Purpose: The purpose of this study is to test the relationship between auditor rotation and the level of auditor independency, and to investigate, the impact of auditor rotation, audit fees, audit tenure and the relationship with client on the auditor independency in Jordanian audit firms. Furthermore, to identify the reasons behind rotating the auditor and the advantages/disadvantages of auditor rotation.Design/methodology/approach: The study has been carried out through a questionnaire survey that aimed to collect data about the auditor independency in Jordan, and through a developed model to test the mandatory auditor rotation in the Jordanian audit firms. Findings:After defining the four independent main variables of the auditor independency (auditor-client relationship, audit tenure, audit fees, and mandatory auditor rotation). The findings of the study revealed that there is a significant positive relationship between the auditor independency and auditor-client relationship and mandatory rotation. A negative one with the audit fees and there is no relationship with audit tenure. Research limitations/implications:This paper has some limitations such as, some audit firms refused to allow researchers to distribute the questionnaire which is not familiar in Jordan.Originality Value: This paper contributes to the understandings of the nature and characteristics of the auditor independency by empirically exploring the nature and characteristics of mandatory auditor rotation in Jordan.

The aim of sentiment analysis is to automatically extract the opinions from a certain text and decide its sentiment. In this paper, we introduce the first publicly-available Twitter dataset on Sunnah and Shia (SSTD), as part of a religious hate speech which is a sub problem of the general hate speech. We, further, provide a detailed review of the data collection process and our annotation guidelines such that a reliable dataset annotation is guaranteed. We employed many stand-alone classification algorithms on the Twitter hate speech dataset, including Random Forest, Complement NB, DecisionTree, and SVM and two deep learning methods CNN and RNN. We further study the influence of word embedding dimensions FastText and word2vec. In all our experiments, all classification algorithms are trained using a random split of data (66% for training and 34% for testing). The two datasets were stratified sampling of the original dataset. The CNN-FastText achieves the highest F-Measure (52.0%) followed by the CNN-Word2vec (49.0%), showing that neural models with FastText word embedding outperform classical feature-based models.

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