SBC Reviews on Computer Science

The Use of Syntactic Information in Fake News Detection: A Systematic Review

2023-08-30T20:24:24+00:00

Fake news has been a critical problem for society, to the extent that its damaging effects can already be seen in several areas, such as democracy and health. However, as fake news grow in number, manual fact-checking becomes impractical for identifying them, which makes automatic detection a compelling alternative. In this sense, this study gathers multiple solutions for the problem of automatically detecting fake news, through the usage of both lexical and syntactic information. This study consists of a systematic review on fake news detection through linguistic patterns, focusing on the use of syntax to aid in the task. Solving complex problems by capturing linguistic patterns is mostly explored in the Natural Language Processing (NLP) area. In general, the use of shallow syntax representations, such as Parts of speech, only marginally increases the performance of classifiers in this task. However, relying on deeper syntactic representations, such as context-free grammars or syntactic dependency trees, present more promising results.

Applications of Socially Aware Design in Education: a systematic mapping of the literature

2024-01-15T14:45:18+00:00

Information and Communication Technologies (ICTs) affect the daily lives of people who use them and knowing the social context, values, culture, and rules, among other characteristics inherent to a society is fundamental to building inclusive, safe, and sustainable technologies. To include the social context in the design of a system and transform it into sociotechnical requirements, Socially Aware Design (SAwD) emerges. Likewise, teaching computing professionals to obtain a more human view of technology, in addition to enabling Software Engineering (SE) practices, which is extremely important for the training of these professionals, teaching SE is a great challenge. This research presents the execution of a systematic mapping of the literature that sought to discover studies that apply SAwD in Education, the techniques, methods, processes, and artifacts associated with SAwD, and the levels and areas of education that use it. It also presents other areas and application domains that consider SAwD in the design and construction process of interactive systems, whose objective is to find and implement socio-technical requirements collaboratively for an inclusive and sustainable design. The results point to the growing use of the SAwD over the years, the importance of including the social context in the development of information and communication technologies, as well as gaps for further research on the application of the SAwD in Education, to propose guidelines for developing games aimed at teaching SE.

The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks

2024-09-02T07:01:56+00:00

The expansion of Data Science projects in organizations has been led by three factors: the growth in the amount of data generated, the evolution in storage capacity, and the increase in computational capabilities. However, most of these projects fail to deliver the expected value: 82% of the teams do not use any process model. Despite the popularity of Agile Methods, their adoption in Data Science projects is still scarce. Most of the existing research focuses on algorithms. There is a lack of studies on agility in Data Science. This Systematic Literature Review (SLR) was performed to identify and evaluate 16 studies that can answer how to adapt and apply CRISP-DM using different approaches — methods, frameworks, or process models. In addition, it shows how CRISP-DM has evolved over the last few decades, with derivations emerging from rigid processes to agile methods. This research then analyzes the 16 tailored models and examines the similarities and differences between CRISP-DM derivatives. As a result, it summarizes the CRISP-DM adaptation patters identified, such as phase addition, phase modification, features and tools addition, and integration with other approaches. Consequently, this SLR showcases how CRISP-DM is a robust, flexible, and highly adaptable model that can be extended to different business domains. Finally, it proposes a theoretical guide to modify and customize CRISP-DM for Data Science projects.