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Partisan news media, which often publish extremely biased, one-sided or even false news, are gaining popularity world-wide and represent a major societal issue. Due to a growing number of such media, a need for automatic detection approaches is of high demand. Automatic detection relies on various indicators (e.g. content characteristics) to identify new partisan media candidates and to predict their level of partisanship. The aim of the research is to investigate to a deeper extent whether it would be appropriate to rely on the hyperlinks as possible indicators for better automatic partisan news media detection.

The authors utilized hyperlink network analysis to study the hyperlinks of partisan and mainstream media. The dataset involved the hyperlinks of 18 mainstream media and 15 partisan media in Slovakia and Czech Republic. More than 171 million domain pairs of inbound and outbound hyperlinks of selected online news media were collected with Ahrefs tool, analyzed and visualized with Gephi software. Additionally, 300 articles covering COVID-19 from both types of media were selected for content analysis of hyperlinks to verify the reliability of quantitative analysis and to provide more detailed analysis.

The authors conclude that hyperlinks are reliable indicators of media affinity and linking patterns could contribute to partisan news detection. The authors found out that especially the incoming links with dofollow attribute to news websites are reliable indicators for assessing the type of media, as partisan media rarely receive links with dofollow attribute from mainstream media. The outgoing links are not such reliable indicators as both mainstream and partisan media link to mainstream sources similarly.

In contrast to the extensive amount of research aiming at fake news detection within a piece of text or multimedia content (e.g. news articles, social media posts), the authors shift to characterization of the whole news media. In addition, the authors did a geographical shift from more researched US-based media to so far under-researched European context, particularly Central Europe. The results and conclusions can serve as a guide how to derive new features for an automatic detection of possibly partisan news media by means of artificial intelligence (AI).

The peer review history for this article is available at the following link: https://publons.com/publon/10.1108/OIR-10-2020-0441.

This study aims to create an idea and a framework to enhance customer stickiness and improve transformation efficiency flow of tourism products from online to offline platforms through the application of personalized recommendation technology.

Studies on an overview of progress in current personalized recommendation research, business scenario analysis of online tourism and some possible logical limitations discussion are required for improvement. This study clarifies concepts including online tourism user behavior and generated data, user preference themes and spaces, user models and image and user-product (two-dimensional matrix, etc.). The author then creates a user portrait based on behavior data convergence to locate the user's role from both horizontal and vertical dimensions and also clear the logical levels and associations among them, verifying the similarity in measurement and calculation and optimizing the implementation of the personalized recommendation program under online tourism business scenarios.

By providing a framework design about personalized recommendations of online tourism including a flow from data collection to a personalized recommendation algorithm selection, logical analysis is established while the corresponding personalization algorithm is improved.

This study show a logical shift of personalized recommendations in online tourism management from focusing on the simple collection of travel information and the logical speculation of tourism products to focusing on the individual behavior of potential travelers.