This study investigates the performance of search engine chatbots powered by large language models in generative political information retrieval. Applying algorithmic accountability as a central theme, this research (a) assesses the alignment of artificial intelligence (AI) chatbot responses with timely political information, (b) investigates the factual correctness and transparency of chatbot-sourced synopses, (c) examines the adherence of chatbots to democratic norms and impartiality ideals, (d) analyzes the sourcing and attribution behaviors of the chatbots, and (e) explores the universality of chatbot gatekeeping across different languages. Using the 2024 Taiwan presidential election as a case study and prompting as a method, the study audits responses from Microsoft Copilot in five languages. The findings reveal significant discrepancies in information readiness, content accuracy, norm adherence, source usage, and attribution behavior across languages. These results underscore the contextual awareness when applying accountability assessment that looks beyond transparency in AI-mediated communication, especially during politically sensitive events.

Scandinavians are among the most datafied citizens in the world. With its digitalised welfare states, Danish, Norwegian, and Swedish e-governance infrastructures collect massive amounts of data about citizens as they search for jobs, apply for building permits, and check school calendars. In this article, we analyse the use of third-party trackers (n = 2,761) on Scandinavian municipal websites (n = 745) between 2007-2023. Mobilising the theoretical framework of universalism, our aim is to understand what kind of cost data tracking constitutes for users of digital government services. Results show that Scandinavian municipal websites are dominated by commercial trackers harvesting citizen data for advertising purposes, particularly those provided by Alphabet and Meta. We conclude that commercial user-tracking on Scandinavian municipal websites does not conform to the principle of universality, proposing 1) that governments ensure transparency of the cost incurred by these websites' data tracking, and 2) that they ban commercial tracking on municipal websites.

This study examines the role of news media in the context of generative AI-enhanced search engines, focusing on the 2024 Taiwan presidential election. Using Microsoft’s Copilot, we conducted a comparative analysis by prompting election news in five languages: English, Traditional Chinese, Simplified Chinese, German, and Swedish. While Copilot uses mainly professional news media, provides quick access to synthesized information, and exhibits source transparency, it frequently creates misinformation and misattributes news sources. The analysis highlights variations in Copilot’s sourcing behavior, showing a strong reliance on English-language sources, particularly those from the UK and US, across different prompting languages. Such reliance raises concerns about the homogenization of information and the marginalization of regional perspectives. The study underscores the critical role and dilemma of news media, which, while serving as authoritative sources in democratic societies, must navigate an increasing AI-mediated information ecosystem to maintain autonomy vis-à-vis powerful technological infrastructures. By evaluating Copilot’s sourcing practices and misinformation prevalence, this research contributes to the discourse on AI’s impact on news dissemination, media diversity, and democratic processes. Specifically, we discuss the consequences of two approaches available to news media to prevent their content from being used without compensation: opting out of crawling (“platform counterbalancing”) or establishing partnerships with AI companies. Current regulatory efforts, including copyright reforms and the EU AI Act, fall short of safeguarding journalism or regulating AI. We propose policy and regulatory recommendations to improve transparency, factual correctness, accuracy in source attribution, and accountability in AI-generated content, supporting informed citizenship in the digital age. 

Geomedia, representing an epochal shift in spatial mediations and spatialized media, changes daily life. This future-directed thematic issue advocates for contextualized understandings of geomedia that transcend contemporary hegemonic representations of technology. It recognizes the transformative powers of geomediatization processes and asks what “geomedia futures” such processes might bring about. Bridging critical geomedia studies and critical future studies, it challenges dominant narratives about tomorrow’s technological society and promotes the exploration of diverse, equitable, and sustainable futures with and under geomedia. Through numerous methodological approaches, the collected articles examine the role of geomedia in contexts such as urban planning, tourism, surveillance, governance, and policy. The thematic issue emphasizes the importance of envisioning alternative futures that resist technological rationalization and unethical exploitation of geospatial data, supporting more inclusive and human-centered mediatized places. This work contributes to ongoing debates in geomedia studies, highlighting the need for critical and interdisciplinary approaches to understand and shape our technological future.

This study explores the visual representation of Great Yarmouth, a British coastal town caught between the urban and the rural, as seen through the quasi-monopolistic image search engine Google Images. The research examines levels of pluralistic or biased place representations to consider how rankings employed by Google Images algorithms represent Great Yarmouth’s identity. The study adopts a visual culture perspective that recognises the role of images in place making and combines digital methods with an image type analysis to investigate how online representations reflect and create the town’s identities. The data shows that Google Images’ preference for representing Yarmouth as a sunny seaside town indicates that the search engine prioritises marketable assets above its connections with its hinterland, its diversity of people, and the cultural activities it has to offer. This, the authors state, is a place far away from Tuan’s (1979) idea of a place that is given meaning and identity from the perspective of people. Instead, Google Images’ representations of Great Yarmouth are an example of a created form of place making as commodification. The article concludes that the inscribed bias and unbalanced search priority criteria employed by the search engine impact upon the diversity of the semi-peripheral town. 

Visibility and its counterpart, invisibility, are critical concepts in digital communication, although research on these concepts in communication studies has rarely been reflected on in an integrative way. This article aims to map key discussions in current research on in/visibility from a communication studies perspective. Therefore, through a literature review, we elaborate on these discussions in research areas dealing extensively with in/visibility. The resulting mapping highlights similarities and differences between definitions of in/visibility and systematizes the various approaches according to three essential understandings (perceptibility, presence, and valuation) and three paradigmatic perspectives (functionalist, interpretive, and critical). The article offers a deeper understanding of the range of previous studies on the undertheorized concepts of in/visibility and demonstrates the concepts' potential for future research within communication studies.

The diffusion of algorithms, robots, and artificial intelligence is accompanied by public debates about opportunities and risks of automation and responsibility for problems and the development of solutions. Since media coverage plays a central role in shaping public perception of automation, this study examines the Austrian media discourses on responsibility in two automation fields: social media algorithms and social companions. The comparative content analysis reveals that social media algorithms are more critically covered than social companions. When it comes to social media algorithms, responsibility issues are often raised by journalists, and responsibility is primarily assigned to the Internet platforms. To the contrary, risks of social robotics are articulated predominantly by experts, and seen as challenges for society, economy, and research alike. Moreover, media provide different perspectives to the question whether algorithms in social media and social robots have agency and can assume responsibility themselves.

Das Schlüsselwerk von Drumwright und Murphy verweist einerseits auf die Vernachlässigung der Produzent*innenethik in der Werbeforschung und andererseits auf die Feststellung, dass viele Werbepraktiker*innen moralisch zu kurzsichtig sind, um sich mit Fragen ethischen Handelns zu beschäftigen oder stumm darin, ihre Bedenken zu äußern. Während theoretische und methodische Ansätze der Erforschung von Werbeethik generell rar sind, wird dies insbesondere für die Meso- und Makroebene betont. So sind organisationale Ansätze zur Erforschung der Werbeethik rar, wären jedoch geboten, da sie berücksichtigen können, inwiefern Organisationskulturen und -strukturen die ethische Entscheidungsfindung von Werber*innen beeinflussen. Das Schlüsselwerk zeichnet sich dadurch aus, dass die Werbeethik aus Perspektive der Verhaltensethik und Organisationskultur analysiert wird, die Mikro- mit der Mesoebene verbindet und analytisch auch die Makroebene miteinbezieht.

Drumwright and Murphy's key work points on the one hand to the neglect of producer ethics in advertising research and on the other hand to the observation that many advertising practitioners are morally too myopic to deal with questions of ethical action or mute in voicing their concerns. While theoretical and methodological approaches to researching advertising ethics are generally scarce, this is particularly emphasized for the meso and macro levels. Thus, organizational approaches to researching advertising ethics are rare, but would be warranted, as they can take into account the extent to which organizational cultures and structures influence the ethical decision-making of advertisers. The key text is characterized by analyzing advertising ethics from the perspective of behavioral ethics and organizational culture, linking the micro level with the meso level and analytically including the macro level.

This study explores the visual representation of Great Yarmouth, a British coastal town caught between the urban and the rural, as seen through the quasi-monopolistic image search engine Google Images. The research examines levels of pluralistic or biased place representations to consider how rankings employed by Google Images algorithms represent Great Yarmouth’s identity. The study adopts a visual culture perspective that recognises the role of images in place making and combines digital methods with an image type analysis to investigate how online representations reflect and create the town’s identities. The data shows that Google Images’ preference for representing Yarmouth as a sunny seaside town indicates that the search engine prioritises marketable assets above its connections with its hinterland, its diversity of people, and the cultural activities it has to offer. This, the authors state, is a place far away from Tuan’s (1979) idea of a place that is given meaning and identity from the perspective of people. Instead, Google Images’ representations of Great Yarmouth are an example of a created form of place making as commodification. The article concludes that the inscribed bias and unbalanced search priority criteria employed by the search engine impact upon the diversity of the semi-peripheral town.

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.