Journalism, more so than other professions, is entangled with technology in a unique and profoundly impactful way. In this context, the technological developments of the past decades have fundamentally impacted the journalistic profession in more ways than one, opening up new possibilities and simultaneously creating a number of concerns for people working in the media industry. The changes that were brought about by the rise of automation and algorithmic technology can mainly be observed in four distinct fields of application within journalism: automated content production, data mining, news dissemination and content optimization. This article focuses on algorithmic journalism and aims to highlight the ways that algorithmic technology is being utilized within those fields, as well as pointing out the ways in which these developments have altered the way journalism is being exercised in the modern world. The study also discusses challenges related to these technologies that are yet to be addressed, as well as potential future implementations related to algorithmic journalism that have the capacity to improve on the foundation of automation in the news industry.

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 journalistic profession has long since entered an age where technology and audience participation are two of its most defining factors. Changes that were brought about by the advent of WEB 2.0 transformed journalism – among other professions – fundamentally, and opened the gates to a more connected future, one in which the lines between content producer and content consumer are far less defined than they used to be. One of the more promising technologies of this new era is that of chatbots and conversational agents. These multifaceted programs have proven to be extremely useful in many aspects of modern journalism, with some of them getting used in scenarios that go as far as trying to replace the traditional role of the journalist as we know it. As such, the influence of these programs has also spread to the field of audience interaction and participatory journalism. This article aims to underline the integral role that chatbots play within the confines of the journalistic profession, while at the same time explore the significant effects they have in the field of audience participation and communication between the journalist and the public in general. To achieve this goal a model chatbot was created in order to demonstrate the benefits of automating the process of filing and transferring a report on account of the public to the news organization.

Purpose: News consumption is a topic that has always been of great interest to media researchers and the media industry alike since news access and intake tendencies play an important role in comprehending the audience’s behavior. This research attempts to shed light on the habits and the news features that determine the selection of a medium over another as well as a news item over another by examining emergent practices of online information reception. Design/methodology/approach: Through qualitative research using three focus groups, the study addresses questions on the preferred media formats, the imposed social influences and the structural elements of news content, while also examining emerging digital features such as text-to-speech and reading time indicators incorporated in contemporary online news media. Findings: The findings highlight a lack of significant generational distinctions in media preferences, while the social influences and media brand name were found to shape media choices, holding crucial roles in some cases. The existence of a commenting sector was also found to be critical for fostering online communities and supporting democratic dialogue. Originality/value: The study through research across demographics provides evidence on current news consumption and engagement while marking credibility as a core concern. Digital interactions are examined through the role of social circles that significantly shape media choices, and of content structure, visuals and new media features that drive engagement. Commenting on articles is seen as crucial for democratic dialogue and critical thinking in online media. Thus, valuable conclusions regarding the broader digital transformation of news consumption are extracted for journalists, the media industry and society.

Interactivity has been a very sought-after feature in professional journalism ever since the media industry transitioned from print into the online space. Within this context, chatbots started to infiltrate the media sphere and provide news organizations with new and innovative ways to create and share their content, with an even larger emphasis on back-and-forth communication and news reporting personalization. The present research highlights two important factors that can determine the efficient integration of chatbots in professional journalism: the feasibility of chatbot programming by journalists without a background in computer science using coding-free platforms and the usability of the created chatbot agents for news reporting to the audience. This paper aims to review some of the most popular, coding-free chatbot creation platforms that are available to journalists today. To that end, a three-phase evaluation framework is introduced. First off, the interactivity features that they offer to media industry workers are evaluated using an appropriate metrics framework. Secondly, a two- part workshop is conducted where journalists use the aforementioned platforms to create their own chatbot news reporting agents with minimum training, and lastly, the created chatbots are evaluated by a larger audience concerning the usability and overall user experience.

Purpose: This study aims to investigate the perception of automated versus traditionally produced news content among general readers and professional journalists. It specifically examines the ability of these groups to distinguish between machine-generated and human-written articles and evaluates their assessments of content quality across various dimensions. Design/methodology/approach: The study utilized automated content created with certain parameters by large language models, specifically OpenAI’s ChatGPT, and compared it to content produced by traditional means via a questionnaire. Two different groups were examined: university students, representing general readers and professional journalists. Non-parametric statistical methods were applied to analyze the results, focusing on the ability of participants to differentiate between the two types of content and their evaluations of various quality dimensions. Findings: Results indicate that neither journalists nor university students could reliably identify the origin of an article with any statistically significant accuracy. There were, however, distinct differences regarding how each group perceived the individual characteristics of each text, suggesting a divergence between the evaluation criteria for each of the two groups. Originality/value: This study contributes to the growing body of research on the integration of AI in journalism by providing empirical insights into how both readers and professional journalists perceive automated content. It recreates a familiar experimental structure but modernizes the way automated journalistic content is produced for the purposes of the experiment, with the utilization of large language models, as opposed to more antiquated algorithmic solutions.