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The Psychology of Why the Right and the Left Believe in Media Bias
In September 2011, Pew released the latest in its annual "Views of the News Media" survey, showing that Democrats have moved closer to Republicans in their dissatisfaction with the performance of the news media. Across questions asked, of particular note were the trends above that showed that about 2/3 of partisans of all stripes believe that news stories are often inaccurate.
This follows a dip for Democrats in 2007-08 when a press strongly critical of President Bush and Republicans combined with favorable election trends likely provided the grist for a more positive estimate of the media's performance. Of relevance, perceptions of political bias among Democrats follow a similar trend line, dipping in 2007-08 and then rising again in 2011, though about half of Democrats in 2011 view the media as biased (54%) compared to about 3/4 of Republicans (76%).
At the Washington Post, in a April 27 article, media reporter Paul Farhi offered an excellent overview of the factors that might be driving perceptions of media bias among the public. Below is an elaboration on several causes that Farhi highlights:
This last cause is a common finding in the literature across studies and topics. As I have written with my colleague John Besley in a recent study, this psychological tendency even likely accounts for why elite groups like scientists hold a pervasive belief in media bias, despite a mainstream media that typically covers science in strongly favorable terms.
Here's how my AU colleague Lauren Feldman and I explained the process driving this "hostile media phenomena" in a recent book chapter on the social psychology of political communication, drawing in part on some of Feldman's own work in the area:
Across national settings, there is an ever pervasive belief in various forms of media bias. In the U.S., over the past two decades, the dominant belief regarding media bias is that that the mainstream news media favor liberal causes and political candidates. Yet, when researchers conduct content analyses to search for systematic patterns of partisan bias in coverage of elections, across studies they are unable to find definitive evidence (D'Alessio D. & Allen, 2000). If social scientists using the best tools available to them find it difficult to observe hard evidence of liberal bias, why are beliefs among the public so widespread? Moreover, across country setting and issue, what explains the difference between subjective perceptions of media bias and objective indicators relative to coverage?
In research on perceptions of the news media, credibility is understood as a subjective assessment, influenced by the partisan or ideological background of the audience and the claims about bias that might emanate from trusted sources such as political commentators or like-minded friends. In the U.S. context, these claims are typically focused on a liberal bias charged by conservative elites and reinforce a widespread belief among conservative-leaning audiences (Watts, Domke, Shah, & Fan, 1999). Audiences, then, do not typically assess story content on its own merits but rather on the basis of preconceived notions about the news media – often stemming from journalists’ tendency in many stories to cover and reflect on their own potential liberal bias. A number of other studies have also suggested that individuals’ expectations for bias in a news source or in the media, more generally, are likely to influence their perceptions of bias in news coverage (Arpan & Raney, 2003; Baum & Gussin, 2007).
Perhaps the most crucial determinant of perceptions of bias in the news, however, is the extent to which news coverage is seen as disagreeing with one’s own views. Individuals who feel most strongly about an issue tend to see their own side’s views as being more a product of objective analysis and normative concerns, and less influenced by ideology, than the other side’s views (Robinson, Keltner, Ward, & Ross, 1995). This human tendency translates directly to judgments about the media. In a range of studies, when news audiences who hew to opposing sides on an issue are given the same news coverage of the topic to evaluate, both view this identical coverage as biased in favor of the other side (Gunther & Schmitt, 2004; Vallone et al., 1985). The phenomenon is commonly referred to as the “hostile media effect.” Researchers believe that the explanation for this hostile media effect is selective categorization: opposing partisans attend to, process, and recall identical content from a news presentation but mentally categorize and label the same aspects of a story differently – as hostile to their own position (Schmitt, Gunther, & Liebhart, 2004).
The original hostile media effect assumes that news coverage is inherently balanced. The relative hostile media perception (Gunther, Christen, Liebhart, & Chia, 2001) relaxes this assumption, making it applicable to news that is slanted in favor of or against a particular issue. In the presence of the relative hostile media effect, supporters and opponents of a given issue perceive bias in a consistent direction (i.e., leaning toward one side), but each group perceives coverage as significantly more unfavorable to their own position relative to those in the other group. In other words, partisans perceive lessbias in news coverage slanted to support their view than their opponents on the other side of the issue.
Interestingly, then, whereas the implication of the original hostile media effect is a partisan public perceiving media bias where none was present and thus potentially rejecting useful information, the implications of the relative hostile media effect are somewhat different. Of consequence here is that partisans will fail to recognize bias in news that is in fact biased, in instances when that bias is congruent with their pre-existing views. This bias against news bias is troubling. Americans’ trust in news sources has become deeply polarized in recent years – with Republicans, for example, attributing more credibility to the conservative Fox News and less to most other news organizations than Democrats (Pew Research Center, 2008). In other countries, similar perceptions of a left or right bias to news or alternatively a bias relative to national or ethnic identity exist.
In each context, as news – particularly on cable TV and online – is infused with increasing amounts of opinion and ideology, this may make it even easier for partisans to validate their personal political beliefs – by accepting at face value information that comports with their views while rejecting information that advocates for the other side. Thus, the relative hostile media effect may not only reflect partisan divides in news perceptions but may also contribute to the further polarization of political attitudes and knowledge across political systems.
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
"You dream about these kinds of moments when you're a kid," said lead paleontologist David Schmidt.
- The triceratops skull was first discovered in 2019, but was excavated over the summer of 2020.
- It was discovered in the South Dakota Badlands, an area where the Triceratops roamed some 66 million years ago.
- Studying dinosaurs helps scientists better understand the evolution of all life on Earth.
Credit: David Schmidt / Westminster College<p style="margin-left: 20px;">"We had to be really careful," Schmidt told St. Louis Public Radio. "We couldn't disturb anything at all, because at that point, it was under law enforcement investigation. They were telling us, 'Don't even make footprints,' and I was thinking, 'How are we supposed to do that?'"</p><p>Another difficulty was the mammoth size of the skull: about 7 feet long and more than 3,000 pounds. (For context, the largest triceratops skull ever unearthed was about <a href="https://www.tandfonline.com/doi/abs/10.1080/02724634.2010.483632" target="_blank">8.2 feet long</a>.) The skull of Schmidt's dinosaur was likely a <em>Triceratops prorsus, </em>one of two species of triceratops that roamed what's now North America about 66 million years ago.</p>
Credit: David Schmidt / Westminster College<p>The triceratops was an herbivore, but it was also a favorite meal of the T<em>yrannosaurus rex</em>. That probably explains why the Dakotas contain many scattered triceratops bone fragments, and, less commonly, complete bones and skulls. In summer 2019, for example, a separate team on a dig in North Dakota made <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">headlines</a> after unearthing a complete triceratops skull that measured five feet in length.</p><p>Michael Kjelland, a biology professor who participated in that excavation, said digging up the dinosaur was like completing a "multi-piece, 3-D jigsaw puzzle" that required "engineering that rivaled SpaceX," he jokingly told the <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">New York Times</a>.</p>
Morrison Formation in Colorado
James St. John via Flickr
|Credit: Nobu Tamura/Wikimedia Commons|
The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.
Can computers do calculations in multiple universes? Scientists are working on it. Step into the world of quantum computing.
- While today's computers—referred to as classical computers—continue to become more and more powerful, there is a ceiling to their advancement due to the physical limits of the materials used to make them. Quantum computing allows physicists and researchers to exponentially increase computation power, harnessing potential parallel realities to do so.
- Quantum computer chips are astoundingly small, about the size of a fingernail. Scientists have to not only build the computer itself but also the ultra-protected environment in which they operate. Total isolation is required to eliminate vibrations and other external influences on synchronized atoms; if the atoms become 'decoherent' the quantum computer cannot function.
- "You need to create a very quiet, clean, cold environment for these chips to work in," says quantum computing expert Vern Brownell. The coldest temperature possible in physics is -273.15 degrees C. The rooms required for quantum computing are -273.14 degrees C, which is 150 times colder than outer space. It is complex and mind-boggling work, but the potential for computation that harnesses the power of parallel universes is worth the chase.