Media Systems in the Digital Age: An Empirical Comparison of 30 Countries

Media systems have changed significantly as a result of the development of information technologies. However, typologies of media systems that incorporate aspects of digitalization are rare. This study fills this gap by identifying, operationalizing, and measuring indicators of media systems in the digital age. We build on previous work, extend it with new indicators that reflect changing conditions (such as online news use), and include media freedom indicators. We include 30 countries in our study and use cluster analysis to identify three clusters of media systems. Two of these clusters correspond to the media system models described by Hallin and Mancini, namely the democratic-corporatist and the polarized-pluralist model. However, the liberal model as described by Hallin and Mancini has vanished; instead, we find empirical evidence of a new cluster that we call “hybrid”: it is positioned in between the poles of the media-supportive democratic-corporatist and the polarized-pluralist clusters.

Chinese state media Facebook ads are linked to changes in news coverage of China worldwide

We studied the relationship between Facebook advertisements from Chinese state media on the global media environment by examining the link between advertisements and online news coverage of China by other countries. We found that countries that see a large increase in views of Facebook advertisement from Chinese state media also see news coverage of China become more positive. News coverage also becomes more likely to use keywords that suggest a point of view favorable to China. One possible explanation is that by drawing greater attention to the issues emphasized by Chinese state media, the advertisements help Chinese state media set the news agenda covered by other media sources.

EXPRESS: How Does the Adoption of Ad Blockers Affect News Consumption?

Ad blockers allow users to browse websites without viewing ads. Online news publishers that rely on advertising income tend to perceive users' adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads—which users presumably dislike—may affect users' online news consumption behavior in positive ways. Using 3.1 million visits from 79,856 registered users on a news website, this research finds that ad blocker adoption has robust positive effects on the quantity and variety of articles users consume. Specifically, ad blocker adoption increases the number of articles that users read by 21.5%-43.3%, and it increases the number of content categories that users consume by 13.4%-29.1%. These effects are stronger for less-experienced users. The increase in news consumption stems from increases in repeat visits to the news website, rather than in the number of page impressions per visit. These post-adoption visits tend to start from direct navigation to the news website, rather than from referral sources. The authors discuss how news publishers could benefit from these findings, including exploring revenue models that consider users' desire to avoid ads.

Recommending News in Traditional Media Companies

The adoption of recommender systems in online news personalization has made it possible to tailor the news stream to the individual interests of each reader. Previous research on commercial recommender systems has emphasized their use in large-scale media houses and technology companies, and real-world experiments indicate substantial improvements of click rates and user satisfaction. It is less understood how smaller media houses are coping with this new technology, how the technology affects their business models, their editorial processes, and their news production in general. Here we report on the experiences from numerous Scandinavian media houses that have experimented with various recommender strategies and streamlined their news production to provide personalized news experiences. In addition to influencing the content and style of news stories and the working environment of journalists, the news recommender systems have been part of a profound digital transformation of the whole media industry. Interestingly, many media houses have found it undesirable to automate the entire recommendation process and look for approaches that combine automatic recommendations with editorial choices.

‘Without white people, the animals will go!’: COVID-19 and the struggle for the future of South African conservation

This article examines the potential for online activism to contest hegemonic neoliberal conservation models in South Africa, using the Covid-19 crisis as a window onto discursive struggle. National lockdown measures during the pandemic sent the vital tourism sector of an already fragile economy into deep crisis. Neoliberal and militarized conservation models, with their reliance on international travel, are examined as affected by a conjunctural crisis, the meaning of which was contested by a broad range of social actors in traditional and on social media. In 30 online news videos, racial hierarchies of land ownership and conservation labour geographies are reproduced and legitimated, as is a visual vocabulary of conservation as equivalent with guns, boots, and anti-poaching patrols. Here, hope is represented as residing in the increased privatization of public goods, and the extraction of value from these goods in the form of elite, luxury consumption. In a corpus of posts on Twitter corpus, on the other hand, significant counter-hegemonic resistance to established neoliberal conservation models is in evidence. In their replies to white celebrity conservationist Kevin Pietersen, critical South African Twitter users offer a contrasting vision of hope grounded in anti-racist equality, a rejection of any special human-animal relations enjoyed by Europeans, and an articulation of a future with land justice at its centre. The analysis supports the idea that in the “interregnum” between hegemonic social orders, pathways towards transformed futures may be glimpsed as “kernels of truth” in discursive struggles on social media.

Predicting the impact of online news articles – is information necessary?

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features – ranging from the semantic relations through readability measures to the article’s digital content – are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

CRITICAL DISCOURSE ANALYSIS OF THE NEWS OF COVID-19 CORPSE SNATCHING PATIENT IN THE I-NEWS

The diction used in the news of corpse snatching of COVID-19 varies and has caused the public to panic. This study aims to show the structure of the media language used in The News of Corpse Snatching of COVID-19 patients in Pasuruan and the factors that caused the hundreds of people attempting to take the deceased's body forcefully. The primary data are the news of corpse snathing of COVID-19 patients in Pasuruan, uploaded on YouTube and the online news media i-News, and comments from netizens in the comments column. In addition, informant interviews were conducted to show the factors causing Corpse Snatching. Critical discourse analysis (CDA) is used for content analysis by describing three dimensions: text, discursive practice, and social practice. It was concluded that the media language used in the news text of the corpse Snatching in Pasuruan tends to use vocabulary that shows negative rather than positive actions. Moreover, the media emphasizes negative actions more than describing solution actions to become government policy steps. Based on informants and studies of the third dimension, hundreds of people who conducted the Corpse Snatching were caused because people hardly accept COVID-19 protocols since they hold Kejawen Islamic funeral traditions.

Improving Classification Efficiency of Fake News using Semi-Supervised Method

Online News media which is more accessible, cheaper, and faster to consume, is also of questionable quality as there is less moderation. Anybody with a computing device and internet connection can take part in creating, contributing, and spreading news in online portals. Social media has intensified the problem further. Due to the high volume, velocity, and veracity, online news content is beyond traditional moderation, also known as moderation through human experts. So different machine learning method is being tested and used to spot fake news. One of the main challenges for fake-news classification is getting labeled instances for this high volume of real-time data. In this study, we examined how semi-supervised machine learning can help to decrease the need for labeled instances with an acceptable drop of accuracy. The accuracy difference between the supervised classifier and the semi-supervised classifier is around 0.05 while using only five percent of label instances of the supervised classifier. We tested with logistic regression, SVM, and random forest classifier to prove our hypothesis.