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Newsrooms and journalists across the world are adopting Generative AI (GenAI). Drawing on in-depth interviews with 23 journalists, this study identifies Bangladeshi journalists' awareness, acceptance, usage patterns, and their media organizations' stance toward GenAI. This study finds Bangladeshi journalists' high reliance on GenAI like their Western colleagues despite limited institutional support and the near absence of AI policy. Despite this contrast, concerns over GenAI's implications in journalism between the West and non-West were mostly identical. Moreover, this study contributes to the Unified Theory of Acceptance and Use of Technology (UTAUT) by proposing two changes regarding GenAI adoption among journalists in non-Western settings. First, this study identifies the non-contribution of facilitating conditions in shaping behavioral intent in GenAI adoption in non-Western contexts. Second, social influence works in a horizontal order through informal peer pressure or professional motivation in the absence of formal institutional hierarchical pressure. Voluntariness in the context of Bangladeshi journalists is underpinned by their professional compulsion. Therefore, this stu

This study was aimed at finding out if journalists in South East Nigeria have knowledge of Google Translate Application and also utilise it. It adopted a survey design with a sample size of 320 which was determined using Krejcie & Morgan (1970). Its objectives were to ascertain the extent journalists in South East Nigeria know about Google Translate Application, assess the utilisation of Google Translate Application among journalists in South East Nigeria, and identify the challenges affecting the journalists in South East Nigeria while using Google Translate Application. The theoretical underpin was Knowledge Attitude and Practise Model (KAP). The findings showed that journalists in South East Nigeria have knowledge of Google Translate Application but apply it mostly outside the region. It concludes that journalists in South East Nigeria have the knowledge of the App. but apply it outside the zone. The study recommends increased usage of the App. within South East Nigeria.

Public digital conversation around major sporting events takes place within a hybrid system in which journalists and the media compete with new intermediaries, including influencers, to gain greater visibility and engage with audiences. This study analyses the Qatar 2022 World Cup as a case of high informational intensity and public opinion monitoring. To that end, social network analysis was applied to X/Twitter using the hashtag #Qatar2022, analysing 1,343 high-engagement accounts, including those of journalists, media and influencers, alongside a random sample of 5,000 users. The findings indicate that journalists are under-represented in the user population as a whole, but significantly over-represented among the highest-engagement accounts, and they maintain stable visibility. The media, by contrast, attract a lower average level of attention and tend to achieve only sporadic peaks of impact. Accordingly, journalistic authority on social media is observed less as dominance in terms of participation volume and more as the capacity to occupy reference positions when public attention is being shaped during the event.

Journalists have incorporated social networks into their work as a standard tool, enhancing their ability to produce and disseminate information and making it easier for them to connect more directly with their audiences. However, this greater presence in the digital public sphere has also increased their exposure to harassment and hate speech, particularly in the case of women journalists. This study analyzes the presence of harassment and hate speech in responses (n = 60,684) to messages that 200 journalists and media outlets posted on X (formerly Twitter) accounts during the days immediately preceding and following the July 23 (23-J) general elections held in Spain in 2023. The results indicate that the most common forms of harassment were insults and political hate, which were more frequently aimed at personal accounts than institutional ones, highlighting the significant role of political polarization-particularly during election periods-in shaping the hostility that journalists face. Moreover, although, generally speaking, the total number of harassing messages was similar for men and women, it was found that a greater number of sexist messages were aimed at women journalists

As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust. Current practice offers two approaches: brief one-line labels or detailed disclosures specifying human oversight, editorial accountability, and error reporting mechanisms. Neither achieves journalists' goal of building trust through transparency. An existing controlled experiment with 34 news readers show that detailed disclosures trigger a \textit{transparency dilemma}, reducing trust rather than increasing it, and risk introducing dark patterns that readers scroll past with the illusion of transparency. One-line disclosures avoid this effect but can create an information gap, prompting readers to expend cognitive effort searching for signs of AI involvement that the disclosure indicates but does not explain. Yet readers are not rejecting transparency, they proposed disclosure designs centered on user agency: detail-on-demand interactions, proportional AI-ratio visualizations, outlet-level signals, and explicit "no AI" labels. I argue that this disconnect between what practitioners believe is responsible disclosure and what users actu

This study examines journalists' perceptions of the impact of artificial intelligence (AI) on disinformation, a growing concern in journalism due to the rapid expansion of generative AI and its influence on news production and media organizations. Using a quantitative approach, a structured survey was administered to 504 journalists in the Basque Country, identified through official media directories and with the support of the Basque Association of Journalists. This survey, conducted online and via telephone between May and June 2024, included questions on sociodemographic and professional variables, as well as attitudes toward AI's impact on journalism. The results indicate that a large majority of journalists (89.88%) believe AI will considerably or significantly increase the risks of disinformation, and this perception is consistent across genders and media types, but more pronounced among those with greater professional experience. Statistical analyses reveal a significant association between years of experience and perceived risk, and between AI use and risk perception. The main risks identified are the difficulty in detecting false content and deepfakes, and the risk of obta

Women are underrepresented in many areas of journalistic newsrooms. In this paper, we examine if this established effect continues in the new forms of journalistic communication, Social Media Networks. We used mentions, retweets, and hashtags as journalistic amplification and legitimation measures. Furthermore, we compared two groups of journalists in different stages of development: political and data journalists in Germany in 2021. Our results show that journalists regarded as women tend to favor their other women in mentions and retweets on Twitter, compared to men. While both professions are dominated by many men and a high share of men-authored tweets, women are mentioning and retweeting other women to a more extensive degree than their male colleagues. Women data journalists also leveraged different sources than men. In addition, we have found data journalists to be more inclusive towards non-member sources in their network compared to political journalists.

Recent discussions at the intersection of journalism, HCI, and human-centered computing ask how technologies can help create reader-oriented news experiences. The current paper takes up this initiative by focusing on immigrant readers, a group who reports significant difficulties engaging with mainstream news yet has received limited attention in prior research. We report findings from our co-design research with eleven immigrant readers living in the United States and seven journalists working in the same region, aiming to enhance the news experience of the former. Data collected from all participants revealed an "unaddressed-or-unaccountable" paradox that challenges value alignment across immigrant readers and journalists. This paradox points to four metaphors regarding how conversational AI agents can be designed to assist news reading. Each metaphor requires conversational AI, journalists, and immigrant readers to coordinate their shared responsibilities in a distinct manner. These findings provide insights into reader-oriented news experiences with AI in the loop.

The scientific community needs tools that help early-stage researchers effectively communicate their findings and innovations to the public. Although existing general-purpose Large Language Models (LLMs) can assist in this endeavor, they are not optimally aligned for it. To address this, we propose a framework for training LLMs to emulate the role of a science journalist that can be used by early-stage researchers to learn how to properly communicate their papers to the general public. We evaluate the usefulness of our trained LLM Journalists in leading conversations with both simulated and human researchers. %compared to the general-purpose ones. Our experiments indicate that LLMs trained using our framework ask more relevant questions that address the societal impact of research, prompting researchers to clarify and elaborate on their findings. In the user study, the majority of participants who interacted with our trained LLM Journalist appreciated it more than interacting with general-purpose LLMs.

On social platforms like Twitter, strategic targeted attacks are becoming increasingly common, especially against vulnerable groups such as female journalists. Two key challenges in identifying strategic online behavior are the complex structure of online conversations and the hidden nature of potential strategies that drive user behavior. To address these, we develop a new tree structured Transformer model that categorizes replies based on their hierarchical conversation structures. Extensive experiments demonstrate that our proposed classification model can effectively detect different user groups, namely attackers, supporters, and bystanders, and their latent strategies. To demonstrate the utility of our approach, we apply this classifier to real time Twitter data and conduct a series of quantitative analyses on the interactions between journalists with different groups of users. Our classification approach allows us to not only explore strategic behaviors of attackers but also those of supporters and bystanders who engage in online interactions. When examining the impact of online attacks, we find a strong correlation between the presence of attackers' interactions and chilling

The integration of artificial intelligence into journalistic practices represents a transformative shift in how news is gathered, analyzed, and disseminated. Large language models (LLMs), particularly those with agentic capabilities, offer unprecedented opportunities for enhancing journalistic workflows while simultaneously presenting complex challenges for newsroom integration. This research explores how agentic LLMs can support journalists' workflows, based on insights from journalist interviews and from the development of an LLM-based automation tool performing information filtering, summarization, and reporting. The paper details automated aggregation and summarization systems for journalists, presents a technical overview and evaluation of a user-centric LLM-driven reporting system (TeleFlash), and discusses both addressed and unmet journalist needs, with an outlook on future directions for AI-driven tools in journalism.

Engaging with online social media platforms is an important part of life as a public figure in modern society, enabling connection with broad audiences and providing a platform for spreading ideas. However, public figures are often disproportionate recipients of hate and abuse on these platforms, degrading public discourse. While significant research on abuse received by groups such as politicians and journalists exists, little has been done to understand the differences in the dynamics of abuse across different groups of public figures, systematically and at scale. To address this, we present analysis of a novel dataset of 45.5M tweets targeted at 4,602 UK public figures across 3 domains (members of parliament, footballers, journalists), labelled using fine-tuned transformer-based language models. We find that MPs receive more abuse in absolute terms, but that journalists are most likely to receive abuse after controlling for other factors. We show that abuse is unevenly distributed in all groups, with a small number of individuals receiving the majority of abuse, and that for some groups, abuse is more temporally uneven, being driven by specific events, particularly for footballe

As part of a broader look at the impact of generative AI, this study investigated the emotional responses of journalists to the release of ChatGPT at the time of its launch. By analyzing nearly 1 million Tweets from journalists at major U.S. news outlets, we tracked changes in emotional tone and sentiment before and after the introduction of ChatGPT in November 2022. Using various computational and natural language processing techniques to measure emotional shifts in response to ChatGPT's release, we found an increase in positive emotion and a more favorable tone post-launch, suggesting initial optimism toward AI's potential. This research underscores the pivotal role of journalists as interpreters of technological innovation and disruption, highlighting how their emotional reactions may shape public narratives around emerging technologies. The study contributes to understanding the intersection of journalism, emotion, and AI, offering insights into the broader societal impact of generative AI tools.

Large language models (LLMs) are increasingly used in daily work. In this paper, we analyze whether training in prompt engineering can improve the interactions of users with LLMs. For this, we conducted a field experiment where we asked journalists to write short texts before and after training in prompt engineering. We then analyzed the effect of training on three dimensions: (1) the user experience of journalists when interacting with LLMs, (2) the accuracy of the texts (assessed by a domain expert), and (3) the reader perception, such as clarity, engagement, and other text quality dimensions (assessed by non-expert readers). Our results show: (1) Our training improved the perceived expertise of journalists but also decreased the perceived helpfulness of LLM use. (2) The effect on accuracy varied by the difficulty of the task. (3) There is a mixed impact of training on reader perception across different text quality dimensions.

OpenStreetMap (OSM) is a vital resource for investigative journalists doing geolocation verification. However, existing tools to query OSM data such as Overpass Turbo require familiarity with complex query languages, creating barriers for non-technical users. We present SPOT, an open source natural language interface that makes OSM's rich, tag-based geographic data more accessible through intuitive scene descriptions. SPOT interprets user inputs as structured representations of geospatial object configurations using fine-tuned Large Language Models (LLMs), with results being displayed in an interactive map interface. While more general geospatial search tasks are conceivable, SPOT is specifically designed for use in investigative journalism, addressing real-world challenges such as hallucinations in model output, inconsistencies in OSM tagging, and the noisy nature of user input. It combines a novel synthetic data pipeline with a semantic bundling system to enable robust, accurate query generation. To our knowledge, SPOT is the first system to achieve reliable natural language access to OSM data at this level of accuracy. By lowering the technical barrier to geolocation verificatio

We created a software enabling journalists to define a set of criteria they would like to see applied regularly to a constantly-updated dataset, sending them an alert when these criteria are met, thus signaling them that there may be a story to write. The main challenges were to keep the product scalable and powerful, while making sure that it could be used by journalists who would not possess all the technical knowledge to exploit it fully. In order to do so, we had to choose Javascript as our main language, as well as designing the code in such a way that it would allow re-usability and further improvements. This project is a proof of concept being tested in a real-life environment, and will be developed towards more and more accessibility.

With the increasing dominance of the internet as a source of news consumption, there has been a rise in the production and popularity of email newsletters compiled by individual journalists. However, there is little research on the processes of aggregation, and how these differ between expert journalists and trained machines. In this paper, we interviewed journalists who curate newsletters from around the world. Through an in-depth understanding of journalists' workflows, our findings lay out the role of their prior experience in the value they bring into the curation process, their use of algorithms in finding stories for their newsletter, and their internalization of their readers' interests and the context they are curating for. While identifying the role of human expertise, we highlight the importance of hybrid curation and provide design insights on how technology can support the work of these experts.