Triggering Democrats: The Link Between Fake and
Non-Mainstream News Sources, and the Backfire Effect
Benyamin Stevanovich | 11595426
Master Thesis | Political Science: Public Policy and Governance
Supervisor: Dr. Seiki Tanaka
Second Reader: Dr. Gijs Schumacher
22
ndJune 2018
1 ACKNOWLDGEMENTS
First, I would like to thank all of the individuals who answered my time-consuming survey and allowed me to analyze the data for this thesis. The valuable and interesting information collected provided the foundation for the analyses and investigation that I performed.
Thank you to my family and friends for supporting me throughout the thesis writing process, and the entirety of my studies. This accomplishment would not have been possible without them.
I am also thankful to Gijs Schumacher for taking the time to be my second reader. Last but not least, I would like to thank my supervisor, Seiki Tanaka, who supported my idea from the beginning and encouraged me to succeed. Thank you for all the helpful feedback and for the occasional joke or two when the pressure was at its greatest.
2 TABLE OF CONTENTS
1. Introduction………4
2. Literature Review………...……9
2.1 Key Definitions………..9
2.2 The Backfire Effect………10
2.3 Emotions and Information Processing………..…………10
2.4 Misperceptions and Fake News………12
2.5 Target Demographic – Republicans or Democrats? ………...……13
2.6 Fake News and Voting Behavior………...……14
3. Theoretical Framework……….……16
3.1 Central Theories………...…16
3.2 Argument, Expectations, and Mechanisms………...………18
4. Research Design………...………..……21
4.1 Variables ………...………...……21
4.2 Methodology………...….……….…………21
5. Analysis and Results………...….………..…………25
5.1 Demographics ………...….……….……….25
5.2 Control Group Results………...….………..25
5.3 Non-mainstream Non-fake Group Results………...….…………26
5.4 Fake Left-wing Group Results………...….………..27
5.5 Fake Right-wing Group Results………...….………28
5.6 Democratic Partisanship ………...….……….29
6. Backfire Effect – Mechanisms……..………...….………30
6.1 Non-mainstream Non-fake Mechanisms………...….…………....30
6.2 Fake Left-wing Mechanisms………...….………...32
6.3 Fake Right-wing Mechanisms………...….…………...33
6.4 Informed Versus Misinformed Analysis………...….………...35
6.5 Level of Misperception – Control Group Analysis………...…....36
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7. The Backfire Effect and Voting Behavior………...…………...….………...38
8. Conclusion………..………...….………...39
9. Bibliography………..………...….………...41
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1. INTRODUCTION
The current American political climate is partially defined by identity politics, partisan thinking, and the so-called “culture war” that seeks to pit differing political ideologies against one other in sometimes violent ways (Koleva et al. 2012; Hartman 2016). A key component of these phenomena is information, as it can be used by one side to reinforce its political position whilst simultaneously discrediting the opposing side’s political position. In this way, it acts as a form of political capital that can be dispensed to win an argument (Cammaerts & Audenhove 2005). Ultimately, we are dependent on news providers and aggregators to provide information that will ultimately play a role in the way we shape our opinions. News aggregators, especially, have the power to curate what information will be prominently displayed to us on the front pages of social media and mainstream news outlets.1 This study specifically aims to answer questions regarding
the way individuals process and interpret information that is derived from various news sources, differing in their reliability and origin, in conjunction with the individuals’ partisan orientation.
While the importance of information is clear, the source, and by extension, the reliability of the information is paramount when it comes to the nuances regarding the use of information in political debates. In recent years, alternatives to traditional established media (television, newspapers etc.) have become even more prevalent in environments with the advent of the Internet and the availability of social media, demonstrated by the fact that 62% of American adults get the majority of their news on social media (Gottfried and Shearer 2016). This can, on the one hand, consist of reliable and informative journalistic pieces, but on the other hand, can also consist of maliciously misleading texts that are created to support a political position rather than report factual information. An example of this can be seen in a publication made by “The Daily World Update”, a known right-wing fake news media outlet, on the 7th of May, 2018,
which stated that the South African anti-apartheid leader Nelson Mandela had applauded Donald Trump’s domestic policies (Mikkelson 2018). Putting aside the fact that Nelson Mandela passed away in 2013, the article offers no sources, no direct quotes, and uses abrasive language and rhetoric to praise Trump and discredit the previous administration under Barack Obama (Ibid).
Colloquially, these kinds of publications are known as ‘fake news’ and their role in political life is to intentionally deceive people in order to perpetuate a specific viewpoint or ideology. The intentions of fake news publishers were made apparent during the 2016 General Election in the United States where fake news itself became a controversial and widely discussed subject for its apparent role in persuading people to vote for a certain candidate with incorrect and sensationalist information (Allcott & Gentzkow 2017; Tandoc et al. 2018). In terms of numbers, fake news is overwhelmingly right-wing, with one study finding that out of the 156 fake news articles they investigated, 115 were pro-Donald Trump and were shared 30
1 News aggregators are software or web applications which aggregate web content such as online newspapers, blogs,
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million times, while the remaining 41 were pro-Hillary Clinton and were shared only 7.6 million times (Allcott & Gentzkow 2017). The controversy surrounding fake news and its impact on the 2016 Presidential Election is still referenced by Democratic voters and used to denounce Republicans for their “gullible and emotive” thinking, as Democrats argue that fake news played a key role in misleading Republican and Independent voters with sensationalist, and often incorrect information (Bessi & Ferrara 2016; Pickard 2016).
The majority of the prevailing literature on fake news is thus centered around Republican voters. Previous studies have explained this using the notion that Republicans rationalize their choices using more emotive reasoning and less logical reasoning, and that Republicans think more negatively and pessimistically than Democrats (Dodd et al. 2012; Block & Block 2006). As a result of these concepts and their relation to fake news, Republicans are also at the center of studies that investigate what is known as the “backfire effect”. This phenomenon occurs when an individual is presented with information that contradicts or undermines their preconceived opinions and beliefs, otherwise known as a corrective text. In response to this new, contradictory information, individuals will often reject this, even if it is factually correct, and instead reinforce their belief in their original opinion (Taber & Lodge 2006; Weeks 2015). Seeing as this phenomenon interacts with information processing mechanisms within an individual and is exacerbated amongst more emotive and spiritual individuals, Republicans are one of the prime demographics for study in this field (Ibid, Ibid).
The discussion above, therefore, alludes that unlike Republicans, Democrats tend to be less prone to this phenomenon. Some research has indeed suggested that more liberal individuals experience the backfire effect to a limited extent (Nyhan & Reifler 2010), but there is little theoretical and empirical work about the backfire effect and its relation to fake news on Democrats in the current literature. That is why this study will focus on Democratic voters in hopes of better understanding to what extent, and to what degree, the backfire effect is experienced by individuals who are left-of-center on the political spectrum, which would directly contribute to the existing knowledge regarding partisanship and information processing (Taber & Lodge 2006; Guess et al. 2018). I believe Democrats should also experience the backfire effect because they too are human beings with political aversions and preferences, and thus, should experience the emotional mechanisms at play and react to certain triggers in a similar way to Republicans.
Furthermore, the Democratic party has its own internal political partisanship spectrum, with more progressive left-wing Democrats on one end and more conservative centrist Democrats on the other end. Their varying degrees of partisanship will influence the way they treat news articles of varying origin, for example, non-mainstream versus establishment media, and the way they process the information contained within those articles (Farrell et al. 2010). That is why this study will divide the Democrat sample into two subcategories, namely ‘leftist’ and ‘centrist’ Democrats. It is important to distinguish between the two types
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of Democrats because they are likely to experience backfire at different rates due to their varying political ideologies that come ingrained with certain attitudes towards the media, logic, and political values (Ibid). It also gives us an opportunity to clarify the mechanisms of the backfire effect and connect them to partisanship and ideology.
Taking these factors into account this thesis examines the following research question:
“To what extent do fake news corrections to already existing common misperceptions trigger the backfire effect in both ‘leftist’ and ‘centrist’ Democratic voters?”
To answer the research question, I argue that both types of Democrats will experience the backfire effect due to the nature of the misperceptions and information presented in the news articles that will be used in the experiment. The participants should experience strong emotions during the experiment, and that coupled with the ingrained preferences for certain kinds of media should evoke a significant backfire effect. Additionally, the participants exposed to fake right-wing news should experience the backfire effect at the most consistent rates due to the addition of fake news that not only possesses incorrect information, but is also of an opposite partisan orientation.
As such, I have developed two hypotheses to guide my research:
H1: Both ‘leftist’ and ‘centrist’ Democratic voters will experience the backfire effect.
H2: Fake-right news corrections will most consistently trigger the backfire effect in both “leftist” and “centrist” Democratic voters.
With the research question and hypotheses in mind, I have created an experimental setup that will be able to accurately investigate the backfire effect amongst Democratic voters and see to what extent fake news corrections play a role in that process. The experimental setup for backfire effect research involves participants reading a text that reinforces an already existing prevalent misperception in society. After, the participants read a second text that provides the factual correction to the misperception, otherwise known as a corrective text. The origin and nature of the corrective text in these experiments should play a key central role in the way individuals react to them. This is because the origin and nature of the texts can play on current societal issues, such as the validity of news, the normative standards for journalistic pieces, and what news outlets can and cannot be trust. These factors ultimately influence peoples’ reactions towards information they perceive as correct or incorrect. Previous studies have not focused on this aspect of the experimental setup and it is where this study can contribute to the existing literature by exploring the way individuals interact to contradictory information in difference news sources, and how that impacts the way they process the information within the texts.
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More specifically, this study distinguishes three types of relevant texts, namely: non-mainstream but factually sound news, fake left-wing news, and fake right-wing news. These three texts were chosen because, according to the literature, they will evoke a backfire effect amongst Democratic voters, as their reputations and partisan leanings will influence the way the individuals perceive the information within the article. Hence, the central expectations of this study are as follows: the non-mainstream article should trigger a backfire effect but not as intensely as the fake news variant, due to the somewhat legitimate perception of the news outlet. Specifically, centrist Democrats should experience higher rates of backfire as they are likely more comfortable with established news outlets, and hence would find the non-mainstream variant more untrustworthy. Conversely, leftist Democrats should view the non-non-mainstream variant more favorably as the information contained within them is more left-wing in nature, according to international news outlet rankings (AllSides 2018; Wormald 2014). Next, the fake news variants, both left and right, should trigger higher rates of backfire in Democratic voters as both types of Democrats are expected to have an aversion to factually incorrect information. Some mechanisms at work here are expected to be the feeling of being undermined by a malicious news sources, or the feelings of embarrassment and stubbornness when it comes to reading information that directly undermines one’s previous beliefs. These distinctions allow for a more thorough analysis of how fake news interacts with individuals with varying liberal political opinions, which has important implications for both academics and society as a whole.
This thesis aims to improve on previous studies and differentiates itself in three distinct ways. First, rather than taking a macroscale approach, this thesis focuses on the individual and seeks to find a unique connection between fake news and the backfire effect. This is done in order to clarify the sources of the psychological phenomenon. Secondly, not only will the link between fake news and the backfire effect be established, but the causal mechanisms that define the process will be uncovered and explained. While there has been research done on the backfire effect itself, the mechanisms that underlie it are unclear. By using Democratic voters as a target group, the possibility of revealing more fine grain mechanisms emerges (Guess et al. 2018; Allcott & Gentzkow 2017). Thirdly, this thesis hopes to uncover more knowledge on liberal Democratic voters and their reactions to fake news and the degree to which they experience the backfire effect. Seeing as fake news is shown to be overwhelmingly biased towards conservative ideology, there is surprisingly little research into its impact on the leftwing majority in the U.S. (Gallup, Inc 2016). As a final justification for this study and the demographic I chose to investigate, the majority of the predominant literature on the subject has been conducted on Americans, and more specifically, on Republicans. This study, therefore, aims to contribute to the existing literature regarding this specific region, demographic, and political climate in order to create a clearer image of the way politically engaged Americans experience the backfire effect.
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The main findings of this thesis are that Democratic voters, regardless of partisan identity within the Democratic party, experience the backfire effect to a moderate extent. In all three experimental groups, the corrective text failed to correct the misperception held by the participants, which lead them to reject the corrective text and agree with the statement perpetuating the misperception. Additionally, the type of text used as the corrective text mattered and altered the way individuals reacted to the information in the articles. This is seen by the fact that the fake left-wing and fake-right experimental groups experienced the backfire effect to a larger extent than the non-mainstream non-fake group.
This thesis has also concluded that the mechanisms underlying the backfire effect in this experiment were feelings of distrust, annoyance, aggravation, and stubbornness that the participants experienced. The participants reported that based on their prior knowledge and knowledge of the news outlet, they felt as though the corrective texts were trying to deceive them, ultimately evoking the emotions mentioned above.
The implications of this study are twofold. First, this study hopes to contribute academically to the emerging literature on the impacts of fake news and the way individuals react to it. The relatively new nature of this topic means that this study can begin to explore how partisanship in individuals interacts with the way they read and process information in fake news articles, the emotions it evokes, and the way it influences them politically. In this way, this study hopes to encourage other scholars to further explore the relatively unknown impacts of fake news. Second, the prominence of fake news and the amount of exposure it receives has significant ramifications for mainstream news outlets, normative political practices, and the way we inform ourselves. By understanding how fake news is received by individuals of varying partisan orientation, further steps can be taken to prevent its dissemination, create countermeasures, and develop better ways to inform the public. Furthermore, the better the conditions for the backfire effect are understood, the more effective credible media institutions will become in information transmission, hopefully leading to a more fact-based society.
Next, the literature review will examine the current literature on the backfire effect, its subsequent components and enablers, and fake news. This will be done to identify where this study will contribute to the existing knowledge on the subject. Then my research design and theoretical framework will be outlined, followed by analysis of the results. Finally, I will conclude my findings and fill the existing knowledge gap on the subject.
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2. LITERATURE REVIEW 2.1 Key Definitions
In order to conduct this investigation, a few key terms need to be defined, namely misperception, ignorance, fake news, and of course, the backfire effect. The first of which is the term “misperception”. The predominant definition for “misperception” in the literature is “cases in which peoples’ beliefs about factual matters are not supported by clear evidence and expert opinion” (Nyhan & Reifler 2010). I use this definition because, if placed in a political context, it distinguishes between individuals who are politically active but hold unsupported political opinions and individuals who are politically ignorant and apathetic.
Similarly, I have chosen to place the concept of “ignorance” in a political context and define it in the following way: “Those who lack fundamental factual knowledge regarding current or contemporary political structures, themes, or issues”. This definition is used by Martin Gilens in his study on political ignorance and is widely accepted as a contemporary definition for “ignorance” in a political context (Gilens 2001). When an individual is politically ignorant, they generally possess little knowledge and interest in topical political phenomena and therefore, ignorance is one of the lowest tiers of political knowledge. Now, misperception differs in this respect as some individuals can possess the fundamental knowledge of political life in their country, whilst simultaneously holding incorrect beliefs regarding certain political phenomena. As a result, they believe they are well informed but possess factually incorrect information and are hence misinformed.
A significant perpetuator of common misperceptions are so-called “fake news” articles that are becoming ever more prevalent on the media landscape of the internet (Guess et al. 2018; Allcott & Gentzkow 2017). In today’s political climate, the term “fake news” has become a buzzword that has gained a substantial political connotation. Contemporary discourse appears to define “fake news” as fictitious and often outrageous reports that emulate established news outlets in their style, making them appear more professional and trustworthy than they are (Tandoc et al. 2018). However, the predominant definition of “fake news” in the literature is “news that is intentionally and verifiably false and created to intentionally mislead readers” (Allcott & Gentzkow 2017). The differences between the two definitions predominantly lie in the degree to which they emphasize the malicious nature of the fake news report and how it seeks to undermine facts to spread a misperception. I chose to work with the latter as I believe that the majority of fake news articles are created to serve a political purpose that is achieved by maliciously and intentionally spreading misinformation that misleads readers (Tandoc et al. 2018; Vosoughi et al. 2018; Guess et al. 2018).
The core of this thesis is to see to what extent fake news articles elicit a backfire effect amongst Democratic voters, and as such, the term “backfire effect” also must be defined. The “backfire effect” is a concept termed by Brendan Nyhan and Jason Reifler in their 2010 study on misperceptions and corrective
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texts (Nyhan & Reifler 2010). This term describes the effect that can occur when an individual is presented with evidence that contradicts their beliefs, but ultimately rejects this new evidence, which leads them to reinforce their original belief (Ibid). The next section will outline the predominate literature on the backfire effect and investigate the individual components that ultimately enable the phenomenon to exist.
2.2 The Backfire Effect
From what we know of the backfire effect, much of it is rooted in the preconceptions individuals hold regarding their political beliefs (Taber & Lodge 2006; Weeks 2015). Studies have shown that most people struggle to adopt an objective viewpoint when processing new information, and instead often rely on their own previously ingrained attitudes and beliefs to form a new judgement. As a result, arguments that align well with one’s prior convictions are evaluated as stronger than those that do not (Taber & Lodge 2006). This can help explain why individuals experience the backfire effect, as when they receive a piece of information that diverges from their predispositions, they view it as a weak argument due to the contradictory nature of the two pieces of information.
Similarly, a further study has found that not only do individuals struggle to process contradictory information objectively, but they in fact seek out information that already aligns with their political viewpoints (Flynn et al. 2017; Fridkin et al. 2015). This is known as directionally motivated reasoning, and it entails that individuals have political goals and seek out information that reinforces those goals, and ultimately view information that aligns with their ideas as more convincing than those that do not (Flynn et al. 2017; Taber & Lodge 2006). This provides another divergent reason why the backfire effect should be present when individuals are presented with contradictory information.
2.3 Emotions and Information Processing
In order to understand the intricacies of the backfire effect, one must understand how the emotional state of an individual interacts with their information processing mechanisms. This is because the backfire effect is believed to be rooted in an emotional reaction that occurs when an individual is presented with information that undermines their views (Nyhan & Reifler 2010; Gadarian 2014). Previous studies have shown that the emotional characteristics exhibited by preschool children can act as strong indicators of political ideology in the future. One particular study found that most individuals who were characterized as “developing close relationships, self-reliant, energetic, somewhat dominating, relatively under-controlled, and resilient” in preschool possessed relatively liberal political opinions. Conversely, those that were characterized as “easily victimized, easily offended, indecisive, fearful, rigid, inhibited, relatively over-controlled, and vulnerable” possessed relatively conservative political opinions later in life (Block & Block
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2006). These character traits ultimately stem from emotions that the individual possesses, clarifying the link between emotions and partisanship.
Brian Weeks built upon the literature surrounding emotions and partisanship and demonstrated that two emotions in particular are central to the way individuals react to information and form political opinions; namely anger and anxiety (Weeks 2015). Here, politically misinformed participants were asked to write a text about something that made them angry or anxious, and in this way, their mental state was altered to reflect those emotions. They were then given a text that amended their preconceptions – a corrective text, or correction. What the study found was that the anxiety-induced participants became increasingly analytical and careful when reviewing the new information, because in order to deal with anxiety, individuals generally take action against the impending threat by rigorously considering their opinions, even if the information contradicts their preestablished viewpoints (Weeks 2015; Marcus et al. 2011). This is supported by a further study that found that anxious individuals seek out more information about the issue causing them anxiety, but are ultimately more focused on remembering and processing threatening information (Gadarian 2014).
Conversely, the anger-induced participants became increasingly partisan and more susceptible to misinformation that is consistent with their party identity, because in order to deal with anger, individuals generally become defensive and dismiss contradictory information that challenges their established viewpoints. This occurs because in the defensive anger-fueled state, the participants retreated into their own pervasive ideologies and social identities that brought them reassurance and comfort, naturally raising partisan tendencies. (Weeks 2015; Marcus et al. 2011). Here, we can see how fake news could interact with certain emotions and the corrective elements that are later introduced and alter the way individuals perceive and process information. This is important for research into the backfire effect as emotions, and the way they influence information processing mechanisms, are key processes for this phenomenon.
Finally, regarding emotion, preconceptions, and political orientation, a link has been made to the previous literature that suggests that people of varying political orientations react differently to certain visual stimuli (Dodd et al. 2012). An experiment was conducted where participants were instructed to follow a visual stimulus with their eyes across a blank canvas that was either coded as ‘pleasing’ or ‘averse’. The movement of their eyes was then recorded. The study found that individuals categorized as ‘right-of-center’ directed more of their attention to the ‘aversive’ stimulus and that individuals categorized as ‘left-of-center’ directed more of their attention to the ‘pleasing’ stimulus (Ibid). The former category of individuals also reported that they felt fear and anxiety when following the ‘aversive’ stimulus. While it is clear that emotions play a pivotal role in the ‘backfire effect’ the fine grain mechanisms at work are relatively unknown, and it is where this study aims to contribute to the existing literature.
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The backfire effect is a complex psychological phenomenon that has multiple mechanisms and triggers that enable its existence. One of these mechanisms is the extent to which an individual is informed or misinformed. Misperceptions are prevalent across the world, but in the U.S., there is one common misperceptions that plays a role in political life, namely wealth distribution. Americans vastly underestimate the levels of wealth inequality, with the average American underestimating the figure by 25% (Norton & Ariely 2011). Furthermore, a study that analyzed the levels of misperception in multiple Western countries found that the U.S. was the only country sampled that underestimated the levels of disparity regarding wealth inequality (Hauser 2017).
Ultimately, misperception can be described as the gap between the world as it actually exists and the world as it exists in the mind of the individual. This statement is premised on the notion that there is a single objective reality and multiple subjective realities. The difference between these realities is the perception of the individual and the errors in reality they introduce (Vertzberger 1990).
But from where do these errors originate? A leading theory suggests that local political elites play an important role in either providing information directly to individuals or by endorsing or denouncing others that seek to provide information (Lupia & McCubbins 1998). In the U.S., citizens are more likely to disagree with expert opinion when a local political elite they favor denounces that opinion, regardless of the validity of the information the political elite provides to do so (Darmofal 2005). However, this phenomenon works in tandem with the individuals’ level of knowledge and existing policy preferences, which means that the extent to which an individual is misinformed plays a large role in this process. This can be extrapolated to the backfire effect as an individuals’ preferred political elites’ actions and the extent to which they themselves are misinformed will ultimately play a role in the backfire effect itself.
Alongside local elites exists a second theory that suggests that ideological media encourages and propagates inaccurate beliefs (Garrett et al. 2016). Within this category falls fake news, as it is generally founded upon a specific political ideology and seeks to perpetuate ideas and philosophies that are in line with said ideology (Allcott & Gentzkow 2017; Tandoc et al. 2018). The prevalence of fake news in our current media landscape is a growing issue that is becoming increasingly important in today’s political climate. Their impact on political processes has also not gone unnoticed, as studies have shown that fake news contributes to the widespread promotion of misperceptions and the undermining of public debate and discourse (Guess et al. 2018; Allcott & Gentzkow 2017; Garrett et al. 2013; Weeks 2015). Furthermore, it has been found that the main propagators of fake news are individual humans, as opposed to robots or organized fake news disseminators, and that fake news is more likely to be shared due to the more intense emotions it evokes in its readers compared to mainstream news articles (Vosoughi et al. 2018). This has interesting implications regarding how individuals react to certain information and how that is further
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propagated online. As a result, there has been substantial research done on the prevalence, origin, and consequences of fake news on various political phenomena (Nyhan & Reifler 2010; Vosoughi et al. 2018; Gottfried and Shearer 2016). While there has been extensive research done on fake news and its potential consequences, there still exists a gap in the literature regarding fake news and its impact on the individual, as well as its contribution to the backfire effect.
2.5 Target Demographic – Republicans or Democrats?
Up until now, this thesis has examined the literature outlining the role emotions play in information processing mechanisms and how that could be linked to the backfire effect. Especially relevant are the negative emotions of anxiety and anger as they are the emotions most closely linked to the backfire effect (Weeks 2015; Gadarian 2014). This thesis has also covered the literature surrounding the pervasiveness of misperceptions in the U.S., and how fake news is partially responsible for its prevalence; linking everything back to the backfire effect.
However, what is still missing is the variable of partisanship and how it influences the extent to which an individual can experience the backfire effect. This is what Nyhan and Reifler partially investigated, and in their study, participants were divided according to their partisan leanings and were given news articles that contained common misperceptions regarding the U.S. war effort in Iraq in 2008. They then introduced corrective texts to the participants and observed whether, and to what extent, they experienced the backfire effect (Nyhan & Reifler 2010). What they found was that liberals welcomed the corrective information that both reinforced their beliefs and contradicted their beliefs. Conservatives, on the other hand, rejected the corrections as they undermined their conservative beliefs and instead supported their original misinformed view even more strongly (Ibid). The gap in their experiment, that they themselves acknowledge, is that there is little research done on the extent to which Democrats experience the ‘backfire effect’, as even their own study focused overwhelmingly on Republicans.
The focus on Republican voters stems from the likelihood of finding positive results within that demographic, as generally more spiritual and emotive individuals are likely to experience the backfire effect to a larger extent (Taber & Lodge 2006; Weeks 2015). But this leaves a significant gap in the literature, as there exists little plausible reason for Democrats to not experience a similar phenomenon. Democrats are still human beings that interact with news and domestic politics in an almost identical environment to their Republican counterparts, and as such, they should experience the backfire effect to a similar extent.
This begs the question: Are Republicans simply more dogmatic, or is there a further explanation that can be uncovered? Despite the fact that the two demographics are significantly different and that it is believed that Democrats do not experience the backfire effect to the same extent as Republicans, this study will hopefully provide an opportunity to uncover the mechanisms underlying the backfire effect. Seeing as
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the predominate studies have already investigated the relationship between Republican voters and the backfire effect, this thesis will focus exclusively on Democratic voters and not include Republicans in the study itself (Nyhan & Reifler 2010; Guess et al. 2018; Weeks 2015). Therefore, the sole focus of the study will be to uncover the mechanisms and processes behind the backfire effect that exist on the left side of the political spectrum and contribute to the existing literature on Democratic voters.
This literature review has covered the backfire effect and its subsequent components and mechanisms that facilitate its occurrence. The final section that will be discussed is the potential link between fake news and voting behavior. This is because we have seen how fake news can elicit a strong emotional response due to its divisive nature, and the controversy surrounding fake news’ role in the 2016 U.S. General Election is predicated on that notion (Allcott & Gentzkow 2017; Pickard 2016).
2.6 Fake News and Voting Behavior
The prevalence of fake news in the 2016 U.S. General Election is one of the most popular focal points of the research regarding misinformation and fake news, as the concept has been picked up social scientists to investigate the way people perceive and respond to political misinformation (Werner 2016; Allcott & Gentzkow 2017). Allcott & Gentzkow’s research focused on the rates of fake news consumption during the campaign season in the U.S. and found that the fake news was overwhelmingly weighted in favor of Donald Trump. The article also indirectly mentions that fake news could not have been a deciding element in the 2016 General Election by comparing it to the effectiveness of television ad campaigns during the same period (Allcott & Gentzkow 2017). However, the study was not geared towards this realm of study, nor did it perform the necessary methodological tasks to accurately draw that conclusion. As a result, the link between fake news and voting patterns is still unclear.
Further research was conducted on the subject when studies began to investigate online user data collected from various online sources. For example, one study found that 25% of Americans visited a fake news website during the final month of the Presidential campaign, and that the websites observed were overwhelmingly conservative and pro-Trump (Guess et al. 2018). They also found that fake news consumers, especially those that voted for Trump in the General Election, were less likely to respond positively to fact-checking mechanisms. While the article does not explicitly mention it, it is implied that these individuals would experience stronger backfire effects when presented with corrections given their fake news consumption (Ibid). This observation is important; however, it does contain a knowledge gap. Namely, how does the inverse of this process work? How do individuals, mainly Democrats, respond to fake news articles when used as a corrective cue? This is where the research presented in this thesis can build on existing knowledge and further clarify the link between fake news, voting patterns, and the backfire effect.
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As was alluded to above, fake news has the power to evoke specific emotions in individuals, which often translate to political action, a shift in preference, or the backfire effect (Werner 2016; Weeks 2015). The texts used to correct misperceptions are, therefore, crucial to the processes. A specific measure for the effectiveness of a corrective text is the level of trust an individual possesses towards a specific politician. Hannah Werner’s study focused on the effects of fake news and misinformation on the level of trust individuals possessed towards political candidates in the 2016 elections. She found that individuals were not more likely to disregard corrective information about their preferred candidate, and instead exhibited higher levels of distrust towards them (Werner 2016). The fact that an individual can experiences heightened levels of distrust towards their preferred political candidate due to a corrective text implies that fake news has a strong possibility to be corrected. However, the article stops short of explaining the causal links between voter trust and voter patterns, and clashes with other literature on the issue, potentially signaling the need for more research on the matter (Nyhan & Reifler 2010; Guess et al. 2018).
Thee predominate literature on the backfire effect, its subsequent components, and fake news and its relation to voting behavior has been covered. Now, this thesis will outline the theoretical framework it will be operating under. Additionally, the central argument of this thesis will be presented.
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3. THEORETICAL FRAMEWORK 3.1 Central Theories
One of the central theories supporting the backfire effect is emotions and the emotional state of the individual. Emotions are central in the way individuals perceive, absorb, and react to certain pieces of information. Specific emotions, such as anger and anxiety, have been shown to play a more central role in the processing of and response to political information (Weeks 2015; Marcus et al. 2011). Emotions directly connect to the backfire effect because the phenomenon is, at its core, an emotive and reactionary response to information that is perceived as hostile or contradictory to one’s preconceptions (Taber & Lodge 2006; Nyhan & Reifler 2010). When an individual is presented with information that undermines their previous beliefs, they feel a range of emotions, from anger to embarrassment, that ultimately can trigger the backfire effect. That is why it is important to understand the role of emotions in the processing of political information, as it plays a crucial role in some psychological processes, such as the backfire effect.
Next, we have the theories that discuss the role of partisanship and the extent to which an individual experiences the backfire effect. Previous studies have found that Republicans, in general, tend to focus on negative stimuli that in turn trigger negative emotions. In contrast, Democrats tend to focus on positive stimuli that in turn trigger positive emotions (Dodd et al. 2012). Furthermore, Republicans, on average, are more spiritual than their Democratic counterparts, which mean they are more likely to argue with emotion and spirituality than logic and reason (Taber & Lodge 2006; Weeks 2015). It is because of studies like these that the majority of the research done on the backfire effect has focused predominantly on Republicans, as the prevalence and scale of the backfire effect is likely to be higher due to their more negative emotive nature and higher levels of spirituality. This is because the backfire effect is a fairly negative response, as it entails the individual disregarding incoming information as a knee-jerk reaction in favor of preexisting views. As a result, this would make Republicans an ideal group to study when it comes to this specific phenomenon. However, this neglects the other side of political spectrum, and to this day, very little research exists relating Democrats to the backfire effect. Democrats are emotive individuals as well, and, therefore, I believe they have the potential to exhibit the backfire effect. That is why this study will seek to find the conditions under which Democratic individuals experience the psychological phenomenon.
The final theories that need to be discussed are those that outline what fake news is, how it operates, and how it distinguishes itself from established mainstream news outlets. The definition of “fake news” this thesis uses is “news that is intentionally and verifiably false and created to intentionally mislead readers” (Allcott & Gentzkow 2017). Fake news articles can appear on different types of websites, ranging from those that intentionally fabricate misleading and false articles, to those that publish a mix of factual articles, but often with a partisan bias, and fake news articles (Subramanian 2017).
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There are two predominant explanations for the motivation behind fake news publication. The first is ideological, as fake news publishers seek to propagate an ideology, often done to support or denounce a certain political candidate. Many fake news articles are shared hundreds of thousands of times, and therefore, the ideology being spread comes into contact with many individuals (Allcott & Gentzkow 2017). The second motivation is purely monetary, as fake news publishers profit from the advertising revenue when one of their articles is spread and viewed on a large scale. One U.S. company called Disinfomedia owns several fake news websites, and during the 2016 U.S. General Election they published fake news articles that favored both Trump and Clinton and ultimately earned them hundreds of thousands of dollars in revenue (Subramanian 2017; Sydell 2016).
But how is fake news different from other forms of media, especially those that contain a partisan bias? There are two main distinguishing factors when it comes to categorization of fake news. First, they invest little to no resources in fact-checking and quality control mechanisms, resulting in inaccurate and partisan reporting. Second, they are generally not concerned with their long-term reputation for quality, and instead they focus on maximizing their profits and influence in the short-term (Vosoughi 2018; Allcott & Gentzkow 2017). By masquerading as trusted news sources and including exaggerated and sensationalist headlines in their articles they can attain a substantial audience, as they appear to hold credibility and authority. Ironically, they often criticize and degrade the very media institutions that they are emulating, labeling them as “untrustworthy establishment media” and even fake news (Subramanian 2017). Consequently, fake news has a substantially large viewership due to the fact that consumers cannot distinguish them from established and trusted news sources, and they ultimately provide psychological and partisan utility to the readers (Ibid; Ibid).
As a result of the widespread viewership fake news outlets possess, there are several policy consequences of fake news that have the potential to impact the foundation of normative media and political standards and practices. First, individuals who mistake fake news outlets for high quality publications will ultimately possess less-accurate beliefs and misperceptions. Second, these beliefs may have external societal consequences that undermine democratic processes that are based on truth and reliability. Third, the constant disparaging of reliable and established media could lead to individuals generally becoming skeptical of such institutions. Furthermore, the same effect could happen as it becomes more difficult to distinguish between fake news and reliable news. It is because of these reasons that fake news and its dissemination need to be fully understood so effective policy measures can be taken to prevent the negative impacts they have on journalistic and political life.
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Based on the literature discussed, the central argument of this thesis is that Democratic voters should experience the backfire effect due to the emotional reaction elicited by the fake news corrective text. These emotions can range from stubbornness and anxiety to a feeling of anger after being intentionally misled by a malicious media text.
The origin and type of information provided as the corrective text plays a central role in the functioning and extent of the backfire effect, and as a result, I vary the information sources used in the experiment. The proxy for varying information is the origin on the news source, namely whether it is non-mainstream non-fake, fake left-wing news, or fake right-wing news. Below is a table outlining the origin of the news sources I will be using and the expected reactions and mechanisms for each source.
Type of News Leftist Democrats Centrist Democrats
Mainstream Right-wing News
Negative Response: No backfire M: framing and presentation of news goes against ideological framework.
Negative Response: No backfire M: framing and presentation of news goes against ideological framework. Mainstream Left-wing
News
Positive Response
M: framing and presentation of news supports ideological framework.
Positive Response
M: framing and presentation of news supports ideological framework. mainstream
Non-fake Right-wing News
Negative Response: No backfire M: framing and presentation of news goes against ideological framework. Also ‘dubious’ perception of news.
Negative Response: No backfire M: framing and presentation of news goes against ideological framework. Also ‘dubious’ perception of news. mainstream
Non-fake Left-wing News
Positive Response
M: Leftist Democrats hold more anti-establishment views, and as such respect alternative media that reinforces their views.
Negative Response: No backfire M: Centrist Democrats hold more establishment views, and as such, tend to not trust alternative media, or at least, prefer traditional media. Fake Left-wing News Backfire
M: Distrust of known fake news outlets, or distrust of unknown ‘fishy’ source. Regardless of partisan agreement, Democrats should experience
stubbornness, embarrassment, feelings of malicious intent (misleading).
Backfire
M: Distrust of known fake news outlets, or distrust of unknown ‘fishy’ source. Regardless of partisan agreement, Democrats should experience
stubbornness, embarrassment, feelings of malicious intent (misleading). Fake Right-wing News Backfire
M: Distrust of known fake news outlets, or distrust of unknown ‘fishy’ source. Stubbornness, embarrassment, feelings of malicious intent (misleading)
Backfire
M: Distrust of known fake news outlets, or distrust of unknown ‘fishy’ source. Stubbornness, embarrassment, feelings of malicious intent (misleading)
Table 1.02
2 M= Mechanisms
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As we can see, the majority of scenarios largely overlap when it comes to the variety of Democrats and their interaction with various types of media outlets. The reactions to “mainstream right-wing news”, “mainstream left-wing news”, and “non-mainstream non-fake right-wing news” are predictable and known, and hence fall outside of the scope of this thesis. It can confidently be said that Democratic voters will respond negatively to established right-wing media, but not to a large enough extent as to experience the backfire effect. This is because the information presented within these articles is factually correct, albeit having a right-wing partisan bias.
Regarding the difference in expectations present in the “non-mainstream non-fake left-wing news” category, I hypothesize that “centrist” Democrats will react negatively to the article because they generally prefer established and traditional media to alterative new media (Sunkara 2017; Foer 2017). As a result, I believe they will evaluate the article as somewhat untrustworthy, which should evoke a negative reaction. Conversely, I hypothesize that ‘leftist’ Democrats should react positively to the article as they are likely to be more receptive to alternative forms of media (Ibid; Ibid). In order to test this theory, a non-mainstream non-fake left-wing article was included in the experiment.
Now, this thesis will focus on the instances that lead to the backfire effect, namely the instances where fake news is present. Previous scholars have cited the “sober democrat” effect as the reason we do not observe the backfire effect in Democratic voters. It suggests that Democratic voters value logic and reason, and hence, do not experience emotive, knee-jerk reactions such as the backfire effect (Bessi & Ferrara 2016; Pickard 2016). I believe that in today’s era of American politics, the reliability of news and its influence on people is an important topic and has relatively high political salience. As a result, it is likely a topic that individuals will feel strongly about and have an opinion on, making it ripe to evoke an emotive reaction.
I hypothesize that the most salient mechanisms at work that enable the backfire effect are twofold: the emotional reaction of an individual, namely the feelings of anger, stubbornness, and embarrassment, and the mechanisms of maliciousness that stems from the nature of a fake news article. We have seen how anger plays a role in the information processing mechanisms of an individual and how in order to deal with anger, individuals generally become defensive and dismiss contradictory information that challenges their established viewpoints (Weeks 2015; Marcus et al. 2011). If the fake news article evokes anger in the participants, then it could alter their information processing mechanisms and ultimately lead to a backfire effect.
The mechanisms of embarrassment and stubbornness might also play a role in the backfire effect. For example, the feeling of embarrassment an individual experiences when they are proven wrong (Tangney & Fischer 1995). This could happen after the participant reads the corrective text if they already believe the misperception about wealth distribution in the U.S. that will be used in the experiment. Having their existing
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opinions undermined could lead to a feeling of embarrassment, which could ultimately lead to the backfire effect. Similarly, an individual that feels they have been proven wrong could exhibit feelings of stubbornness and rather support something they know is wrong rather than admit they were wrong in forming their previous opinion (Zapf 2002). These are the main emotional mechanisms I believe to be relevant when discussion the backfire effect.
Aside from the emotional mechanisms, a further important mechanism at work for the backfire effect is the feeling of malicious intent individuals feel when reading a fake news article. Democrats who read fake news when presented to them as fact are expected to feel as though they are being intentionally misled to believe false information. This should trigger the backfire effect as Democrats have the “I knew it” attitude towards the intentionally misleading news and proceed to believe their initial thoughts even more strongly. For example, if a Democrat would read an article from The Daily Caller (a fake non-mainstream right-wing news source), they would likely disagree with the style and framing, but not necessarily with the information within it. However, with fake news, it is often apparent that the article is not genuine in the information it presents and that its intent is to mislead (Allcott & Gentzkow 2017). I believe this would evoke a much stronger negative reaction in Democratic voters. This effect should occur regardless of political orientation within the Democrat party, as both types of Democrats have likely been exposed to the same rhetoric regarding fake news by popular culture and other news outlets. Overall, I expect to see both types of Democratic voters experience the backfire effect when it comes to the fake news corrective texts, as the type of news fundamentally goes against traditional Democratic principles, such as integrity, validity, and trust in the establishment (Wagner 2007; Democratic Party 2016).
If the backfire effect is experienced in more or fewer cases than the ones outlined above, then my theories would be incorrect. If more cases are observed, an alternative explanation could be that Democrats experience the backfire effect more than anticipated due to the politically volatile climate and so called “culture war” in today’s age. If fewer cases are observed, an alternative explanation could be that Democrats are truly “sober” and do not react emotively based on certain information, regardless of political orientation within the Democratic party. In this case, Democrats could simply be less dogmatic and reactive to their Republican counterparts, which would be in line with previous studies and the current literature on the subject. However, further research would need to be done in order to confirm this in direct relation to Democratic voters, as it would fall outside of the scope of this thesis.
Now that this thesis has thoroughly examined the literature and theories behind the backfire effect, the methodology surrounding the experimental design will be discussed.
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4. RESEARCH DESIGN 4.1 Variables
The dependent variable I will be measuring in this study is the responses of registered Democrats to the corrective media texts (varying in origin) that are made to address an already existing misperception surrounding a current political issue. I will examine the existence and extent of the backfire effect by comparing the average Likert scale responses from the control group, that will not receive a corrective media text, and the experimental groups that receive a corrective text at the end of the experiment. The extent to which the participants experienced the backfire effect can then be concluded simply by comparing the mean of the two groups.
The independent variable will be the origin of the information given to the respondents. This will be implemented by using media text origin as a proxy. All of the media texts will originate from non-mainstream alternative sources. As such, the categories will be either non-non-mainstream but factually correct news (otherwise demarcated as “non-mainstream non-fake”), fake news with a right-wing bias (otherwise demarcated as “fake right-wing”), or fake news with a wing bias (otherwise demarcated as “fake left-wing”). The labels that will be placed on the articles in the survey will be determined using an international media ranking institution that evaluates the validity of various news sources (Snopes 2018).
The variation in this experiment lies in the distinction made between the two types of Democrats and the types of media texts used. One group is more progressive whilst the other is more conservative, and the natural partisan variation created within the group should be suitable for the purposes of this experiment. Additionally, the origin of the news sources acts as a proxy for varying types of information.
4.2 Methodology
For this experiment, I have chosen to conduct a survey in the United States in order to accurately collect microlevel data from the relevant demographics. Surveys are an ideal tool for microlevel data collection as it gathers the information straight from the individuals that are being studied. Furthermore, conducting a survey will allow for my results to be compared with similar studies, adding directly to the existing knowledge on the subject in a similar and comparable format.3 This will enable further research to easily
draw upon my and other similar data for future analysis.
A limitation, however, is that because this survey collected data at a single point in time, it is difficult to identify trends in a given population, as well as measure changes over time unless more surveys are conducted. Repeating the survey at a later point in time would be both time consuming and expensive and would present significant challenges for the completion of my research. A final limitation is that whilst
3 Survey comparison could be applicable (but not limited) to: Nyhan & Reifler 2010, Guess et al. 2018, Flynn et al.
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this study will have a more representative sample than previous studies conducted due to the inclusion of participants outside of only university students, it will in no way be an accurate representative sample. This is also because this survey was distributed online, and hence, only reached a certain demographic of individuals who use social media and other online internet fora.
The survey was predominantly given to online contributors on Democrat internet fora and message boards, as this was a relatively easy access point to Democratic voters and should work to increase diversity in the data set. 4 This sample will also have the advantage of not being restricted to university students and
will have relatively varied demographic representation. Seeing as it is clear that the survey is targeted towards Democratic voters, Republican and Independent voters, who identified more as Republicans, were discarded from the sample. This was done to maximize the number of Democratic voters that would be exposed the survey, and also to target a demographic that would be politically interested and engaged enough to participate in the study. It is important to mention that this creates a bias towards younger, more politically engaged individuals that actively contribute on internet fora. As a result, this is not a completely accurate cross-section demographic.
The experiments used in previous studies to investigate the backfire effect have been almost uniform in their design (Nyhan & Reifler 2010; Fridkin et al. 2015). The survey itself consisted of one control group and three treatment groups (each receiving a different type of media text), each with a limit of 50 randomly assigned respondents.5 The survey began with a partisanship test where the participants
were asked to report their own ideological position (progressive or conservative) and their partisan affiliation within the Democratic party (leftist Democrat or centrist Democrat) on a 5-point Likert scale. Those who fell in the ‘1’ or ‘2’ category were given the left-leaning labels, and those that fell in the ‘3’, ‘4’, or ‘5’ were given the center-leaning labels. I have decided to assign ‘3’ to the center-leaning label due to the fact that those that belong in the left-leaning category are generally more convinced of their views, and hence tend to move closer to the poles of the political spectrum. Secondly, participants were asked if they self-identify as “strong Democrats” or “not so strong Democrats” which will act as a second indicator of partisanship. I believe these two indicators will overwhelmingly overlap and will be enough to label the participants with the categories above. In the case that there is a contradiction in the different tests, then the subject will be removed from the data set.
Next, the respondents were asked to give their views on how the wealth in America is distributed amongst the top 20% of wealthiest individuals and the bottom 20% of poorest individuals. This figure was reported as a percentage. Their responses were then compared to what the actual figures are, and based on
4 Such social media sites include Facebook, Twitter, and LinkedIn. The fora mentioned are predominantly hosted on
Reddit, with subreddits such as r/Politics, r/Democrats, r/Progressive, r/HillaryClinton, and r/SandersforPresident.
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how close they were, they were labeled as “informed” or “misinformed”. This was done to see if the extent to which an individual is informed or misinformed plays a role in the extent to which the participant experiences the backfire effect.
After partisanship and the extent to which the individual was informed or misinformed were ascertained, participants were given a mock mainstream newspaper article that outlined one issue in American politics and ultimately reinforced an already existing widespread misperception surrounding it. This mock newspaper article stated it received its sources from The New York Times (NYT) in order to establish it as a credible source of information. It is important to emphasize that the misperception used in this experiment is one that is already commonly presented in American society. The NYT article does not fabricate the misperception, but instead facilitates its exposure and lends it authority.
The text was adapted from the mock text used by Nyhan & Reifler in their experiment by replacing the subject matter and statistics with those that were relevant for this experiment (Nyhan & Reifler 2010). For this study, I used various statistics and indicators regarding U.S. wealth inequality and distribution to act as the already existing common misperception, as studies have shown that the American public is highly misinformed in this area and that it is currently a topical and relevant issue (Lusardi et al. 2015; Norton 2011). The mock NYT article claimed that the middle class in America was expanding and how the rich were in fact getting poorer. It is important to note that all of the groups received the same mock article from and an additional text of varying origin, depending on which group the participants were randomly assigned to. The control group received no additional text.
After reading the mock NYT article, the participants (minus the control group) were asked to either read a second text published either by The Daily Beast (non-mainstream non-fake news), The Alternative
Media Syndicate (fake left-wing news), or The Breitbart News Network (fake right-wing news) that directly
contradicted the information published by the initial article (FactCheck 2018). They describe how the middle class is shrinking whilst the lower class is expanding, citing statistics such the fact that the middle class in the U.S. shrunk from 62% to 59% over the past 19 years whilst the average business executive receives $100,000 in bonuses a year (Kochhar 2017). In this way, the second text acted as a corrective text meant to trigger a backfire effect in the participants. The type of text they received depended on the group they were randomly assigned to.
After reading the text(s), the participants were asked to give their opinion on the validity of the following statement:
“Middle-class Americans are seeing a positive change in terms of wealth disparity, as they are being paid more and have better opportunities, as money is being directed from the top back to the middle of society”.
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This statement reinforces the misperception, as perpetuated in the mock NYT article, and acts as the vehicle for measuring the extent of the backfire effect experienced by the participants. They answered in the form of a Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). The difference in the aggregated average response between the control group and the experimental group indicates whether the participant has viewed the correction favorably or has experienced the backfire effect.
Next, the respondents were asked to report the intensity of the emotions they experienced while reading the articles, and also to report why they answered the way they did. This was measured using a 5-point Likert scale for various emotions that respondents could experience, ranging from ‘not at all’ to ‘completely’. An open-ended question was also included to allow respondents to describe their thought process and emotions in more detail. This combination of quantitative and qualitative data should provide enough information to deduce the mechanisms behind the backfire effect the participants experienced.
Finally, they were asked who they would vote for if a General Election were to be held tomorrow. The answers were based on ideology, as the respondents could choose from multiple options ranging from a “right-leaning Republican” to a “left-leaning Democrat”, with the inclusion of a “third party candidate” and a “prefer not to say” category. This will be done in hopes that a link can be established between the effects of fake news, misperceptions, and the backfire effect on voting behavior.
In order to ensure that any consequences of the experiment are linked to the origin of the news source, a question was added at the end of the survey that asked participants if they believed the second text they read was false, inaccurate, or fake. Those who answered ‘no’ or ‘unsure’ for either of the two fake texts used in this study were removed from the sample as their response would likely be independent of the type of news source they were assigned.
The survey results from the experiment will now be analyzed and a discussion will be held to explain and evaluate the collected results.
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5. ANALYSIS AND RESULTS 5.1 Demographics
This section of the analysis will investigate the demographics of the sample and conclude whether they are statistically similar enough to be accurately compared. In order to process the results of the survey, the demographical answers were coded to ‘1s’ and ‘0’ for ease of analysis. Therefore: gender was coded as ‘1’ for male and ‘0’ for females, ethnicity was coded as ‘1’ for White or Caucasian (as they were by far the largest single category) and ‘0’ for the rest, education was coded as ‘1’ for university education and ‘0’ for other, and employment was coded as ‘1’ for those employed (fulltime, part time, or self-employed) and ‘0’ for the rest. Lastly, age was collected by asking participants to place themselves within premade age categories. Therefore, the median age from each age category was used to calculate the average age of each sample.6
An analysis of variance (ANOVA) test was conducted on the samples in order to determine their comparability. I follow the previous literature and the critical 𝑝 value at 5%. The ANOVA test resulted in an 𝐹 value of 0.001097 and an 𝐹𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 value of 3.098391. Seeing as 𝐹𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙> 𝐹, we fail to reject the null
hypothesis, indicating that the populations are indeed similar enough to be accurately compared. This also means that this thesis can accurately conduct t-tests to compare the means of all of the groups. The next section will outline the results derived from the control group that will ultimately become the foundation for the remainder of this study.
5.2 Control Group Results
As was previously discussed, the control group was not given a second text to act as a correction to the already existing common misperception perpetuated in the NYT text. As a result, these respondents could also not answer the questions relating to their emotional state while reading the articles, or the open question about their thought process. Therefore, they were only asked to report to what extent they agreed with the statement regarding the growth of the middle class and the shrinking of wealth disparity in the U.S.7
The majority of respondents disagreed with the statement supporting the already existing common misperception perpetuated by the mock NYT article. 74% of respondents said they either strongly disagreed or somewhat disagreed with the statement reinforcing the misperception. Additionally, the mean response was 1.96, indicating that the majority of the responses are clustered around the ‘1’ and ‘2’ categories on the Likert scale used. The mean result for this control group will be used as a benchmark and point of reference for this study going forward. A higher mean in the experimental groups should indicate that the participants
6 The mean and standard deviation of each demographic variable can be found in Appendix 3. 7 The visualization of the Likert scale answers of the control group can be found in Appendix 4.
