Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

Sequential pattern mining in data streams environment is an interesting data mining problem. The problem of finding sequential patterns in static databases had been studied extensively in the past years, however mining sequential patterns in the data streams still an active field for researches. In this research a new greedy sequence pattern mining algorithm for the data streams is introduced, it will be used to find the strongly supported sequences. The proposed algorithm is built based on the sequence tree which is used to find the sequential patterns in static databases. The proposed algorithm divides the streams into patches or windows and each patch will update the sequence tree which built from the previous windows. An example is introduced to explain how this algorithm works. We also show the efficiency and the effectiveness of the proposed algorithm on a synthetic dataset and prove how it is suited for data streams environment. We showed experimentally that the proposed algorithm is more efficient than the PrefixSpan algorithm for patterns with any support less than 30% for CPU time and with any support less than 60% for memory usage.

The fast spread of web services in our businesses and day-to-day lives has made QoS an essential aspect for both the service providers and consumers. The main problem is how the consumer obtains a high comprehensive quality composite service when there are a large number of web services available; the choice of the optimal path depends on the QoS for every atomic service. Our contribution is studying the influence of the reputation factor in the process of selecting the optimal path in the absence of one of four factors (Availability, Reliability, Response Time, and Price) and the possibility of covering for this absence. We have used the reputation factor when calculating the QoS by using artificial bee colony algorithm for selecting the optimal web service composition; then we analyzed the impact of reputation on the process of selecting web service composition in terms of the QoS and accuracy of the solution. Also, we studied the impact of the reputation factor in the case of the absence of one of the four factors through three experiments and a set of comparisons. The result was that the reputation factor could cover factors such as availability, Response Time, and technical support. We used multiple linear regression and polynomial regression to show the prediction of the reputation factor using the four other factors. The result had higher confidence when we used multiple polynomial regression where the Residual Sum of Squares (RSS) was less than the multiple linear regression. In addition, we analyzed the association between reputation and the four other factors using ANOVA test; the result indicates that there is a significant association between reputation and (availability, response time, and price), but the association is not significant with the reliability.