Illuminating an Ecosystem of Partisan Websites

This paper aims to shed light on alternative news media ecosystems that are believed to have influenced opinions and beliefs by false and/or biased news reporting during the 2016 US Presidential Elections. We examine a large, professionally curated list of 668 hyper-partisan websites and their corresponding Facebook pages, and identify key characteristics that mediate the traffic flow within this ecosystem. We uncover a pattern of new websites being established in the run up to the elections, and abandoned after. Such websites form an ecosystem, creating links from one website to another, and by `liking' each others' Facebook pages. These practices are highly effective in directing user traffic internally within the ecosystem in a highly partisan manner, with right-leaning sites linking to and liking other right-leaning sites and similarly left-leaning sites linking to other sites on the left, thus forming a filter bubble amongst news producers similar to the filter bubble which has been widely observed among consumers of partisan news. Whereas there is activity along both left- and right-leaning sites, right-leaning sites are more evolved, accounting for a disproportionate number of abandoned websites and partisan internal links. We also examine demographic characteristics of consumers of hyper-partisan news and find that some of the more populous demographic groups in the US tend to be consumers of more right-leaning sites.Comment: Published at The Web Conference 2018 (WWW 2018). Please cite the WWW versio

There can be only one truth: Ideological segregation and online news communities in Ukraine

The paper examines ideological segregation among Ukrainian users in online environments, using as a case study partisan news communities on Vkontakte, the largest online platform in post-communist states. Its findings suggest that despite their insignificant numbers, partisan news communities attract substantial attention from Ukrainian users and can encourage the formation of isolated ideological cliques – or ‘echo chambers’ – that increase societal polarisation. The paper also investigates factors that predict users’ interest in partisan content and establishes that the region of residence is the key predictor of selective consumption of pro-Ukrainian or pro-Russian partisan news content

A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 ( n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research

Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy

We address an important gap in detection of political bias in news articles. Previous works that perform supervised document classification can be biased towards the writing style of each news outlet, leading to overfitting and limited generalizability. Our approach overcomes this limitation by considering both the sentence-level semantics and the document-level rhetorical structure, resulting in a more robust and style-agnostic approach to detecting political bias in news articles. We introduce a novel multi-head hierarchical attention model that effectively encodes the structure of long documents through a diverse ensemble of attention heads. While journalism follows a formalized rhetorical structure, the writing style may vary by news outlet. We demonstrate that our method overcomes this domain dependency and outperforms previous approaches for robustness and accuracy. Further analysis demonstrates the ability of our model to capture the discourse structures commonly used in the journalism domain.Comment: Preprint. Under revie

Taking a stand on not taking a stand: media bias in the online reporting of COVID-19

Thesis (M.A.) University of Alaska Fairbanks, 2021This thesis was written to examine the digital communication strategies of three major news organizations when reporting on COVID-19 in the U.S. for bias. The research looked at social media posts, online article counts and themes, main websites of each organization and audio/visual broadcast segments from all three organizations posted online. This research used an advocacy approach, examining the tension between entertainment and journalism ethics by holding news organizations to journalism standards to see how they compare. Results showed that NPR and Fox News produced more online articles than MSNBC and linked to their own articles on twitter more. The audiovisual content from MSNBC and Fox News did not follow the code of ethics created by the Society of Professional Journalists. All three organizations used biased methods for providing information to the public, during a time period where public knowledge is key to managing a pandemic

Domestic terrorism or political protest? : Partisan cable news framing of the January 6 attack on the U.S. Capitol

The attack on the U.S. Capitol on January 6, 2021, was a historical event that received widespread media attention in the days and weeks that followed. This study focuses on the differential framing techniques used by Fox News and CNN, specifically, in their coverage of January 6. Additionally, this study addresses the differential framing techniques used across different shows on the same network: “commentary-based” shows and “information-based” shows. In doing so, this research builds upon the vast body of pre-existing news media framing research. This study finds that the differences in framing are more pronounced between Fox News and CNN than across the different shows on each network, thus providing an explanation for why Americans are so polarized about the events of January 6. Notably, Fox News highlights the peaceful aspects of January 6, labeling it a protest, whereas CNN stresses the idea of January 6 as an act of domestic terror. On a less significant level, the commentary-based shows utilized different framing techniques from their information-based counterparts. The commentary-based shows presented their audiences with a more emotional depiction of the news that more heavily relied on the anchor’s opinion

Political polarisation on social media in different national contexts

The present dissertation examines the phenomenon of political polarisation on social media. Specifically, the dissertation addresses the question of how the intensity of polarisation and the ideological lines along which it occurs might vary between different national contexts. First, it explores the differences in the intensity of political polarisation on Twitter in 16 democratic countries (Article 1). Second, it examines the ideological lines along which polarisation occurs in two non-Western contexts, specifically among Russian (Article 2) and Ukrainian (Article 3) users of Vkontakte – a social media platform popular among users from post-Soviet states. The dissertation demonstrates that the levels of political polarisation differ dramatically between countries. In democracies, polarisation tends to be lowest in multi-party systems with proportional electoral rules (e.g., Sweden), and the highest in pluralist two-party systems (e.g., United States). It also shows that, in non-democratic non- Western contexts, polarisation does not necessarily run along the left–right spectrum or party system lines. In authoritarian regimes or those with less stable party systems, polarisation runs along the lines of other issues that are more politically relevant in a given context. In Russia, polarisation manifests itself along pro-regime vs anti-regimes lines, whereas in Ukraine, polarisation happens around geopolitical issues. Polarisation on social media thus tends to reflect existing political cleavages and their intensity, in line with the theory of political parallelism. The major implication of this dissertation in the context of research into polarisation on social media is that findings on the topic from single-country studies that come from Western democratic contexts should be interpreted with caution, as they are not necessarily generalisable. To make generalisable inferences about the relationship between social media and political polarisation, more comparative studies are needed, as well as studies that take into account platform affordances and the causal mechanisms that might drive polarisation