Abstract-Through this article, we try to propose the design of notification system that runs on the Android operating system. This notification will be used for the academic announcements in the campus environment. The current announcement system only depends on the web portal, phones, groups in social media or short message system. The use of web portals and phones still has many limitations. Our proposed notification system can meet the needs of academic announcement broadcast quickly and massively. In the end, this will cut the cost that stakeholders need to operate the academic announcement system in the campus environment. The design of smart notification system that we proposed in the future will be integrated with the academic information system.

Social media has become a constant in our everyday life. However, its steady growth has increased the hate speech and hostile content problem. To curb this, hate speech detection and recognition is required, but it is faced to two major challenges - laws and enforcement, and automatic computerized hate speech detection. Although many studies are already implemented in detecting hate content, many of these are done in a single setting showing a single dataset in comparison to machine learning or deep learning models. Thus, there is no comparison between previous approaches and recent inventions such as transformer model. Therefore, in this work we explored and compared recent advanced approaches in automatic hate speech detection. Our aim is to analyze the influence different approaches in detecting hate content and its applicability in the real world. Several experiments were conducted on eight real hate speech datasets from recent studies. We present the results of each comparison which shows that the recent transformer model approach is able to outmatch many of the previous hate speech recognition models by significant G-Means and F1 scores. To the author’s knowledge, this paper is the first attempt to present a large comparative study of approaches in hate speech detection.

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