Quality features are one of the most important issues that should be considered during the development of Mobile Social Networks (MSN) applications in learning environments. These features are considered one of the most important factors for the successful development of any system. In order to increase the chance of the successful development of MSN applications, there need to identify well-defined set of quality features for MSN applications adoption in the learning environment. Therefore, this study proposes a model that captures most important quality features of MSN applications based on extending the DeLone and McLean information systems success model empirically. The proposed model of this study is examined empirically through a survey of 235 students. The findings also indicate that there are positive relationships between students satisfaction, behavioral intention to use with the overall quality features. The proposed model presents for researchers and designers the most important quality features as guidelines to design and develop MSN applications with a positive effect on students’ behavioral intention to use of MSN applications, and this situation can lead to enhance the learning outcomes using this new application.

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Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words.

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