The security and reliability of Arabic text exchanged via the Internet have become a challenging area for the research community. Arabic text is very sensitive to modify by malicious attacks and easy to make changes on diacritics i.e. Fat-ha, Kasra and Damma, which are represent the syntax of Arabic language and can make the meaning is differing. In this paper, a Hybrid of Natural Language Processing and Zero-Watermarking Approach (HNLPZWA) has been proposed for the content authentication and tampering detection of Arabic text. The HNLPZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark key. Fifth level order of word mechanism based on hidden Markov model is integrated with digital zero-watermarking techniques to improve the tampering detection accuracy issues of the previous literature proposed by the researchers. Fifth-level order of Markov model is used as a natural language processing technique in order to analyze the Arabic text. Moreover, it extracts the features of interrelationship between contexts of the text and utilizes the extracted features as watermark information and validates it later with attacked Arabic text to detect any tampering occurred on it. HNLPZWA has been implemented using PHP with VS code IDE. Tampering detection accuracy of HNLPZWA is proved with experiments using four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experimental results show that HNLPZWA is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection.

A social network is a useful model for identifying hidden structures and meaningful knowledge among social atoms, which have complicated interactions. In recent years, most studies have focused on the real data of the social space such as emails, tweets, and human communities. In this paper, we construct a social network from literary fiction by mapping characters to vertices and their relationship strengths to edges. The main contribution of this paper is that our model can be exploited to reveal the deep structures of fiction novels by using graph theoretic concepts, without the involvement of any manual work. Experimental evaluation showed that our model successfully classified fictional characters in terms of their importance to the plot of a novel.

As the recent growth of online social network services such as Facebook and Twitter, people are able to easily share information with each other by writing posts or commenting for another's posts. In this paper, we firstly suggest a method of discovering information flows of posts on Facebook and their underlying contexts by incorporating process mining and text mining techniques. Based on comments collected from Facebook, the experiment results illustrate how the proposed method can be applied to analyze information flows and contexts of posts on social network services.

We have developed a visualization system for dialog text exchanged in e-learning virtual classrooms. In this system, text-based online discussions among learners are effectively visualized as discussions held in a virtual classroom in cyberspace. Discussion participants are displayed as avatars. The virtual classroom maintains the interest of learners because it incorporates professional camerawork and switching know-how based on rules derived from an analysis of 42 TV programs. The gestures of the CG avatar depend on the dialog text. A series of virtual classroom experiments confirmed that elementary and junior high school students maintained an interest in using the system.