This article examines digital activism on Twitter against gender-based violence with regard to the March 8, 2017, feminist movement in Spain (8M). We explore a sample of 20,000 messages (tweets, retweets, and mentions) of the most commonly utilized hashtags in this context, such as #8M and #NiUnaMenos (Not one less [woman]). We analyze if, in the case of 8M in Spain, digital feminist activism constituted a challenge to the hegemonic frames of representation of gender-based violence. Using the concept of “ethical testimony,”, we demonstrate the opportunity that giving testimony on gender-based violence offers to digital feminist activism to act in an effective, political manner on Twitter, circumventing invisibility. These hashtags do not form strong conversational communities but rather serve to diffuse messages at a mass scale. Within these conversational communities, the term “victim” is rarely utilized, which we interpret as an accomplishment of women as agents capable of resisting their position of vulnerability.

Cancer is the second leading cause of death worldwide, and within this type of disease, lung cancer is the second most diagnosed, but the leading cause of death. Early detection is crucial to increase patient survival rates. One of the primary methods for detecting this disease is through medical imaging, which, due to its features, is well-suited for analysis by deep learning techniques. These techniques have demonstrated exceptional results in similar tasks. Therefore, this paper focusses on analyzing the latest work related to lung cancer detection using deep learning, providing a clear overview of the state of the art and the most common research directions pursued by researchers. We have reviewed DL techniques for lung cancer detection between 2018 and 2023, analyzing the different datasets that have been used in this domain and providing an analysis between the different investigations. In this state-of-the-art review, we describe the main datasets used in this field and the primary deep learning techniques used to detect radiological signs, predominantly convolutional neural networks (CNNs). As the impact of these systems in medicine can pose risks to patients, we also examine the extent to which explainable AI techniques have been applied to enhance the understanding of these systems, a crucial aspect for their real-world application. Finally, we will discuss the trends that the domain is expected to follow in the coming years and the challenges that researchers will need to address.

The relationship between social work and Information and Communication Technologies (ICTs) is an awkward one in Spanish tradition, particularly from social work perspective due to a certain lack of institutional and professional competence in terms of understanding the capabilities of ICTs. Young people use ICTs to connect to each other and express themselves, however. ICTs represent a means—increasingly the main one—for young people to build and communicate their own identities and understand reality. As with other social sciences, social work suffers from a difficult relationship with young people. Youth is often studied differently and sometimes perceived as a problem that ends when adulthood is reached. In this context, the relationship among ICTs, social work and young people is also a difficult one. But this problem may also be a solution. By adopting a critical ICT approach, social work can create innovative initiatives and frameworks to improve communication between social work practitioners and teachers as with young people and students. Imagination and empathy will clearly be key to achieving this, in addition to deeper involvement in the use of new software and applications that can offer enhanced communication and build bridges between social workers and young people.

Instant messenger Telegram has emerged as a favoured platform for far‐right activism, conspiracy theories, political propaganda, and misinformation, which has its own target audience. This study explores the application of multilingual pre‐trained language models to detect and measure toxicity in political content on Telegram channels. The proposed techniques have shown notable advancements in identifying toxic information using a fine‐tuned RoBERTa model. Through the combination of data analysis, time‐series analysis, and BERTopic modelling, the research demonstrates how toxicity varies by topic, country, and time period, using metadata. The study identified key topics in the dataset, which includes 23.6 million messages from 1491 Telegram channels, including the Russian–Ukrainian conflict and political tensions in Europe and the United States from 2016 to 1 July 2023. Despite these achievements, challenges such as the dominance of Russian language content and a focus on specific topics were highlighted. This research advances the understanding of how toxic language and propaganda are disseminated across different languages and political narratives, contributing to the study of digital communication and information warfare.

Resumen El surgimiento del partido político Podemos en España, en el contexto de crisis de representación política, se explica en una perspectiva de interacciones transformadoras entre esferas públicas, movimientos sociales y nuevas tecnologías. La coyuntura abierta tras el ciclo 15M ha propiciado un nuevo diagnóstico político recogido por Podemos, activamente puesto en circulación en las redes sociales, verdadera plataformas de difusión electoral y organizativa para partidos de bajos presupuestos. Analizando Twitter y Facebook se confirma su veloz proceso de crecimiento y liderazgo en redes, el perfil de sus seguidores y publicaciones con mayor impacto y difusión de estos nuevos discursos. Se concluye analizando los retos de este tipo de partidos para incorporarse al panorama político español y europeo.

Facebook ya forma parte de nuestro imaginario social. Su notoriedad ha crecido exponencialmente en la era del Big Data. Este artículo ilustra algunas de las formas en que las redes sociales podrían funcionar como un recurso importante de información para la investigación social. Estas posibilidades se ilustran a través del estudio de caso de grupos de personas que conducen bajo los efectos del alcohol y las drogas. Método. Este estudio se basa en herramientas cuantitativas y cualitativas: un breve cuestionario de encuesta online accesible a través de Facebook, que permite la obtención de datos primarios cuantitativos, y el análisis cualitativo de los Me gusta de Facebook (215.232) de los encuestados (n = 1.437). Resultados. El análisis muestra que se pueden diferenciar tres grupos de riesgo (alto, medio, bajo) entre los participantes en función de sus patrones de conducción bajo la influencia del alcohol y las drogas y en función de los estilos de vida, que reflejan a través de sus Me gusta en Facebook, y de la estructura clásica. variables. En particular, el grupo de alto riesgo exhibe preferencias y gustos más cercanos al descontento y la resistencia social, y es el grupo con menor nivel educativo y mayor tasa de empleo eventual. Conclusiones. Esta investigación muestra la utilidad de las redes sociales en la caracterización sociodemográfica y cultural de los grupos de riesgo, lo que debe ser un paso previo al diseño y difusión de campañas de prevención y sensibilización.