic events and emergencies, people progressively use microblogging platforms such as Twitter, creating a high volume of posts spread across these platforms. The information disseminated on Twitter contains critical indicators about evacuations or emergency actions that could incite panic, affecting the response and evacuation behavior of the general population. In order to avoid panic, these indicators need to be detected, the credibility of their source needs to be validated, and the emergency agencies need to mitigate the risk of panic by quickly taking the right actions for these panic triggering situations. This paper presents a Panic Trigger Identification Method (PTIM) which applies machine learning techniques on disaster-related tweets to detect panic triggers, and classifies the tweets based on the triggers identified and the corresponding credibility level of the tweets to improve the emergency response, and to suggest mitigation actions for emergency management. Two types of text vectorizers, CountVectorizer and TfidfVectorizer, are used as features for the supervised machine learning classification models. A performance comparison is conducted among the classifiers. Results show that for the classification of the tweets with panic triggers, Random Forest and Decision Tree give the best predictions with high accuracy (95% on average) when using CountVectorizer features.

During natural disaster events, social media users generate a significant amount of data, some of which are valuable for relief efforts and emergency management. In this paper, we study the nature of social media content generated during two natural disasters. We provide a system for labeling Twitter data and classifying disaster-related tweets. The focus is on Twitter data generated before, during, and after Hurricane Florence and Hurricane Michael, both of which occurred in 2018. Then we apply various machine learning models on the labeled data and provide a performance comparison between the models using features generated from TfidfVectorizer and CountVectorizer Approaches.