The aim of this research is to detect and classify websites based on their content if it encourages spreading hate speech toward Islam and Muslims, or Islamophobia using sentiment analysis and web text mining techniques. In this research, a large dataset corpus has been collected, to identify and classify anti-Islamic online contents. Our target is to automatically detect the content of those websites that are hostile to Islam and transmitting extremist ideas against it. The main purpose is to reduce the spread of those webpages that give the wrong idea about Islam. The proper dataset is collected from different sources, and the two datasets for the Arabic language (balanced and unbalanced) have been produced. The framework of the proposed approach has been described. The approach used in this framework is based on supervised Machine Learning (ML) approach using Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB) models as classifiers, and Term Frequency-Inverse Document Frequency (TF-IDF) as feature extraction. Different experiments including word level and trigram level on the two datasets have been conducted, and compared the obtained results. The experimental results shows that the supervised ML approach using word level is the finest approach for both datasets that produce high accuracy with 97% applied on the balanced Arabic dataset using SVM algorithm with TF-IDF as feature extraction. Finally, an interactive webapplication prototype has been developed and built in order to detect and classify toxic language such as anti-Islamic online textcontents.
Background In most recent studies in Saudi Arabia, the percentage of uncontrolled asthma was higher than the worldwide population, especially among the younger age group. Despite the effectiveness of medications, poor adherence in adolescents is a major barrier that prevents them from achieving good asthma control. The objective of this study was to guide adolescents towards better asthma control. We explored barriers affecting medication adherence among adolescents with asthma in the Medina region.
Methods: A cross-sectional study was carried out in the Medina region. Data were collected from online surveys, intermediate and secondary schools, and primary healthcare centers from 221 adolescents between 12 and 18 years old who answered online self-reported questionnaires that included the modified Medication Adherence Rating Scale (MARS) and the Asthma Control Test (ACT).
Result: Our results showed that 63.8% of the study population were non-adherent to asthma medications. Overall, 44.8% had impaired asthma control. Furthermore, poor adherence was significantly associated with uncontrolled asthma scores. Almost two-thirds of participants with uncontrolled asthma based on ACT scores were non-adherent (46 out of 59 patients with uncontrolled asthma). The most important factors contributing to poor adherence include 62% of adolescents forgetting to take their medication and 59% stopping their medication after feeling better. Moreover, there was no statistically significant association between the sociodemographic factors and medication adherence or asthma control score.
Conclusion: This study showed a high prevalence of non-adherence to asthma medications among adolescents in the Medina region, significantly affecting their asthma control. The most commonly reported reasons for non-adherence could be adjusted by increasing the adolescents' awareness during a consultation with their healthcare providers.
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