Scholars conceptualize journalism’s transformation and explain the changes occurring at different levels under the rubric of convergence. Contrary to optimistic views of convergence, claiming for its potential to satisfy both good journalism and good business practices, the paper argues that at times of economic uncertainty, hyper-competition and diminishing accountability levels, convergence is used as a cost-effective strategy fostering low-cost and spreadable news production. Engaging in quantitative analysis, the article provides empirical evidence showing that the recycling of news content from established elite sources and across popular news sites has increased between 2013 and 2016, posing serious threats for content plurality and independent reporting. Despite online journalism’s development as a field, and the appearance of social networks and user-generated content as alternative and easyily accessible sources, a pervasive survival and monetization culture has turned churning into a mainstream journalistic practice.

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

The exploitation of data in the media industry has always played a significant role. This is especially evident today, since data (and in many cases big data) are generated through various activities that relate to the production and also consumption of news. This paper attempts to highlight the importance of big data utilization in the media industry. Specifically, it discusses cases of big data exploitation, such as media content consumption and management, data journalism production, social content utilization, and participatory journalism applications. The study also examines the changes that big data has introduced in all stages of the journalism practice, from news production to news distribution, by utilizing the available tools. Finally, it discusses new developments that relate to semantic web (Web 3.0) technologies, which have already started to be adopted by media organizations around the world.

The emergence of generative AI has led to significant transformations, reshaping media organizations’ workflows and facilitating new forms of participation in the news. This scoping review aims to map the existing literature on the role of generative AI in enhancing audience participation and engagement in journalism. It investigates the theories and methods employed in relevant studies, emerging areas of focus and AI applications used by media organizations to foster audience involvement with the news. The data collection was conducted using online sources (Scopus, EBSCO, ProQuest, and Google Scholar) and a total of 30 studies, published between 2022 and 2024, were selected based on the following criteria: English-language, peer-reviewed, and (hybrid) open-access publications relevant to the research objectives and aligned to the search keywords. The results reveal different theoretical and empirical approaches to studying generative AI in journalism, emphasizing emerging focus areas regarding the adoption of relevant tools and the legal and ethical challenges associated with the credibility of news content. Additionally, generative AI applications in journalism primarily serve to facilitate participation and engagement through personalization, accessibility and interactive storytelling. Lastly, this study highlights the potential of generative AI to enhance audience participation in the news, underscoring the ethical and practical challenges it poses as well as existing research gaps, setting the stage for further research.

During the last two decades, citizens’ participation in news production process has attracted significant interest from both academia and the media industry. Media production and consumption have been altered considerably and traditional concepts, such as gatekeeping, have been under discussion. Many news organisations include in their websites tools and applications that allow users to be active consumers or even co-producers of journalistic content, by liking, sharing, commenting and submitting material. At the same time, large amounts of user-generated content are uploaded every day on social media platforms. Subsequently, media organisations must deal with continually available information which requires management, classification and evaluation not only to keep high journalistic standards, but also to avoid problems. The latter category can include grammar mistakes, fake or misleading information and hate speech. All the above-mentioned parameters highlight the obvious need for platforms that can support journalism manage practice. Such a platform should utilise semantic technologies, which can support organised collection and moderation of content in an effective way and in short time. This study discusses the design and the implementation of a participatory journalism management platform.

The paper presents the motivation, development, and evaluation of an AI-driven framework for media stream impact analysis at the consumption end, employing user reactions monitoring through attention tracking (i.e., eye and mouse tracking). The adopted methodology elaborates on software and system engineering processes, combining elements of rapid prototyping models with interdisciplinary participatory design and evaluation, leaning on the foundation of information systems design science research to enable continuous refinement through repeated cycles of stakeholder engagement, feedback, technical iteration, and validation. A dynamic Form Builder has been implemented to supplement these tools, allowing the construction and management of pre- and post-intervention questionnaires, thus helping associate collected data with the respective tracking maps. The present begins with the detailed presentation of the tools’ implementation, the respective technology, and the offered functionalities, emphasizing the perception of tampered visual content that is used as a pilot evaluation and validation case. The significance of the research lies in the practical applications of AI-assisted monitoring to effectively analyze and understand media dynamics and user reactions. The so-called iMedius framework introduces an integration of innovative multidisciplinary procedures that bring together research instruments from the social sciences and multimodal analysis tools from the digital world.