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BackgroundThe massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. ObjectiveThis study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. MethodsWe used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. ResultsThe analysis of hate speech in Twitter data in the Arab region identified that the number of non–hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19–related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic. ConclusionsThe COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19–related tweets in the Arab region.

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Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In most academic institutes, there is an increasing demand on faculty members to perform not only more teaching and research but also community service and administrative works. Simultaneously, they also face growing pressure to improve the quality of their work. Therefore, maintaining a balance among these different tasks is a major issue in academic institutions, as it is imperative for academic administrators to ensure that the faculty members work productively and are also satisfied. Information systems that will help manage workload allocation and accurately measure academic input and output must be maintained. These systems will directly affect academic performance management. Accordingly, we propose and develop a faculty-load--management system (FLMS) for our university. The proposed system manages workload allocation and time-conflict detection in addition to information management and reporting. The proposed FLMS system is based on the client/server model. Two databases were developed, one for faculty and the other for courses, sections, and administrative tasks. On the client side, there will be a browser, which is responsible for user interaction, and it connects to a web portal (server), which performs data processing. FLMS was developed and tested for our academic department. It was evaluated by the faculty members and administrators, and the results showed that the system met their requirements and helped in maintaining both quality and faculty satisfaction. However, based on user feedback, some improvement to the FLMS is planned as future work. [ABSTRACT FROM AUTHOR]