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The internet and smartphone penetrations continue to rise reaching large percentages of the world populations. Likewise, many Jordanians are actively communicating through the popular social networks and mobile phone messages. There are large questions and concerns related to the characteristics and quality of the language used in these forums and how to improve it. This study addresses these issues by collecting and analyzing a large sample of text from five sources: Facebook, Twitter, news sites, blogging sites, and mobile phone short messages. We analyzed the sample comprehensively including the sender, context, message, channel, and code. We present in this paper the results related to the used language, alphabet, dialect, text components, and style. The study concludes that the bilingualism problem is manifested in Twitter and Facebook with 24% and 14% of contributions in English, respectively. Moreover, 6.4% of the analyzed Arabic samples have English words and 13.2% are written in Arabizi (Arabic in English letters and numerals). The diglossia problem is manifested as 55.4% of the sample is in colloquial Arabic, 36.4% in the standard Arabic, and 8.2% in standard with some colloquial words.

This article generalizes a characterization based on a truncated mean to include higher truncated moments, and introduces a new normality goodness-of-fit test based on the truncated mean. The test is a weighted integral of the squared distance between the empirical truncated mean and its expectation. A closed form for the test statistic is derived. Assuming known parameters, the mean and the variance of the test are derived under the normality assumption. Moreover, a limiting distributionfor the proposed test as well as an approximation are obtained. Also, based on Monte Carlo simulations, the power of the test is evaluated against stable, symmetric, and skewed classes of distributions. The test proves compatibility with prominent tests and shows higher power for a wide range of alternatives.