Said A. Salloum

Mining Social Media Text: Extracting Knowledge from Facebook

Social media websites allow users to communicate with each other through several tools like chats, discussion forums, comments etc. This results in learning and sharing of important information among the users. The nature of information on such social networking websites can be straight forward categorized as unstructured and fuzzy. In regular day-to-day discussions, spellings, grammar and sentence structure are usually neglected. This may prompt various sorts of ambiguities, for example, lexical, syntactic, and semantic, which makes it difficult to analyse and extract data patterns from such datasets. This study aims at analyzing textual data from Facebook and attempts to find interesting knowledge from such data and represent it in different forms. 33815 posts from 16 news channels pages over Facebook were extracted and analyzed. Different text mining techniques were applied on the collected data. Findings indicated that Fox news is the most news channel that share posts on Facebook, followed by CNN and ABC News respectively. Results revealed that the most frequent linked words are focused on the USA elections. Moreover, results revealed that most of the people are highly interested in sharing the news of Mohammed Ali Clay through all the news channels. Other implications and future perspectives are presented within the study.

A Survey of Arabic Text Mining

Recently, text mining has become an interesting research field due to the huge amount of existing text on the web. Text mining is an essential field in the context of data mining for discovering interesting patterns in textual data. Examining and extracting of such information patterns from huge datasets is considered as a crucial process. A lot of survey studies were conducted for the purpose of using various text mining methods for unstructured datasets. It has been noticed that comprehensive survey studies in the Arabic context were neglected. This study aims to give a broad review of various studies related to the Arabic text mining with more focus on the Holy Quran, sentiment analysis, and web documents. Furthermore, the synthesis of the research problems and methodologies of the surveyed studies will help the text mining scholars in pursuing their future studies.

Analyzing the Arab Gulf Newspapers Using Text Mining Techniques

Nowadays, the broadcasting of news via social media networks is almost provided in a textual format. The nature of the broadcasted text is considered as unstructured text. Text mining techniques play an essential role in converting the unstructured text into informative knowledge. It has been observed that there is no research has addressed the textual analysis of Arabic newspapers on social media. Accordingly, this paper attempts to bridge this gap through building on related studies and applying various text mining techniques on a new under-researched context. 62,327 posts were collected from 24 Arab Gulf newspapers pages on Facebook. Results indicated that most of the discussed issues in the Arab Gulf region newspapers are related to trade, petroleum, and development. In addition, results revealed that the United Arab of Emirates (UAE) newspapers represent the source that is highly discussing issues regarding trade and economy followed by the Kingdom of Saudi Arabia (KSA) newspapers. Furthermore, results indicated that the financial and health-care issues news in the Arab Gulf region were highly tackled by Alkhaleej (UAE) newspaper. Besides, results pointed out that KSA newspapers, is on the top in disseminating issues regarding education.

Text Mining and Analytics: A Case Study from News Channels Posts on Facebook

Nowadays, social media has swiftly altered the media landscape resulting in a competitive environment of news creation and dissemination. Sharing news through social media websites is almost provided in a textual format. The nature of the disseminated text is considered as unstructured text. Text mining techniques play a significant role in transforming the unstructured text into informative knowledge with various interesting patterns. Due to the lack of literature on textual analysis of news channels’ in social media, the current study seeks to explore this genre of new media discourse through analyzing news channels online textual data and transforming its quantifiable information into constructive knowledge. Accordingly, this study applies various text mining techniques on this under-researched context aiming at extracting knowledge from unstructured textual data. To this end, three news channels have been selected, namely Fox News, CNN, and ABC News. Data has been collected from the Facebook pages of these three news channels through Facepager tool which was then processed using RapidMiner tool. Findings indicated that USA elections news received the highest coverage among others in these channels. Moreover, results revealed that the most frequent shared posts regarding the USA elections were tackled by the CNN followed by ABC News, and Fox News, respectively. Additionally, results revealed a significant relationship between ABC News and CNN in covering similar topics.

The Impact of Knowledge Sharing on Information Systems: A Review

Recently, knowledge sharing (KS) has become an essential research field in the context of knowledge management (KM). Information systems (ISs) are playing a key role in facilitating the process of KS. According to the literature, research neglects to review studies regarding the impact of KS on ISs. Therefore, the main objective of this study is to review studies regarding the impact of KS on ISs with an advanced focus on M-learning, social media, ERP systems, and weblogs. Furthermore, the synthesis of the research problems, research methods, and findings of the surveyed studies will assist the scholars in pursuing their future studies. Further discussions and implications are also presented in the study.

Adoption of E-Book for University Students

This paper employs the Technology Acceptance Model (TAM) to study the adoption of E-book amongst higher-education students in a well-known academic institute in the UAE, where E-book was being implemented. Computer self-efficacy, confirmation, innovativeness, satisfaction, and subjective norm are the five factors that this model embarks on to realize the influence on the university students as a result of the adoption of the E-book. This study was conducted among 350 university students through a survey which has used the quantitative evaluation to gain the optimum advantage from the subjective methods. The hypotheses were analyzed, and the model was assessed with the help of the statistical package for Structural Equation Modeling (SEM). The main findings that can be derived from the existing study are the factors that have positive impact on students’ perceived ease of use and perceived usefulness of E-book. They are computer self-efficacy, confirmation, innovativeness, and subjective norm. As a result, it is imperative for legislators and managers of E-book applications to concentrate on the factors that are critical for encouraging learning and enhancing students’ efficiency in developing and executing successful E-book applications.

Factors affecting the E-learning acceptance: A case study from UAE

The main objective of this article is to study the factors that affect university students’ acceptance of E-learning systems. To achieve this objective, we have proposed a new model that aims to investigate the impact of innovativeness, quality, trust, and knowledge sharing on E-learning acceptance. Data collection has taken place through an online questionnaire survey, which was carried out at The British University in Dubai (BUiD) and University of Fujairah (UOF) in the UAE. There were 251 students participated in this study. Data were analyzed using SmartPLS and SPSS. The Structural Equation Modelling (SEM) has been used to validate the proposed model. The outcomes revealed that knowledge sharing and quality in the universities have a positive influence on E-learning acceptance among the students. Innovativeness and trust were found not to significantly affect the E-learning system acceptance. By identifying the factors that influence the E-learning acceptance, it will be more useful to provide better services for E-learning. Other implications are also presented in the study.