• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.317-323
• /
• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.523-531
• /
• 2021

This study aims to examine the role of social media marketing on the performance of micro-, small and medium-sized enterprises (MSMEs) during the COVID-19 pandemic. This research method uses a quantitative approach and the analytical tool used is Structural Equation Modeling (SEM) based on Partial Least Square. The sampling technique used purposive sampling and the sample in this study was MSMEs who use social media for marketing. The social media used are Facebook, Instagram, and Whatsapp. The number of samples in this study was 254 MSMEs. Data collection used online questionnaires. The research findings show that the use of social media marketing is influenced by compatibility, perceived usefulness, and perceived ease-of-use. The results of this study indicate that the use of social media for marketing has a positive effect on the performance of MSMEs, especially in increasing sales, customer relationships, productivity, and creativity. This means that the better the management of social media for marketing, the better the performance of MSMEs will be. The theoretical contribution to this research is built on the TAM and UTAUT theories and their impact on social media marketing on MSMEs in developing countries that are living through the COVID-19 pandemic.

• Journal of Digital Convergence
• /
• v.12 no.1
• /
• pp.241-251
• /
• 2014

Mobile messenger services (MMSs), such as Kakaotalk, Wetalk, Line, and Whatsapp, are gaining popularity worldwide because of communicating with other people without any cost. We examined key antecedents of user loyalty to understand user's decision-making process in the MMS environment. To capture a long-term relationship with service providers, we posit relational commitment and user satisfaction as key predictors of user loyalty. Moreover, the effects of perceived usefulness, perceived enjoyment, and corporate image on user's decision-making processes were examined. The proposed model was tested by applying partial least squares to data from 220 experienced Kakaotalk users. The findings of this study found that both relational commitment and user satisfaction play an important role in enhancing user loyalty in the MMS context. The analysis results indicate that corporate image influences relational commitment and user loyalty through user satisfaction. The analysis results help MMS providers establish management or marketing strategies to effectively improve user loyalty.