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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.

The consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually left an impact on the audience but the news channels have upgraded and providing various news services online on social media websites. The present study aims to study the type of news videos uploaded by the top five Hindi TV news channels on their YouTube channels with an aim to see which type of videos spark interest for YouTube viewers.

By applying the techniques of content analysis, sentiment analysis and text mining the study aims to measure the average sentiments, top words and the trend of selected popular terms in the comments on uploaded news videos by the top five Hindi news channels over a period of one year.

Results of the study indicate that the news channels are uploading more news videos about crime and investigation, politics, health and protests while uploading fewer news videos covering travel, science and technology, and religion. While the viewers of the participating news channels are more interested in giving their thoughts or opinions in the form of comments on news videos concerning crime, politics, protests and health or that these videos inspire conversation on YouTube.

The findings might be of interest to content managers of news channels to understand the interest of their audience.

The study's distinctiveness resides in the approach utilised to collect data and analyse the results in order to better understand the online behaviour of news channel audiences.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0007

The purpose of this study is to examine, in the context of online news use, the predictive values of two factors: perceived bias in traditional media and preference for partisan news.

This study used data collected as part of the Pew Internet and American Life Project between December 28, 2009, and January 19, 2010. The data were analyzed using linear regression analysis.

The findings provide evidence of the values of two potentially significant predictors of online news use: a perception of bias in traditional media and preference for partisan news. In addition, higher levels of political partisanship were shown to intensify the positive effect of perceived bias in traditional media on online news use in new media outlets, reinforcing the impact of preference for partisan news on participatory online news use.

Depending on individual decisions, the internet can either help to empower deliberative democracy (where diverse and different voices coexist) or lead to an extremely polarized society.

With the explosive growth of the internet as a news source, media scholars have explored the factors that encourage people to rely on the internet for news and information. Nevertheless, certain attributes of online news consumption originating from individual attitudes about and perceptions of the media environment remain underspecified. This research helps advance an understanding of the types of people who seek news online and how they use various sources.

Purpose – Although scholars have long been interested in how social movements use mass media to forward their goals, sociological research almost exclusively focuses on the ability of activist groups to get their ideas and organizations in general audience, mainstream media coverage. This paper contributes to a more systematic understanding of media coverage outcomes by broadening the range of outlets considered relevant to political discourse. In addition to mainstream venues, we consider conservative and liberal/left outlets in our analysis of social movement organization media coverage.

Method – Using negative binomial regression, we analyze how organizational characteristics, organizational frames, political elites, and event type affect the rates of social movement organization media coverage in mainstream and partisan news venues.

Findings – We find that the independent variables play very different roles in mainstream and partisan media coverage outcomes. Specifically, while organizational characteristics and frames often enhance the media coverage outcomes of activist groups in mainstream venues, political elites have no effect at all. In contrast, organizational characteristics and frames do not affect social movement media coverage in partisan outlets, whereas political elites and event type do.

Originality of the paper – Conceptually, this research broadens how scholars think about the relationship between social movement groups and mass media as well as the factors that influence media outcomes.

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller.

Topic sentiment analysis is a text analysis method that estimates the polarity of sentiments across units of text within large text corpora (Lin & He, 2009; Mei, Ling, Wondra, Su, & Zhai, 2007).

We apply topic sentiment analysis to public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller. Based on studies that depict contemporary news media as an “outrage industry” that incentivizes media personalities to be controversial and polarizing (Berry & Sobieraj, 2014), we predict that high-profile commentators will be more polarizing than other news personalities and topics.

Results of the topic sentiment analysis support this prediction and in so doing provide partial validation of the application of topic sentiment analysis to online opinion.

In light of the phenomenon of blogging in the 2004 US presidential campaign, this article aims to examine blogging during the 2005 British general election campaign. The article seeks to establish how widespread blogging was, the extent of bloggers' partisanship, what issues blogs were concerned with, what the purpose of the messages posted by the bloggers were, and what if any impact blogs had beyond the immediate community of users.

These questions were addressed through a content analysis of over 300 blogs and 1,300 posted messages, by additional searches of the Lexis‐Nexis newspaper archive, and via the use of internet poll data on blogging.

The research showed that blogging in the UK is in its infancy. There were few bloggers posting messages about the campaign and few people accessed blogs regularly. Most of the comments posted focused on campaign issues and were observational. While some of the bloggers exhibited an overt partisan identity, most did not. There was also some evidence of partisan networks, with blogs of a particular partisan/ideological position hyperlinking to others with a similar outlook. However, unlike the 2004 US presidential election campaign, blogs had no impact on the campaign news agenda.

Despite extensive research it is difficult to ascertain whether the sample of blogs examined in the research represented the entirety of the election campaign blogosphere. Nevertheless, the findings provide important insights into the evolving online election communication environment in the UK.

The study is the first to examine systematically the blogging about the general election campaigns in the UK.

Since the end of 2016, “fake news” has had a clear meaning in the USA. After years of scholarship attempting to define “fake news” and where it fits among the larger schema of media hoaxing and deception, popular culture and even academic studies converged following the 2016 US presidential election to define “fake news” in drastically new ways. The paper aims to discuss these issues.

In light of the recent elections in the USA, many fear “fake news” that have gradually become a powerful and sinister force, both in the news media environment as well as in the fair and free elections. The scenario draws into questions how the general public interacts with such outlets, and to what extent and in which ways individual responsibility should govern the interactions with social media.

Fake news is a growing threat to democratic elections in the USA and other democracies by relentless targeting of hyper-partisan views, which play to the fears and prejudices of people, in order to influence their voting plans and their behavior.

Essentially, “fake news” is changing and even distorting how political campaigns are run, ultimately calling into question legitimacy of elections, elected officials and governments. Scholarship has increasingly confirmed social media as an enabler of “fake news,” and continues to project its potentially negative impact on democracy, furthering the already existing practices of partisan selective exposure, as well as heightening the need for individual responsibility.

Under which conditions do social movements receive extensive attention from the mainstream news media? We develop an institutional mediation model that argues that combinations of the news-heightening characteristics of movements, including their disruptive capacities, organizational resources, and political orientation, and political contexts, including partisan regimes and benefiting from national policies, bring extensive attention to movements. It also holds that investigations will draw extensive media attention to movements, and those that have achieved prominence in the news will remain prominent under specific conditions. We appraise these combinational arguments by examining 29 social movements across 100 years in four national newspapers using qualitative comparative analysis (QCA). Researchers typically use QCA to study the consequences of movements when they hypothesize outcomes to result from multiple combinations of conditions. This raises our second main question: How should scholars best address combinational hypotheses using QCA? Here we employ Venn diagrams to identify and illustrate key analytical issues and anomalies, including constrained diversity in observational data, empirical instances when combinations of conditions do not produce the expected outcome, and instances when unexpected combinations of conditions produce a consistent result. We also demonstrate the value of broad comparisons across movements and over time in these analyses.