Hyperpartisan News Research Articles

Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter

While news sharing by ordinary social media users has received growing attention, hyper-partisan news sharing, which has been closely associated with misinformation circulation, has received less attention. In this study, we investigate hyper-partisan news sharing from two perspectives: (1) the features that make hyper-partisan news share-worthy, as well as (2) the user motivations that drive the sharing process. We scrutinize one week’s content from Infowars.com as it was shared on Twitter. Through both manual coding of news content and semi-automated clustering of Twitter account descriptions, we find that human interest and conflict in news stories drive the sharing process from a content perspective. Concerning the user perspective, we find partial support for a sharing hypothesis based on motivated reasoning, which indicates that users are more likely to share hyper-partisan news stories if these align with their own political opinions.

Publishers/sources (Disinformation)

Recent research has mainly used two approaches to identify publishers or sources of disinformation: First, alternative media are identified as potential publishers of disinformation. Second, potential publishers of disinformation are identified via fact-checking websites. Samples created using those approaches can partly overlap. However, the two approaches differ in terms of validity and comprehensiveness of the identified population. Sampling of alternative media outlets is theory-driven and allows for cross-national comparison. However, researchers face the challenge to identify misinforming content published by alternative media outlets. In contrast, fact-checked content facilitates the identification of a given disinformation population; however, fact-checker often have a publication bias focusing on a small range of (elite) actors or sources (e.g. individual blogs, hyper partisan news outlets, or politicians). In both approaches it is important to describe, compare and, if possible, assign the outlets to already existing categories in order to enable a temporal and spatial comparison.

Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines

AbstractWhy is misleading partisan content believed and shared? An influential account posits that political partisanship pervasively biases reasoning, such that engaging in analytic thinking exacerbates motivated reasoning and, in turn, the acceptance of hyperpartisan content. Alternatively, it may be that susceptibility to hyperpartisan content is explained by alackof reasoning. Across two studies using different participant pools (total N = 1,973 Americans), we had participants assess true, false, and hyperpartisan news headlines taken from social media. We found no evidence that analytic thinking was associated with judging politically consistent hyperpartisan or false headlines to be accurate and unbiased. Instead, analytic thinking was, in most cases, associated with an increased tendency to distinguish true headlines from both false and hyperpartisan headlines (and was never associated with decreased discernment). These results suggest that reasoning typically helps people differentiate between low and high quality political news, rather than facilitate belief in misleading content. Because social media play an important role in the dissemination of misinformation, we also investigated willingness to share headlines on social media. We found a similar pattern whereby analytic thinking was not generally associated with increased willingness to share hyperpartisan or false headlines. Together, these results suggest a positive role for reasoning in resisting misinformation.

Hyperpartisan News Use: Relationships with Partisanship and Cognitive and Affective Involvement

ABSTRACT This paper revisits the issue of partisan selectivity in the era of prevalent false, misleading, and biased information. We surveyed a representative sample of adults living in the United States to investigate the relationships among hyperpartisan news use, partisanship, and cognitive and affective involvement. First, we find that strong partisanship—from both Democrats and Republicans—relates to exposure to both left- and right-leaning hyperpartisan news. Second, exposure to left-leaning hyperpartisan content relates to negative affective involvement, while exposure to right-leaning hyperpartisan news relates to positive affective involvement. Neither left- nor right-leaning hyperpartisan news use relates to cognitive involvement, though traditional news use does. These findings add to the literature on partisan selective exposure and provide new details about the makeup of news audiences and their cognitive and affective involvement with hyperpartisan news.

What Drives Hyper-Partisan News Sharing: Exploring the Role of Source, Style, and Content

A growing number of hyper-partisan alternative media outlets have sprung up online to challenge mainstream journalism. However, research on news sharing in this particular media environment is lacking. Based on the virality of seventeen partisan outlets’ coverage of immigration and using the latest computational linguistic algorithm, the present study probes how hyper-partisan news sharing is related to source transparency, content styles, and moral framing. The study finds that the most shared articles reveal author names, but not necessarily other types of author information. The study uncovers a salient link between moral frames and virality. In particular, audiences are more sensitive to moral frames that emphasize authority/respect, fairness/reciprocity, and harm/care.

Hyperpartisan news: Rethinking the media for populist politics

Online media sites such as Breitbart News in the United States and The Canary in the United Kingdom have come to prominence as powerful new agents. Their reach and influence in the contemporary digital media ecology have been widely highlighted, yet there has been little scholarship to situate these important new players in the field of political communication. This article argues that, first, these ‘interlopers’ known as the ‘alt-right’ and ‘alt-left’ need to be understood as embedded in the context of populist politics. Second, ‘hyperpartisan’ describes these sites better than the framework of alternative media as it mirrors populism’s ideological pillar of ‘us’ versus ‘them’. Finally, a deliberate provocation is argued to name these digital start-ups as news to create a starting point for conceptualising these disruptive new media forces.

Populist Attitudes and Selective Exposure to Online News: A Cross-Country Analysis Combining Web Tracking and Surveys

Research has shown that citizens with populist attitudes evaluate the news media more negatively, and there is also suggestive evidence that they rely less on established news sources like the legacy press. However, due to data limitations, there is still no solid evidence whether populist citizens have skewed news diets in the contemporary high-choice digital media environment. In this paper, we rely on the selective exposure framework and investigate the relationship between populist attitudes and the consumption of various types of online news. To test our theoretical assumptions, we link 150 million Web site visits by 7,729 Internet users in France, Germany, Italy, Spain, the United Kingdom, and the United States to their responses in an online survey. This design allows us to measure media exposure more precisely than previous studies while linking these data to demographic attributes and political attitudes of participants. The results show that populist attitudes leave pronounced marks in people’s news diets, but the evidence is heterogeneous and highly contingent on the supply side of a country’s media system. Most importantly, citizens with populist attitudes visit less Web sites from the legacy press, while consuming more hyperpartisan news. Despite these tendencies, the Web tracking data show that populist citizens still primarily get their news from established sources. We discuss the implications of these results for the current state of public spheres in democracies.

Facebook News Use During the 2017 Norwegian Elections—Assessing the Influence of Hyperpartisan News

ABSTRACT The paper at hand presents a comparative study on news use in relation to media content as posted to Facebook by a series of established and hyperpartisan media outlets during the 2017 Norwegian national election campaign. Specifically, we are interested in determining what types of news emanating from what types of news outlets that result in comparably higher levels of news use—defined as levels of likes, shares and comments—on Facebook. Results indicated that with a few exceptions, established, legacy media dominate the most engaging news stories during the election campaign, while results for hyperpartisan media outlets suggests rather limited influence. Nevertheless, the hyperpartisan media outlets succeeded in making themselves visible on the platform under scrutiny, driving attention to issues such as immigration during the election campaign.

Robust Fake News Detection Over Time and Attack

In this study, we examine the impact of time on state-of-the-art news veracity classifiers. We show that, as time progresses, classification performance for both unreliable and hyper-partisan news classification slowly degrade. While this degradation does happen, it happens slower than expected, illustrating that hand-crafted, content-based features, such as style of writing, are fairly robust to changes in the news cycle. We show that this small degradation can be mitigated using online learning. Last, we examine the impact of adversarial content manipulation by malicious news producers. Specifically, we test three types of attack based on changes in the input space and data availability. We show that static models are susceptible to content manipulation attacks, but online models can recover from such attacks.

Journalism Featuring Artificial Intelligence

The article deals with mass media techniques based on the algorithms of artificial intelligence, principles of machine learning and deep learning based on neural networks, and recommenders. The authors provide an analytical review of experiences of applying these techniques in various spheres, namely, data processing and analysis, automated production of reports on current events and facts, interactive communication with audience, tracking newsworthy events, fact-checking, visual discovery, video content production, and others. The mass media techniques are demonstrated by the list of examples of Reuters News Tracer, Wordsmith, Heliograf, Perspective (an interface of applied programming), Newswhip, Quackbot, Guardian Chatbot, Wibbitz, Factmata, et al. Factmata is given special attention to as a complex approach to algorithmization of the Media that includes such methods as contextualization of statements, arguments and stories, and keeping a blacklist of domains which the algorithms mark as hateful, hyperpartisan, toxic, or fake news. The authors note that machine learning of algorithms for generating and analyzing texts is becoming easily accessible. Moreover, the new generation of algorithms based on artificial intelligence is able to identify text sentiment. The analysis of the impact of the media environment, including such factors as echo chamber and filter bubble, on information users shows that information can now be compared to a drug which is almost legal and easily available for use by any social group, and its users, due to targeting and personalization, are transforming into its «ideal consumers».

A Third Wave of Selective Exposure Research? The Challenges Posed by Hyperpartisan News on Social Media

Hyperpartisan news on social media presents new challenges for selective exposure theory. These challenges are substantial enough to usher in a new era—a third wave—of selective exposure research. In this essay, we trace the history of the first two waves of research in order to better understand the current situation. We then assess the implications of recent developments for selective exposure research.

Fighting misinformation on social media using crowdsourced judgments of news source quality

Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n = 1,010 from Mechanical Turk and n = 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content ("fake news"). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans-mostly due to distrust of mainstream sources by Republicans-every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r = 0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low- and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.

Crowdsourcing Judgments of News Source Quality

Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources which users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (N=1,010 from Mechanical Turk and N=970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: 1) mainstream media outlets, 2) hyper-partisan websites, and 3) websites that produce blatantly false content (“fake news”). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyper-partisan or fake news sources. Although this difference was larger for Democrats than Republicans – mostly due to distrust of mainstream sources by Republicans – every mainstream source (with one exception) was rated as more trustworthy than every hyper-partisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r=0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low- and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.

The Brexit Botnet and User-Generated Hyperpartisan News

In this article, we uncover a network of Twitterbots comprising 13,493 accounts that tweeted the United Kingdom European Union membership referendum, only to disappear from Twitter shortly after the ballot. We compare active users to this set of political bots with respect to temporal tweeting behavior, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs. bot-to-bot) to evidence strategies for bot deployment. Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small- to medium-sized cascades; that the retweeted content comprises user-generated hyperpartisan news, which is not strictly fake news, but whose shelf life is remarkably short; and, finally, that a botnet may be organized in specialized tiers or clusters dedicated to replicating either active users or content generated by other bots.

The Brexit Botnet and User-Generated Hyperpartisan News

In this paper we uncover a network of Twitterbots comprising 13,493 accounts that tweeted the U.K. E.U. membership referendum, only to disappear from Twitter shortly after the ballot. We compare active users to this set of political bots with respect to temporal tweeting behavior, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs. bot-to-bot) to evidence strategies for bot deployment. Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small to medium-sized cascades; that the retweeted content comprises user-generated hyperpartisan news, which is not strictly fake news, but whose shelf life is remarkably short; and, finally, that a botnet may be organized in specialized tiers or clusters dedicated to replicating either active users or content generated by other bots.