Detecting Users Prone to Spread Fake News on Arabic Twitter

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users' recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task. © European Language Resources Association (ELRA).

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People.

Visually impaired people face many challenges that limit their ability to perform daily tasks and interact with the surrounding world. Navigating around places is one of the biggest challenges that face visually impaired people, especially those with complete loss of vision. As the Internet of Things (IoT) concept starts to play a major role in smart cities applications, visually impaired people can be one of the benefitted clients. In this paper, we propose a smart IoT-based mobile sensors unit that can be attached to an off-the-shelf cane, hereafter a smart cane, to facilitate independent movement for visually impaired people. The proposed mobile sensors unit consists of a six-axis accelerometer/gyro, ultrasonic sensors, GPS sensor, cameras, a digital motion processor and a single credit-card-sized single-board microcomputer. The unit is used to collect information about the cane user and the surrounding obstacles while on the move. An embedded machine learning algorithm is developed and stored in the microcomputer memory to identify the detected obstacles and alarm the user about their nature. In addition, in case of emergencies such as a cane fall, the unit alerts the cane user and their guardian. Moreover, a mobile application is developed to be used by the guardian to track the cane user via Google Maps using a mobile handset to ensure safety. To validate the system, a prototype was developed and tested.

Diagnosing COVID-19 in Chest X-ray Images based on Deep Learning: Transfer Learning versus Deep Features Extraction

Chest X-ray (CXR) images provide an effective modality for detecting COVID-19 infections. Nevertheless, the interpretation of CXR images is challenging and operator-dependent task. Several studies proposed the use of pretrained convolutional neural network (CNN) models to classify CXR images with the goal of detecting COVID-19 infections. In fact, the classification of CXR images using the pretrained CNN models is essentially performed using two approaches, namely the transfer learning approach and deep features extraction approach. This study aims to compare the performance of these two approaches to classify CXR images as COVID-19, pneumonia, and normal. Three pretrained CNN models, namely the AlexNet, VGG19, and ResNet50 CNN models, have been utilized. Furthermore, a balanced dataset of CXR images is used to perform the analysis, where this dataset includes 1,228 COVID-19 CXR images, 1,228 pneumonia CXR images, and 1,228 normal CXR images. For the three pretraiend CNN models, the deep features extraction approach achieved better classification results compared with the transfer learning approach. Moreover, the results show that the ResNet50 CNN model obtained the highest classification performance based on the transfer learning approach and the deep features extraction approach. The highest macro-averaged sensitivity, specificity, and F1 score values, which have been achieved using the deep features extraction approach and the ResNet50 CNN model, are equal to 93.7%, 96.9%, and 93.7%, respectively. © 2022 IEEE.

Did I See It Before? Detecting Previously-Checked Claims over Twitter

With the proliferation of fake news in the last few years, especially during the COVID-19 period, combating the spread of misinformation has become an urgent need. Although automated fact-checking systems were proposed recently, they leave much to be desired in terms of accuracy and explainability. Therefore, involving humans during verification could make the process much easier and more reliable. In this work, we propose an automated approach to detect claims that have been already manually-verified by professional fact-checkers. Our proposed approach uses recent powerful BERT variants as point-wise rerankers. Additionally, we study the impact of using different fields of the verified claim during training and inference phases. Experimental results show that our proposed pipeline outperforms the state-of-the-art approaches on two English and one Arabic datasets.

Impacts of COVID-19 on Electric Vehicle Charging Behavior: Data Analytics, Visualization, and Clustering

COVID-19 pandemic has infected millions and led to a catastrophic loss of lives globally. It has also significantly disrupted the movement of people, businesses, and industries. Additionally, electric vehicle (EV) users have faced challenges in charging their vehicles in public charging locations where there is a risk of COVID-19 exposure. However, a case study of EV charging behavior and its impacts during the SARS-CoV-2 is not addressed in the existing literature. This paper investigates the impacts of COVID-19 on EV charging behavior by analyzing the charging activity during the pandemic using a dataset from a public charging facility in the USA. Data visualization of charging behavior alongside significant timelines of the pandemic was utilized for analysis. Moreover, a cluster analysis using k-means, hierarchical clustering, and Gaussian mixture models was performed to identify common groups of charging behavior based on the vehicle arrival and departure times. Although the number of vehicles using the charging station was reduced significantly due to lockdown restrictions, the charging activity started to pick up again since May 2021 due to an increase in vaccination and easing of public restrictions. However, the charging activity currently still remains around half of the activity pre-pandemic. A noticeable decline in charging session length and an increase in energy consumption can be observed as well. Clustering algorithms identified three groups of charging behavior during the pandemic and their analysis and performance comparison using internal validation measures were also presented.

Engineering students' readiness to transition to emergency online learning in response to COVID-19: Case of Qatar

This study examined engineering students' initial readiness to transition to emergency online learning in response to COVID-19 in Qatar. A theoretical framework is proposed for understanding the factors influencing students' readiness for change. Sequential explanatory mixed-method research was conducted, with 140 participants completing an online survey, of which 68 also contributed written reflections and 8 participated in semi-structured interviews. Exploratory factor analysis displayed a four-factor structure, including initial preparedness and motivation for online learning, self-efficacy beliefs about online learning, self-directed learning online, and support. The qualitative outcomes supported the four factors and provided further insight into their varied and nuanced manifestation. In accounting for the perceived impact of the factors on readiness, significant differences were identified regarding pedagogical mode, with students enrolled in PBL courses reporting higher readiness than those from non-PBL courses. The practical implications for preparing students for future emergency online learning are discussed. © 2020 by the authors.