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Social media have established a new way of communicating and understanding social relationships. At the same time, there are downsides, especially, their use of algorithms that have been built and developed under their umbrella and their potential to alter public opinion. This paper tries to analyse the YouTube recommendation system from the perspectives of reverse engineering and semantic mining. The first result is that, contrary to expectations, the issues do not tend to be extreme from the point of view of polarisation in all cases. Next, and through the study of the selected themes, the results do not offer a clear answer to the proposed hypotheses, since, as has been shown in similar works, the factors that shape the recommendation system are very diverse. In fact, results show that polarising content does not behave in the same way for all the topics analysed, which may indicate the existence of moderators –or corporate actions– that alter the relationship between the variables. Another contribution is the confirmation that we are dealing with non-linear, but potentially systematic, processes. Nevertheless, the present work opens the door to further academic research on the topic to clarify the unknowns about the role of these algorithms in our societies. [ABSTRACT FROM AUTHOR]

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Training teachers means confronting them with challenges that enable them to develop social and technological skills for their future career. This article seeks to understand which social problems teachers draw attention to on Twitter and the educational arguments they put forward to address them in class. The digital sources used were also examined. The sample comprised 574 students of the bachelor's degree in primary education at the University of Seville, and our research was conducted in three phases with two types of coding, open and axial, based on grounded theory. The analysis yielded 19 social problems, 4 levels of complexity of educational arguments, and 12 most frequent sources of information. The findings show a need to develop skills to improve the critical filtering of information obtained from digital media and the communication of social problems that can potentially be used in education. Our conclusions highlight the viability of these activities in promoting social media as tools to further knowledge for teaching. [ABSTRACT FROM AUTHOR]

This article offers methodological and theoretical reflections based on the interdisciplinary project "Algorithmic Identities." Initiated in 2019 between researchers from the Pontificia Universidad Católica de Chile and the University of Warwick (UK), this project seeks to understand how personhood is configured in times of algorithms and digital data. We designed a prototype app, "Big Sister," which emulates algorithmic profiling and recommendation systems of major digital platforms. This prototype has served as a twofold social research device. Firstly, it allowed us to produce knowledge from a critical and decolonial doing and undoing; secondly, through digital trace interviews with prototype users, we explored their experiences and stimulate reflection on the mediations of algorithms in their lives. This article examines the prototyping process of this app and some findings of how people in Chile understand, inhabit, and shape data and algorithms daily. Drawing on insights from Latin American authors such as Arturo Escobar, Jesús Martín-Barbero, and Paulo Freire, we argue how the prototyping of an app can move towards a "problematizing" approach to datafication and algorithmic systems in Latin America. [ABSTRACT FROM AUTHOR]

Copyright of Austral Comunicacion is the property of Austral Comunicacion 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 a Web dominated by social media for information, relationships and communication, the established dynamics between content, people and technology change radically. Given the relevance of the user-generated content in such a scenario and its essentially relational nature, successfully locating the best sources of information requires the development of recommender systems that incorporate the social characteristic of scenarios built on a network that goes beyond the original Internet. The article provides a review of current approaches to the process of recommendation, placing them in the context of consolidated trends associated with the phenomenon of Social Computing, and highlights some lines of development in the area of redefining the problem of recommendation in a context dominated by social networks and user-generated content. [ABSTRACT FROM AUTHOR]