Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing rate of cyber hate speech. This drew our attention to aspire healthy online environments that are free of hatred and discrimination. Therefore, this article aims to detect cyber hate speech based on Arabic context over Twitter platform, by applying Natural Language Processing (NLP) techniques, and machine learning methods. The article considers a set of tweets related to racism, journalism, sports orientation, terrorism and Islam. Several types of features and emotions are extracted and arranged in 15 different combinations of data. The processed dataset is experimented using Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF), in which RF with the feature set of Term Frequency-Inverse Document Frequency (TF-IDF) and profile-related features achieves the best results. Furthermore, a feature importance analysis is conducted based on RF classifier in order to quantify the predictive ability of features in regard to the hate class.

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The World Wide Web (WWW) today is so vast that it has become more and more difficult to find answers to questions using standard search engines. Current search engines can return ranked lists of documents, but they do not deliver direct answers to the user. The goal of Open Domain Question Answering (QA) systems is to take a natural language question, understand the meaning of the question, and present a short answer as a response based on a repository of information. In this paper we present QARAB, a QA system that combines techniques from Information Retrieval and Natural Language Processing. This combination enables domain independence. The system takes natural language questions expressed in the Arabic language and attempts to provide short answers in Arabic. To do so, it attempts to discover what the user wants by analyzing the question and a variety of candidate answers from a linguistic point of view.

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The majority of Arabic text available on the web is written without short vowels (diacritics). Diacritics are commonly used in religious scripts such as the holy Quran (the book of Islam), Al-Hadith (the teachings of Prophet Mohammad (PBUH)), children's literature, and in some words where ambiguity of articulation might arise. Internet Arabic users might lose credible sources of Arabic text to be retrieved if they could not match the correct diacritical marks attached to the words in the collection. However, typing the diacritical marks is very annoying and time consuming. The other way around, is to ignore these marks and fall into the problem of ambiguity. Previous work suggested pre-processing of Arabic text to remove these diacritical marks before indexing. Consequently, there are noticeable discrepancies when searching the web for Arabic text using international search engines such as Google and yahoo. In this article, we propose a framework to enhance the retrieval effectiveness of search engines to search for diacritic and diacritic-less Arabic text through query expansion techniques. We used a rule-based stemmer and a semantic relational database compiled in an experimental thesaurus to do the expansion. We tested our approach on the scripts of the Quran. We found that query expansion for searching Arabic text is promising and it is likely that the efficiency can be further improved by advanced natural language processing tools.