News Ecosystem Research Articles

Gatekeeping in a Digital Media Habitat: The Role of Secondary Gatekeepers

This study analyzed mission statements of mainstream news media in the United States and surveyed news consumers to investigate gatekeeping practices in a contemporary news ecosystem. Data show that news organizations expressed commitment to independence, truth-telling, and crafting news based on readers’ interests. However, the mission statements never mentioned any form of citizens’ active engagement in news creation. Looking into the citizens’ perspective, a survey of 280 participants shows that they primarily expect news organizations to provide opportunities for them to post comments on news and share stories, which is the most common form of “secondary gatekeeping.” Other forms of “secondary gatekeeping,” the practice that allows users to share information others might not be able to see otherwise were less likely to occur, partly because of limitations posed by news organizations as they strive to preserve their authority over information.

Orchestrator Bypass: The Emergence of Informal Collaboration in the Danish News Ecosystem

Orchestrator Bypass: The Emergence of Informal Collaboration in the Danish News Ecosystem

Online news ecosystem dynamics: supply, demand, diffusion, and the role of disinformation

The digital age provides new challenges as information travels more quickly in a system of increasing complexity. But it also offers new opportunities, as we can track and study the system more efficiently. Several studies individually addressed different digital tracks, focusing on specific aspects like disinformation production or content-sharing dynamics. In this work, we propose to study the news ecosystem as an information market by analysing three main metrics: Supply, Demand, and Diffusion of information. Working on a dataset relative to Italy from December 2019 to August 2020, we validate the choice of the metrics, proving their static and dynamic relations, and their potential in describing the whole system. We demonstrate that these metrics have specific equilibrium relative levels. We reveal the strategic role of Demand in leading a non-trivial network of causal relations. We show how disinformation news Supply and Diffusion seem to cluster among different social media platforms. Disinformation also appears to be closer to information Demand than the general news Supply and Diffusion, implying a potential danger to the health of the public debate. Finally, we prove that the share of disinformation in the Supply and Diffusion of news has a significant linear relation with the gap between Demand and Supply/Diffusion of news from all sources. This finding allows for a real-time assessment of disinformation share in the system. It also gives a glimpse of the potential future developments in the modelisation of the news ecosystem as an information market studied through its main drivers.

Before the Environment Was News: Outdoor Writers and the Boundaries of Journalism

Outdoor writing is a little respected and often forgotten genre of American newsmaking. Widely popular in the early to middle twentieth century, outdoor journalists presented a blend of factuality with personal narrative and advocacy for environmental preservation as they told stories of fishing, hunting, and the outdoors more broadly in columns tucked away in the sports section. Outdoor columns were the single most consistent source of environmental newsmaking in US newspapers until the late 1960s, when news organizations started assigning journalists working in the news section to the environmental beat. But because outdoor columnists violated norms of twentieth century newsmaking by including advocacy and personal narrative in their columns, their contributions have been overlooked. This study, which draws on the organizational archives of the Outdoor Writers Association of America, demonstrates that outdoor writers were deeply engaged with the concept of “news” even from their position as delegitimized actors within the news ecosystem. As a result, an historic account of the outdoor writing profession provides a fruitful site of inquiry to examine norms of journalism, boundaries of the field, legitimacy, and ultimately power.

AI GENERATIVE CHATBOT IN THE MEDIA: JOURNALISTIC COVERAGE OF CHATGPT IN BOSNIA AND HERZEGOVINA

In diffusion research, journalistic coverage is acknowledged as a significant factor in spreading awareness and fostering knowledge about innovation, potentially accelerating or impeding the adoption process. With regards to AI-related innovations, this dynamic has largely been studied within the context of Western developed countries. There is far less understanding of how this process unfolds in the news ecosystem of post-communist countries, particularly those with lower democratic standards and weaker economic development, such as Bosnia and Herzegovina. With the intention of gaining preliminary insights, this study investigated how the journalistic organizations in Bosnia and Herzegovina covered the emergence and societal adoption of ChatGPT, a novel form of generative AI, during the initial six-month period following its widespread availability. The content analysis of relevant news messages (N=542) published by 40 legacy and digital- only news outlets was used to explore the key characteristics of journalistic coverage, the attention given to the issue over time and the media depictions of this innovative AI technology. Results indicate that a small group of news outlets, predominantly legacy news organizations, provided significantly more content on ChatGPT than others, particularly public broadcasting services. Findings highlight a tendency among news outlets to focus on either the risks or benefits of ChatGPT and similar AI-based products and amplify sources associated with the business sector and high-tech industry, overrepresented by male voices.

Bloglator in the era of social media: a case study of the reports about the ‘tragically ugly’ math textbooks on What’s on Weibo

ABSTRACT While extensive research exists on blogs, news-related blog translation has been relatively unexplored. This study delves into the realm of news-related blog translation through the case of What’s on Weibo (hereafter WoW), an independent English-language news-related website that reports trending topics on Weibo. Given the unique nature of blog translation, the term bloglator is proposed to refer to a blog translator. By analyzing the reports related to the controversial Chinese primary school mathematics textbooks, this paper examines WoW’s criteria for content selection, the characteristics of the reports, and the role a bloglator plays within the news ecosystem. The research findings reveal that there are both similarities and differences between a bloglator and a journalator, and a bloglator may employ a variety of translation methods and assume an important role in the news ecosystem in this era of social media.

News ecosystems: tackling unfinished business

News ecosystems: tackling unfinished business

Audiences, automation, and AI: From structured news to language models

AbstractThe appearance of large language models (LLMs) and other forms of generative AI portend a new era of disruption and innovation for the news industry, this time focused on the production and consumption of news rather than on its distribution. Large news organizations, however, may be surprisingly well‐prepared for at least some of this disruption because of earlier innovation work on automating workflows for personalized content and formats using structured techniques. This article reviews this work and uses examples from the British Broadcasting Corporation (BBC) and other large news providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI. Using the BBC's next‐generation authoring and publishing stack as an example, the article also discusses how earlier innovation work has influenced the design of flexible infrastructure that can accommodate uncertainty in audience behavior and editorial workflows – capabilities that are likely to be well suited to the fast‐approaching AI‐mediated news ecosystem.

News platformization in Portugal: analysis of the dependence of the news media on social media and search engines

The research aims to understand the news platformization process based on the identification and analysis of the dependence that Portuguese news media have on social media and search engines in the process of distributed discovery of news. The method replicated the dependency analysis model proposed by Nielsen & Fletcher (2002) and Tambini & Labo (2016), selecting the 30 Portuguese journalistic media with the highest average volume of web traffic between July, August, and September 2022. Thus, the different types of access were identified with the SimilarWeb platform, used in the literature as a reference source in webometrics. The data showed that 19 news media have more than 50% of indirect traffic dependent on the platforms. As for the total volume, we found that, on average, 56.26% of news access originates from social media or search engines; 36.05% from direct access; 7.05% from reference links; and 0.31% from email, sponsored search, and advertising. Considering mobile devices, the average dependency increases to 57.35% and, on desktop devices, it drops to 50.05%. We found that there is variation according to the type of media and its content: those specializing in football have greater direct access, and those specializing in lifestyle and celebrities have greater indirect access. Digital native media also rely more on platforms, as do media coming from Radio and TV. We conclude that platformization is a relevant phenomenon in the news ecosystem in Portugal, with significant integration of journalistic media content in these digital infrastructures. Thus, media will have to find ways to counterbalance the power of platforms, which, at the same time, improve connection with the user and increase audience reach, control data, interaction patterns, as well as online advertising, operating in a logic of increased dependency.

Comparative Analysis of Machine Learning Algorithms for Detecting Fake News: Efficacy and Accuracy in the Modern Information Ecosystem

In an era where the spread of fake news poses a significant threat to the integrity of the information landscape, the need for effective detection tools is paramount. This study evaluates the efficacy of three machine learning algorithms—Multinomial Naive Bayes, Passive Aggressive Classifier, and Logistic Regression—in distinguishing fake news from genuine articles. Leveraging a balanced dataset, meticulously processed and vectorized through Term Frequency-Inverse Document Frequency (TF-IDF), we subjected each algorithm to a rigorous classification process. The algorithms were evaluated on metrics such as precision, recall, and F1-score, with the Passive Aggressive Classifier outperforming others, achieving a remarkable 0.99 in both precision and recall. Logistic Regression followed with an accuracy of 0.98, while Multinomial Naive Bayes displayed robust recall at 1.00 but lower precision at 0.91, resulting in an accuracy of 0.95. These metrics underscored the nuanced capabilities of each algorithm in correctly identifying fake and real news, with the Passive Aggressive Classifier demonstrating superior balance in performance. The study's findings highlight the potential of employing machine learning techniques in the fight against fake news, with the Passive Aggressive Classifier showing promise due to its high accuracy and balanced precision-recall trade-off. These insights contribute to the ongoing efforts in digital media to develop advanced, ethical, and accurate tools for maintaining information veracity. Future research should continue to refine these models, ensuring their applicability in diverse and evolving news ecosystems.

Re-humanising News: Delineating the Trends and Research Scope for Curatorial Journalism

The pivot to online news in the last two decades has had far-reaching changes in the way information is created, disseminated and consumed. One aspect of this transformation to digital has been the salience of curatorial journalism – a human-centric practice involving selection of newsworthy content, its organisation and presentation in consumable styles and formats. News curation has been pitched as an antidote to an ecosystem of information overload. While research into the practice online exists in certain contexts, it has been sparse in countries such as India. This paper reviews and synthesises contemporary literature pertaining journalistic practice and roles, news curation and its formats, digital intermediaries and structures of the digital journalism ecosystem. It thematically suggests three approaches that could be adopted by researchers to fill the gaps in the discourse around news curation : the role and identity-centric approach; format and style-centric approach; and, business and economy-centric approach. While the role and identity-centric approach emphasises on how many journalists today work as ‘information “managers”’, the format and style-centric approach delves into how curatorial products may adopt a conversationalised style to engage audiences. The question of the value proposition of curatorial products in a precarious environment is dealt with by the third approach. By placing the discussion of these approaches in the context of four curatorial offerings in the English language Indian news ecosystem, the paper highlights the vast potential of research that exists for a diverse country like India. But at the same time, these approaches would be applicable to other contexts as well, where extant research on curation has been found lacking.

Digital news ecology and polarization in the context of deep learning: empowerment, entrenchment and reconciling failure

Abstract This paper aims to discuss the causes of public opinion polarization in digital news ecology and then analyze group polarization’s specific manifestations and carriers. Secondly, it introduces word embedding technology to analyze digital news text, utilizes a multi-head self-attention mechanism to construct a new classification model, and realizes collaborative filtering recommendations of digital news based on users’ interests and news hotspots. Finally, social media is taken as an example to analyze the factors that cause polarization in digital news ecology. The results show that the digital news ecosystem can empower the development of the polarization phenomenon, deeply involve users, and group users by solidifying their identity construction, social endorsement and other directions. It can also provide a space for the polarization phenomenon to be reconciled, but due to the emotional loss of control and the decline of publicity leading to the failure of reconciliation, the similarity of sadness emotion reconciliation is 36.94%. It is suggested that the digital news ecology provides technical support for developing polarization phenomena, enabling them to thrive in digital news.

Joint Credibility Estimation of News, User, and Publisher via Role-relational Graph Convolutional Networks

The presence of fake news on online social media is overwhelming and is responsible for having impacted several aspects of people’s lives, from health to politics, the economy, and response to natural disasters. Although significant effort has been made to mitigate fake news spread, current research focuses on single aspects of the problem, such as detecting fake news spreaders and classifying stories as either factual or fake. In this article, we propose a new method to exploit inter-relationships between stories, sources, and final users and integrate prior knowledge of these three entities to jointly estimate the credibility degree of each entity involved in the news ecosystem. Specifically, we develop a new graph convolutional network, namely, Role-Relational Graph Convolutional Networks (Role-RGCN), to learn, for each node type (or role), a unique node representation space and jointly connect the different representation spaces with edge relations. To test our proposed approach, we conducted an experimental evaluation on the state-of-the-art FakeNewsNet-Politifact dataset and a new dataset with ground truth on news credibility degrees we collected. Experimental results show a superior performance of our Role-RGCN proposed method at predicting the credibility degree of stories, sources, and users compared to state-of-the-art approaches and other baselines.

Introduction to AI journalism: Framework and ontology of the trans-domain field for integrating AI into journalism

Artificial Intelligence (AI) is being introduced to news routines. There is a need for theoretical framework to instruct the AI-adopting process and better integrate the cross-domain knowledge. This article attempts to explicate the major transformations AI brings to news systems, i.e., automated writing and personalization at scale. In light of the transformations, the tension between AI and human journalists has drawn attention. In order to unravel the accompanied conundrums, we propound a new notion – AI journalism, which is a trans-domain field of various categories, deriving from journalism automation. We propose to build AI journalism ontology that can manifest the emerging phenomenon in news ecosystem where intelligent machines and human journalists are coexisting. This article’s ultimate contribution is proffering a new framework and a transdisciplinary field to examine the integration of AI and associated technologies into journalism.

Examining Journalist’s Perception of Fake News and Their Attitude toward Debunking disinformation

Fake news and other forms of disinformation pose a serious threat to the news ecosystem and the informing of audiences, who are increasingly dependent on online sources of information. After the attention paid to the spreading of fake news, researchers have focused on the study of the negative effects that different typologies of disinformation are prone to having on audiences. Similarly, great attention has also been paid to the motives that users have for the spreading of fake news.These studies, which combine various disciplines, attempt to analyze the psychological factors and motives that lead users to engage with online fake news. On the other hand, several studies have analyzed the role of platforms and their algorithmic logic, as well as the main approaches for addressing this significant problem. Although much effort has been devoted to the phenomenon of disinformation in social media, the role of professional journalists in exposing false information has not been given the necessary attention.Albania is a country with a high rate of fake news and conspiracy theories. This study will investigate the Albanian journalists’ perception of fake news and the risk in them being used as a means of (dis)informing the audience. It will also focus on how this perception affects their behavior and willingness to debunk disinformation on social media. Making use of a national-level questionnaire, to which 270 journalists contributing in the Albanian media responded, the study aims to answer some important questions about the role of journalists in addressing disinformation issues in the social media space. The findings from this study indicate that although journalists in Albania perceive fake news as a danger to democracy, the media and the journalism profession, they do not seem motivated to engage in the debunking actions and exposing of fake news circulating online.

Bursts of contemporaneous publication among high- and low-credibility online information providers

In studies of misinformation, the distinction between high- and low-credibility publishers is fundamental. However, there is much that we do not know about the relationship between the subject matter and timing of content produced by the two types of publishers. By analyzing the content of several million unique articles published over 28 months, we show that high- and low-credibility publishers operate in distinct news ecosystems. Bursts of news coverage generated by the two types of publishers tend to cover different subject matter at different times, even though fluctuations in their overall news production tend to be highly correlated. Regardless of the mechanism, temporally convergent coverage among low-credibility publishers has troubling implications for American news consumers.

Asymmetric ideological segregation in exposure to political news on Facebook.

Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta's Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook's news ecosystem than those favored by liberals.

Hey Google, What is in the News? The Influence of Conversational Agents on Issue Salience

The emerging use of Conversational Agents (CAs), such as Google Assistant, highlights the role of algorithmic gatekeeping power in news consumption. However, our knowledge of the effects of CAs on shaping the public’s perception of the most important topics (issue salience) is limited. To investigate this, we conducted a seven-day longitudinal survey in the Netherlands, comparing the most important issues reported by users (N = 352) across various channels, including CAs, social media, and news websites. Our aim was to analyze how CAs influence the transfer of issue salience from the channel to the user. While our hypotheses were not supported, different patterns emerged. Users consuming news through CAs exhibited a have a lower probability of agreeing on the most important issues in the Netherlands compared to participants consuming information on social media and news websites. This suggests that news consumption through CAs may offer less shared context compared to other channels. Furthermore, trust in the channel and attention to the news were found to be significant predictors of agreement on issue salience. These findings raise important questions for further research, particularly regarding the impact and democratic role of CAs within the broader news ecosystem.

Facebook’s News Feed Algorithm and the 2020 US Election

Facebook plays a substantial role in shaping exposure to news and information, a role that is particularly significant during democratic elections. This article aims to characterize Facebook’s contribution to news exposure during the 2020 US election. Using internet browsing data from 1.3 million desktop panelists in the United States, we analyze audience flows from Facebook to news and information publishers ( N = 3,247 websites) labeled according to quality (high or low) and geography (non-local or local). We find that Facebook ranked as the fourth largest external referral source to publishers. Our analysis further shows that, among the panel of 1.3 million US adults visiting these publishers via desktop computers, Facebook disproportionately amplified low-quality publishers and extended their audience reach during the study period. We also find evidence that Facebook’s intervention to improve “news ecosystem quality” by tweaking their News Feed algorithm was associated with fewer daily visits to low-quality publishers, but seemed to have a similar effect on other publishers and, according to Facebook-provided data, had little effect on month-over-month traffic. We discuss limitations, open questions about platform impact, and directions for future work.

"A Special Operation": A Quantitative Approach to Dissecting and Comparing Different Media Ecosystems’ Coverage of the Russo-Ukrainian War

The coverage of the Russian invasion of Ukraine has varied widely between Western, Russian, and Chinese media ecosystems with propaganda, disinformation, and narrative spins present in all three. By utilizing the normalized pointwise mutual information metric, differential sentiment analysis, word2vec models, and partially labeled Dirichlet allocation, we present a quantitative analysis of the differences in coverage amongst these three news ecosystems. We find that while the Western press outlets have focused on the military and humanitarian aspects of the war, Russian media have focused on the purported justifications for the “special military operation” such as the presence in Ukraine of “bio-weapons” and “neo-nazis”, and Chinese news media have concentrated on the conflict’s diplomatic and economic consequences. Detecting the presence of several Russian disinformation narratives in the articles of several Chinese media outlets, we finally measure the degree to which Russian media has influenced Chinese coverage across Chinese outlets’ news articles, Weibo accounts, and Twitter accounts. Our analysis indicates that since the Russian invasion of Ukraine, Chinese state media outlets have increasingly cited Russian outlets as news sources and spread Russian disinformation narratives.