Polarized platforms? How partisanship shapes perceptions of “algorithmic news bias”

The use of artificial intelligence-based algorithms for the curation of news content by social media platforms like Facebook and Twitter has upended the gatekeeping role long held by traditional news outlets. This has caused some US policymakers to argue that platforms are skewing news diets against them, and such claims are beginning to take hold among some voters. In a nationally representative survey experiment, we explore whether traditional models of media bias perceptions extend to beliefs about algorithmic news bias. We find that partisan cues effectively shape individuals’ attitudes about algorithmic news bias but have asymmetrical effects. Specifically, whereas in-group directional partisan cues stimulate bias perceptions for members of both parties, Democrats, but not Republicans, also respond to out-group cues. We conclude with a discussion about the implications for the formation of attitudes about new technologies and the potential for polarization.

Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions

Computational modelling of political discourse tasks has become an increasingly important area of research in natural language processing. Populist rhetoric has risen across the political sphere in recent years; however, computational approaches to it have been scarce due to its complex nature. In this paper, we present the new Us vs. Them dataset, consisting of 6861 Reddit comments annotated for populist attitudes and the first large-scale computational models of this phenomenon. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.

The Influence of News Brand Cues and Story Content on Citizen Perceptions of News Bias

Mainstream U.S. news media stand accused of bias against the forty-fifth president, Donald Trump. The relentlessness and intensity of these accusations over the course of Trump’s presidency are unusual and make for an opportunity to study perceptions of news bias. During the experiment reported here, participants ( N = 315) were exposed to biased (pro- and anti-Trump) news stories that were attributed to either CNN, Breitbart, or remained unattributed to a news brand. After reading the stories, participants rated the stories for their relative slantedness in favor of, neutral, or against the president. Findings reveal that news users are sensitized to the president’s accusations of bias against CNN. For example, anti-Trump stories were rated as more slanted than pro-Trump stories when they were attributed to CNN. This was not the case when the same stories were attributed to Breitbart. Interestingly, unattributed biased news received the highest ratings for slantedness.

Media Bias Effects on Voters in Pakistan

The news media play a significant role in shaping political opinions and party choices of voters as most of the people learn about politics through media. The study investigated the influence of television news channels' biases over the voting behavior of the electorates. The survey method was used as a tool for data collection to determine the relationship between media bias and its influence on voting behavior. Partisan views are exposed when news channels give one side of the political spectrum a distinct advantage through subjective reporting. News channels include cable television news stations operating in Pakistan. The study findings suggest that voters do rely on television news channels for information during election campaigns. The study found that television news bias has a strong influence on people's voting behaviors and election outcomes.

Determination of news biasedness using content sentiment analysis algorithm

<span lang="EN-US">Nowadays, i</span><span lang="EN-US">dentifying news biases in the social media is one of the most fundamental problems. News bias is a complex process that comprises several dimensions to be taken into account and it is interlinked with social, political and economic problems. In general, news bias has the ability to reflect opinion of people about a topic or government policies and actions. The proposed algorithm develops a system which can detect the biasedness of news topics from different news Websites.</span><span>This approach automatically collects the news contents from various online news media portals and then consolidates them for the determination of news biasedness. </span><span lang="EN-US">In the experimental study, the news topics are gathered from various Websites of U.S., U.K., and India. For training dataset 3265 news sentences were collected under various news topics from 20 different news Websites. The effectiveness of classification of algorithm is proved by the extensive experimental study. The proposed algorithm provides a method improves the determination of news biasedness, which in turn may help in providing impartial, unbiased and reliable information.</span>

Introduction to Series: Informing Science Perspectives on Fake News

Aim/Purpose: This series of papers on Fake News: Bias, Misinformation, and Disinformation examines fake news from an Informing Science perspective. As such, the papers in this special series make novel con-tributions to the field by viewing the issues through the transdisciplinary lens of informing science. This series makes no claim to summarize or review all that has been written on this topic. Rather it provides a glimpse into this immense literature from the perspective of informing science. Background: It is one small step on the 20+ year quest by the editor to explore better ways to inform from an approach that transcends academic disciplines (Cohen, 1998, 1999) and a 20 year quest to under-stand the issues of how we become misinformed and disinformed (Cohen, 2000). The series pro-vided here gains thrust for two reasons. One reason is that the study has become more popular with academicians due to the blathering of politicians and the attacks by national powers on de-mocracy. The second reason is more mundane; without the deadline that the end-of-year affords, the papers would become richer, fuller, and more detailed. Recommendation for Researchers: Taken together, the results brought forth across these papers is truly scary. Due to their biases, when presented with information, people can and do generate their own misinformation. People tend to communicate such misinformation that they self-generated with others in groups sharing their beliefs, strengthening the misinformation by some and silencing those do not share these thoughts. This process creates divisions in society. How can humanity seek wise decisions when we cannot agree even upon the facts. We see the results of this syndrome in Operation SIG and cur-rent divisions within politics in the West.