Detecting non-human activity in social networks has become an area of great interest for both industry and academia. In this context, obtaining a high detection accuracy is not the only desired quality; experts in the application domain would also like having an understandable model, with which one may explain a decision. An explanatory decision model may help experts to consider, for example, taking legal action against an account that has displayed offensive behavior, or forewarning an account holder about suspicious activity. In this paper, we shall use a pattern-based classification mechanism to social bot detection, specifically for Twitter. Furthermore, we shall introduce a new feature model for social bot detection, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis. From our experimental results, we shall see that our mechanism outperforms other, state-of-the-art classifiers, not based on patterns; and that our feature model yields better classification results than others reported on in the literature.

Education 4.0 Framework reveals the need for the curricula of Higher Education Institutions to incorporate new learning approaches, along with new Competency Assessment Models. In the case of STEM (Science, Technology, Engineering, and Mathematics) programs, the inclusion of new technological tools is relevant to effectively assess the level reached by students in the development of transversal skills. NLP (Natural language processing) is a practical approach to understanding the effectiveness of learning processes because it provides solutions in various fields associated with the social and cultural context of Competency-Based Learning. In this study, we evaluate the usefulness of integrating NLP tools in evaluation procedures in advanced STEM subjects to aid the educator in developing competencies outside its core subject expertise. Different instruments were considered: surveys, questionnaires, interviews, observation lists, rubrics, and other tools to handle parametric data statistically. The study’s findings confirmed that the NLP tools are handy to evaluate higher-order functions and determine levels of cognitive understanding of concepts. Additionally, results showed that NLP tools could support instructors in carrying out better review and feedback sessions and providing personalized reports on oral and written communication skills.