The use of emotions in conspiracy and debunking videos to engage publics on YouTube

Abstract

With the rise of digital media, conspiracy theories infamous for their emotional manipulation have challenged science epistemology and democratic discourse. Despite extensive literature on misinformation and the role of emotion in persuasion, less is understood about how emotion is used in conspiracy and debunking messages on video platforms and the impact of emotional framing on public engagement with science on social media. Our article fills this gap by analyzing thousands of YouTube videos that propagate or debunk COVID-19 conspiracy theories from March to May 2020. We found that conspiracy and debunking videos used the emotions of trust and fear differently depending on the issue framing of the conspiracy. Our article also reveals a dilemma facing debunking messages—when debunking videos used more trust-related emotions, these videos received more likes yet fewer views. These findings shed new light on the role of emotion on user engagement with misinformation and its correction on digital platforms.

Keywords

Automated text analysis conspiracy COVID-19 debunking emotion fear trust user engagement YouTube mixed methods

With the rise of conspiracy and misinformation on social media, the detrimental effects of emotionally charged content embedded in conspiracy theories have become a focal issue in public spheres (Cichocka, 2020). Nevertheless, the influence of emotion on individuals’ attention to misinformation is underexplored (Pasquetto et al., 2020). Misinformation could be considered an antonym for accurate messages (Scheufele and Krause, 2019). Among the various types of misinformation, conspiracy theories are narratives about groups of social actors who have malicious intent to threaten citizens for nefarious purposes (Oliver and Wood, 2014).

One noteworthy feature of conspiracy theories is their strong emotional narratives (van Prooijen et al., 2021). Conspiracy theories often direct anger toward the object of blame to justify violent behaviors toward an established political system and actors (Jolley and Paterson, 2020). As a core topic in communication research, scholars have studied emotion in persuasion (e.g. fear appeals; trustworthiness), message frames (Bilandzic et al., 2020), and affective publics on social media (Gerbaudo et al., 2019). In the context of conspiracy theories, emotional narratives have been shown to intensify partisanship and hatred (van Prooijen, 2018). Recent works noted that emotion is an important mediator that influences the effectiveness of misinformation correction (Nabi, 2003, 2015) and the sharing of misinformation and accurate information on social media (Freiling et al., 2021).

Despite a growing body of scholarship examining the use of emotion in conspiracy theories and public responses on social media, several areas are still underexplored. By examining discrete emotions utilized by conspiracy videos and debunking videos on YouTube and demonstrating the association between emotional frames and audiences’ attention and engagements with these videos, our article fills gaps in two underexplored areas in current scholarship. The first area is how emotions are used in debunking messages on social media, especially on video platforms. Many studies on misinformation and conspiracies focus on the narrative and diffusion of misinformation (Vosoughi et al., 2018) on text-based platforms, such as Twitter or Facebook. Yet, conspiracy theories and debunking narratives often co-exist and compete on social media (Jung et al., 2020). Both the lay public and experts refute misinformation on social media (Bode and Vraga, 2017). This nature of the media ecosystem requires researchers to investigate not only the narratives of conspiracy theories but also debunking narratives on social media.

As Pasquetto et al. (2020) stressed, there is a need for a “more comprehensive picture of the emotional nature of misinformation” (p. 5) to address a gap in the current misinformation literature—the degree to which emotion influences audiences’ engagements with social media content. Thus, this article examines how conspiracy videos and debunking videos use discrete emotions on YouTube and how these emotions are associated with audiences’ attention and engagement with videos. Our article aims to add breadth to current literature on examining emotions in conspiracy theories and debunking messages by collecting and analyzing YouTube videos that were highly viewed and published from 1 March to 31 May 2020—when the United States first experienced the pandemic. We focus on COVID-19 conspiracy videos because the spread and powerful influence of these conspiracies have become a major safety concern during the pandemic.

The second underexplored area is how emotional use might differ across various conspiracy narratives, from geopolitical-focused conspiracies (e.g. bioweapons) to social elite–focused conspiracies (e.g. Bill Gates, QAnon). There could be heterogeneous use of emotion across conspiracy themes, even for the same topic, such as COVID-19 (Jolley and Paterson, 2020). Exploring the heterogeneous use of emotions and the role of these various emotion narratives in user attention (e.g. number of views) and engagement (e.g. number of likes), our article contributes to scholarship in misinformation and provides implications for corrective messages (e.g. Lewandowsky et al., 2012; Tully et al., 2020).

Our article is structured as follows. We first introduce literature on emotion frames in conspiracy theories and debunking messages and scholarship on social media attention and engagement. We then discuss why we chose YouTube as our study platform, the dataset, and the methodology of studying emotions. Results of our research questions will be presented. We finally discuss how our article shed new light on our understanding of emotional frames in social media conspiracies and implications for misinformation correction.

Conspiracy theories and debunking messages on social media

Social media has reduced the barrier to sharing content (Kruse et al., 2018). This content creation environment includes traditional media actors and scientists, the lay public, and alternative influencers (Chen et al., 2022). The low entry cost of social media also makes harmful actors easily spread conspiracy theories and misinformation (Tingley and Wagner, 2017).

While an increasing number of conspiracy theories are circulated on social media, there are also numerous users who debunk such misinformation (Chadwick and Vaccari, 2019). As such, social media is a playground of contestation in which conspiracies and debunking messages co-exist and compete. For example, both conspiracy theorists and self-correcting crowds compete on YouTube where the former spreads misinformation and the latter counterargues misinformation about science, technology, and health (Schmitt et al., 2018).

Conspiracy theories on social media often seek to persuade users to believe in their plots through their narratives (Oswald, 2016). At the same time, debunking messages on social media aim to convince users to reject conspiracy theories or dispel false beliefs (Pal et al., 2020). Especially, conspiracy theories often provide audiences with emotional experiences (Panchenko, 2016), and viral conspiracy theories on social media utilize emotional appeals to grab users’ attention (Zhang et al., 2021) and engagement (Germani and Biller-Andorno, 2021). As a result, audiences’ beliefs in conspiracy theories sometimes stem from emotions (van Prooijen and Douglas, 2018). While it is known that emotionally charged attitudes should be countered with emotional messages (Sangalang et al., 2019), emotional appeals used in debunking messages by self-correcting crowds are little studied. Therefore, it is crucial to understand how conspiracy theories and debunking messages utilize emotional cues to engage users and compete for public attention on social media.

Studying emotional frames in conspiracy theories and debunking messages

Emotional cues in conspiracy theories

Emotion is a mental state or biological reaction representing the evaluation of an external or internal stimulus event (Nabi, 2010; Scherer, 2005). When an emotion is represented as a mental state, pleasure or displeasure is experienced as the core affect comprising emotion (Barrett et al., 2007). Increasing research explores the explanatory power of emotions, and one thread of research focuses on how emotions are reflected in languages (Dukes et al., 2021). Emotional vocabulary in messages, therefore, can function as emotional cues. These cues in messages can serve as frames that elicit emotions from the recipients, as explicated in the emotion-as-frames model (Nabi, 2003).

In the emotions-as-frames model, Nabi (2003) incorporates the appraisal theory of emotion and framing theory and suggests that when certain emotions are repeatedly paired with certain events or ideas, these events or ideas induce distinct appraisal patterns. For example, if a message has features that repeatedly evoke appraisal patterns related to fear, audiences experience fear through unique appraisal patterns while reading the message. The signifiers of appraisal patterns that evoke fear can, therefore, function as a frame in a message arousing fear.

Conspiracy theories are known to utilize emotional appeals on social media (Marcellino et al., 2021). Studies found that conspiracy theories often utilize negative emotions to persuade audiences. For instance, Fong et al. (2021) found that compared to science advocates, conspiracy theorists significantly use negative emotions, such as anger, on Twitter. Gerts et al. (2021) also noted that tweets delivering misleading information related to COVID-19 contain a substantial number of negative sentiments compared to other tweets without misinformation involved. Of the various emotion types, classic literature argues that conspiracy theories are infamous for utilizing emotional appeals to arouse distrust and fear among audiences (Bale, 2007; van Prooijen, 2018). Commonly, for instance, they target audiences who distrust legitimate institutions and then generate fear, uncertainty (van Prooijen, 2018), or paranoia (Bale, 2007) about the current state of the audience.

While most studies have examined conspiracy theories on platforms such as Twitter or Facebook, there is a shortage of studies examining how conspiracy theories utilize emotions on video platforms such as YouTube, even though conspiracy theories such as Plandemic were shared extensively as a format of video (DFRLab, 2020). While Twitter often delivers messages in short textual form, YouTube provides messages in longer video format. Therefore, the emotional use of conspiracy theories on YouTube can be different from those on Twitter. Moreover, most studies that examined how conspiracy messages use emotions on social media were often exploratory by calculating the frequencies across different emotions. Exploratory analyses are valuable for understanding the intensity of emotions, yet they often lack a theoretical angle to justify why researchers analyzed certain emotions in conspiracy theories more than others. Also, examining the use of emotions solely on calculating frequencies cannot identify themes that contextualize the use of emotion, which are critical for substantiating our understanding of how emotions are used. Therefore, we raise our first research question based on classic literature that defines the core emotions of conspiracy theories as trust and fear and we also provide rich contexts to uncover the themes used with these emotions:

RQ1. How are emotional appeals (primarily those related to trust and fear) used in COVID-19 conspiracy messages on YouTube?

Emotional cues in debunking messages

While it is well known that fear and (dis)trust are associated with conspiracy theories, what emotions are used in debunking messages on social media is little known, despite the growing research on experimenting with emotional appeals in debunking designs (Sangalang et al., 2019; Trevors and Kendeou, 2020). In the context of the tobacco domain, Sangalang et al. (2019) conducted a survey experiment testing the effectiveness of correction messages eliciting discrete emotions debunking misinformation about organic tobacco, such as fear and anger.

Sangalang et al. (2019) found that correction messages with emotional endings are better at correcting audiences’ attitudes toward organic tobacco compared to simple corrective without emotion-inducing narratives. In a similar vein, Trevors and Kendeou (2020) showed that negative emotions embedded in the corrective messages help audiences revise their knowledge of misconceptions about vaccines through a set of experiments. While both experimental studies stress the important role of emotions in debunking messages, the question of how emotions are embedded in debunking messages on social media is not yet answered.

As Pasquetto et al. (2020) stressed, tackling misinformation on social media requires scholars to understand the role of emotion in the social media environment where misinformation flourishes. Developing a comprehensive understanding of this emotional nature (1) should not only consist of studying how emotions are used in misinformation (2) but also how emotions are used in debunking messages. There has been an increase in conducting experimental studies to understand the role of emotion in debunking messages (van der Meer and Jin, 2020; Wang and Huang, 2021). For instance, Wang and Huang (2021) found that debunking messages containing emotionally engaging stories about e-cigarettes did not reduce counterarguing the arguments in messages from audiences. van der Meer and Jin (2020) also demonstrated that emotions mediated the risk assessment of the public health crisis when corrective messages are given to the audiences.

Compared to the increasing interest in exploring the role of emotions in debunking messages with experimental designs, there is a gap in the literature on how debunking messages on social media utilize emotional frames to make audiences attentive or engage with the content. Addressing this gap requires researchers to investigate discrete emotions used in debunking messages in more organic settings, such as social media. We especially focus on the emotion of trust and fear because debunking messages challenge conspiracy theories on social media and therefore could also target emotions that are frequently used in conspiracy theories. Therefore, using YouTube as an example, we propose our second research question to examine how emotions are used in debunking messages on social media:

RQ2. How are emotional appeals (primarily those related to trust and fear) used in COVID-19 debunking videos on YouTube?

Moreover, it is imperative to note that emotional frames embedded in conspiracy theories and debunking messages can be contingent on the specific issue addressed in each theory. These issue narratives diverge by attributing responsibility and blame to distinct groups as origins of negative events (Chen et al., 2020). The diverse issue narratives stemming from a single topic have been well demonstrated (Niehaus and Jonsson, 2005), but the emotional frames contingent on these issue narratives are little understood. Recent evidence shows that emotions are contingent depending on the issue frames for COVID-19 misinformation. For instance, Gerts et al. (2021) studied COVID-19 tweets regarding five specific issues related to COVID-19 misinformation and found that compared to other issues, utilization of emotions by misinformation and non-misinformation tweets was similar for the 5G conspiracy. These findings illustrate how social media posts associated with various themes of a conspiracy theory can utilize emotions differently. Grounded in these recent works that have suggested the importance of examining how emotion use might be issue-dependent, we raise our third research question:

RQ3. Within the larger category of COVID-19 conspiracy theories, how are emotional frames used differently in conspiracy theories and debunking messages with different issue narratives (e.g. geopolitics, technology)?

Emotional frames of conspiracy and debunking messages and users’ attention and engagement

Emotional frames have been shown to influence people’s cognitive processing of messages (Lang et al., 1996). Lang and her co-authors found that the presence of negative videos in the news stories increased people’s attention as measured by their heart rate. This positive relationship between emotion and attention is explained in the “dimensional theories of emotion,” where people give more attention to emotional messages because these messages are more easily to be stored in and retrieved from memory compared to non-emotional messages (Lang et al., 1995). Other research shows that messages with emotional frames, such as threatening faces, are more likely to capture the automatic attention of audiences because people recognize these as important for their individual survival and socialization (Maratos, 2011).

Attention is one of the most valuable commodities in the social media environment because information on social media is almost infinite, but individuals’ capacity to pay attention to given information is relatively limited (Webster, 2014). In the attention economy, attention is captured by attention metrics such as the number of views, and audiences further interact with the content with engagement metrics, such as the number of sharing, retweets, or likes on social media platforms (Webster, 2014). However, users must first spot messages on social media to be processed further and to change users’ attitudes or behaviors. Thus, it is critical to investigate the relationship between emotional frames embedded in conspiracy and debunking messages and users’ attention (e.g. number of views).

Affective intelligence theory (AIT) further posits that, particularly in politics, emotion and reason regarding an issue or figure could interactively determine the extent of people’s attentiveness, the depth of thinking, and the degree of engagement about an issue or figure (MacKuen et al., 2010, 2011; Marcus et al., 2000). Choi et al. (2021) found that emotional frames embedded in the news, such as “sadness frames” increase Facebook users’ engagement with news by sharing and commenting. This evidence demonstrates that emotional frames on social media not only capture attention but also increase users’ engagement with the content.

As mentioned before, conspiracy theories are messages that contain strong emotional tones (Katz and Mays, 2019; Vosoughi et al., 2018), such as fear and distrust. Conspiracy theories spread widely on social media platforms as the emotions evoked by conspiracy theories could encourage audiences to actively engage with the content. For example, Zollo et al. (2015) found that the number of comments on Facebook increases when anti-vaccination posts include negative sentiments. Compared to debunking messages, conspiracy theories usually contain strong emotional cues, so audiences are more likely to be attentive to conspiracy theories. Therefore, we propose our first hypothesis to test the relationship between conspiracy messages and social media attention and engagement:

H1. Conspiracy messages (related to COVID-19) will gain more attention and engagement from social media users (i.e. measured by the number of views and likes) compared to messages that debunk COVID-19 conspiracy theories.

Compared to studies on conspiracy theories to understand how emotion can grab the attention of citizens on social media, there is a dearth of literature on how emotional frames in debunking messages attract attention from social media users. Most current literature has focused on experimental settings to study how debunking narratives with emotional frames might change people’s attitudes toward health decisions (e.g. Sangalang et al., 2019). Although these papers suggested the reason that emotional correctives can decrease public misperception is due to the mental model of participants paying more attention to emotional content, these studies often did not test “attention” or “engagement” in their experiments. Our article fills this gap in the literature by studying attention on social media messages and illustrating how emotional content in debunking messages might be received by digital audiences. Therefore, we propose our fourth research question:

RQ4. How do emotional frames moderate the relationship between debunking messages and attention and engagement from social media users?

Data and method

YouTube as an emotional public sphere for studying conspiracy theories and debunking messages

This article focuses on YouTube, which predominantly functions as an emotional public sphere in which emotionally charged expressions are prevalent and audiences’ responses to posted videos are often emotional (Lee, 2019). YouTube, compared to other social media platforms, provides a space for users to express their vivid experiences exchanging emotions through multi-modal forms (Rosenbusch et al., 2019). Of numerous video-sharing social media platforms acting as information sources, YouTube is one of the biggest for science, technology, and health information (Marchal and Au, 2020). Global citizens rely on YouTube as a cultural archive, where they learn the contemporary culture and at the same time actively engage in creating the cultural archive (Burgess and Green, 2018). While citizens heavily rely on YouTube to attain such scientific and cultural knowledge, YouTube is known for being a “radicalizer” in the digital space because misinformation is heavily circulated on the platform (Tufekci, 2018). Both conspiracy theorists and prevention actors compete on YouTube to spread misinformation or to counterargue against misinformation (Schmitt et al., 2018). As such, YouTube functions both as an emotional public sphere and a battlefield for conspiracy theories and debunking messages. Seeing as citizens are heavily influenced by the content hosted on YouTube, it is important to understand how emotional appeals are being utilized in conspiracy theories and debunking messages to engage users and to compete for the public’s attention on YouTube.

Sampling COVID-19 conspiracy-related videos

This article uses the example of COVID-19 conspiracy theories to examine the use of emotion in conspiracy versus debunking videos and how emotion influences user engagement. We chose COVID-19 as our topic because of the relevant political and scientific implications (Krause et al., 2020). To sample COVID-19 conspiracy-related videos, we drew on search terms from literature and the Google YouTube Search trend (Pennycook et al., 2020). These search terms covered a variety of themes found in COVID-19 conspiracies (Table 1), such as those related to geopolitics (Wuhan Virus, Bioweapon, Dean Koontz), modern technology (5G), and distrust of social elites (Bill Gates population control conspiracy, Judy Mikovits, QAnon). After obtaining access to the YouTube Application Programming Interface (API), we used the Python package youtube-data-api (Yin and Brown, 2018) to sample the top 10 most viewed videos each day from 1 March to 31 May 2020 (N = 3668). Among them, 2706 were English-speaking videos. Using youtube-data-api, we extracted video-related information such as title, description, channel-related information, and engagement metrics (number of views, dislikes, likes, and comments). Then, using the Python package PyTube (n.d.), we collected the transcripts (textual captions) of these videos.

Table 1.

The procedure of developing search term related to COVID-19 conspiracy theories.

Topics from literature review	Literature review sources	Developed search term from Google Trends
Bill Gates: Bill Gates planned COVID-19, Bill Gates implant microchip vaccines	Bass and Cheng (2020)	Bill Gates coronavirus
Judy Mikovits: Dr Judy’s documentary Pandemic is correct	McGreal (2020)	Judy Mikovits
QAnon: Different conspiracies against US government	Ferrara (2020)	QAnon coronavirus
Wuhan lab/ Wuhan virus: COVID-19 came from a lab in Wuhan	Au et al. (2020)	Wuhan lab
coronavirus weapon/biowarfare/bioweapon: COVID-19 is bioweapon	The Medical Futurist (2020)	bioweapon coronavirus
5G coronavirus: 5G causes coronavirus	Ahmed et al. (2020)	5G coronavirus
darkness dean koontz: Dean Knootz predicted COVID-19 in his book The Eyes of Darkness	Enders and Uscinski (2020)	dean koontz darkness

Content analysis on video transcripts

To classify conspiracy videos and debunking videos related to COVID-19, we performed a content analysis on video transcripts to label two main variables: relevance and type of the video. Relevance was coded as 1 if a video indeed discussed the COVID-19 conspiracy theories and 0 otherwise. The type of the video was coded as 1 if the video propagated COVID-19 conspiracy theories and 0 if the video debunked COVID-19 conspiracy theories (see Supplemental Material I for coding rules). Two researchers first manually coded a small sample of videos (n = 62) and achieved inter-coder reliability of 78% for relevancy and 95% for the type of the video (using Krippendorff’s alpha). After achieving the inter-coder agreement, 560 more videos were hand-labeled by researchers. Following this stage, we used these 560 videos as the training dataset and applied the supervised machine learning method in the Python scikit-learn library to train a classifier to categorize the relevance and the type of videos. During this training process, we turned words into vectors and implemented a multi-layer perception with two densely connected layers of 64 hidden elements. The precision and recall of the supervised machine learning performance were above 80%. Thus, we used this classifier to label the remaining 2000+ videos.

To measure the frequency of the use of various emotions, we used the dictionary method and applied the NRC Emotion Lexicon to our video transcripts. The NRC Emotion Lexicon allows counting the frequency of the words associated with eight emotions: anger, fear, anticipation, trust, surprise, sadness, joy, and disgust. We chose the NRC dictionary since it contains two key emotions of our interest (i.e. trust and fear). We computed the normalized emotion count by dividing the raw count by the transcript’s total number of characters. Our validation of the NRC dictionary is detailed in Supplemental Material Appendix IV. To situate the understanding of our dictionary emotions under the YouTube context, we also randomly selected transcript segments from conspiracy videos and debunking videos using the NRC dictionary’s trust and fear words and took a qualitative examination to complement the automated content analysis (Andreotta et al., 2019). Details of our qualitative reading procedures are recorded in Supplemental Material II.

Analysis method

To examine the use of emotion in the conspiracy (RQ1) and debunking videos (RQ2), we conducted a two-samples t-test to analyze the difference in emotion count between conspiracy videos and debunking videos. To answer RQ3, we subset our videos based on search words to distinguish which themes a video belongs to and then compared the emotion count between conspiracy and debunking videos within each theme. To study the impact of emotion and the type of video on user engagement (H1, RQ4), we conducted the ordinary least squares (OLS) regression.

Results

Trust-related narratives in conspiracy and debunking videos

To answer RQ1, we first conducted a qualitative analysis to provide a contextualized understanding of the two emotions closely related to conspiracy theories—trust (Table 2) and fear (Table 3). This analysis helps enrich our understanding of the contexts of our quantitative analyses for RQ1. The most common theme from conspiracy theories using trust-related terms was elaborating on the conspiracy plot. Conspiracy theories associated with this theme included explanations about “how 5G could be dangerous” or “how silent war removes power structure.” The second common theme was expressing distrust in established institutions. The claims from conspiracy theories with this theme included blaming “World Health Organization” or “medical practitioners.” The last theme that emerged from the conspiracy theories was presenting fearful situations. Claims such as “fear on the street is very real” were associated with this theme.

Table 2.

Summary of themes emerged in conspiracy theories and debunking messages using trust-related terms.

Classification	Themes	Example	Proportion (%)
Conspiracy	Elaborating on the conspiracy plot	“behind the scenes the silent war removing the power structure, removing their entire global apparatus”	42
Conspiracy	Expressing distrust in established institutions	“why didn’t the World Health Organization know that this had human to human transmission”	38
Conspiracy	Presenting fearful situations	“then all told the fear on the street is very real”	10
Debunking	Counterarguing conspiracy theories	“let’s explain exactly why these theories are so wrong”	54
Debunking	Emphasizing the effort of official institutions	“World Health Organization has been fighting it has reportedly held meetings with tech groups like Facebook Amazon and Airbnb about providing accurate information today”	10

Note. 10% of the conspiracy theories and 36% of the debunking messages that contained the trust-related vocabulary were not classified into a theme because it was hard to understand the overall context of the given segment for the qualitative analysis.

Table 3.

Summary of themes emerged in conspiracy theories and debunking messages using fear-related terms.

Classification	Themes	Example	Proportion (%)
Conspiracy	Elaborating on the conspiracy plot	“DNA genetically engineered into a camera biological warfare weapon involving the corona virus”	38
Conspiracy	Presenting fearful situations	“I know the mainstream media is out there they’re still pushing fear panic chaos”	30
Conspiracy	Expressing distrust in established institutions	“it’s just horrible journalism you only learn one side they always interview low IQ people”	12
Debunking	Counterarguing conspiracy theories	“it’s not merely in an opinion or an interesting conspiracy it’s just bollocks so what has 5g got to do with coronavirus”	58
Debunking	Supporting explanations of COVID-19 causes by official institutions	“I think the data is so overwhelmingly valid and validated to show that the viruses cause disease it’s almost difficult for me to argue why.”	10

Note. 12% of the conspiracy theories and 32% of the debunking messages that contained the trust-related vocabulary were not classified into a theme because it was hard to understand the overall context of the given segment for the qualitative analysis.

To answer RQ2, we conducted a qualitative analysis to contextualize the understanding of using emotions in debunking videos. For debunking videos, the most common theme using trust-related words was about counterarguing conspiracy theories. Claims such as “there is no connection between 5g towers and coronavirus” were associated with this theme. Another theme from debunking messages using trust-related words was about emphasizing the effort of official institutions. The claims from debunking messages with this theme included descriptions of efforts by “World Health Organization” or “scientists.”

Fear-related narratives in conspiracy and debunking videos

Similar to conspiracy theories using trust-related terms, conspiracy theories using fear-related vocabulary included elaborations on conspiracy plots, such as “coronavirus is a bioweapon.” Claims such as “there’s gonna be an army presence” appeared in conspiracy theories presenting fearful situations. Finally, claims such as “it’s just horrible journalism you only learn one side” express the distrust in established institutions.

In contrast, the two themes that appeared in debunking messages using fear-related terms were (1) directly counterarguing conspiracy theories and (2) supporting explanations of COVID-19 causes by official institutions. Claims such as “it’s not merely in an opinion or an interesting conspiracy it’s just bollocks” were included in debunking messages counterarguing conspiracy theories. Claims such as “World Health Organization has found no evidence that 5g adversely affects health” were included in debunking messages supporting explanations of COVID-19 causes by official institutions.

The themes we identified in conspiracy videos using (dis)trust and fear echo with previous works of van Prooijen (2018) and Bale (2007), demonstrating that conspiracy theories target audiences who distrust legitimate institutions and generate fear, uncertainty, or paranoia in audiences. However, we revealed the themes related to trust and fear in debunking videos on social media that are little understood in the literature. These themes include the following: (1) counterarguing conspiracy theories, (2) emphasizing the efforts of established institutions, and (3) supporting explanations of COVID-19 causes by official institutions.

Conspiracy videos use more emotions than debunking videos for every emotion type

We conducted a quantitative analysis to complement our qualitative findings. We found that conspiracy videos, on average, used more emotional words compared to debunking videos, for every emotion type (RQ1). For instance, for the trust-related emotion, conspiracy videos used 5.66 more trust-related words than debunking videos (p < .01). Conspiracy videos also used 4.81 more fear appeal words compared to debunking videos (p < .01) (see Table 4). To answer RQ2 regarding how debunking videos used emotions, Figure 1 shows that among the various emotions, trust-related emotions and fear-related emotions are much more frequently used in conspiracy and debunking videos compared to other emotions such as disgust, joy, and surprise.

Table 4.

T-test results on the frequency of emotion use in conspiracy and debunking videos.

Variables	Mean of conspiracy videos	Mean of debunking videos	p-value	Cohen’s d
anger	18.15 (15.89)	14.80 (12.87)	.0001	0.22
anticipation	23.60 (17.75)	20.19 (15.93)	.001	0.20
disgust	13.22 (11.72)	11.19 (10.59)	.004	0.18
fear	28.22 (22.76)	23.41 (18.89)	.0002	0.22
joy	15.37 (13.02)	13.26 (11.94)	.007	0.17
sadness	19.70 (16.81)	16.25 (14.07)	.0003	0.21
surprise	10.90 (9.01)	9.29 (8.08)	.003	0.18
trust	33.50 (25.92)	27.84 (22.30)	.0001	0.22

Note. Standard deviations are presented in parentheses.

Figure 1.

Emotion use in conspiracy videos and debunking videos.

Our results demonstrate how the emotion-as-frames model (Nabi, 2003) applies to emotional narratives in conspiracy and debunking messages on social media. Consistent with previous literature on the nature of conspiracies (Bale, 2007; van Prooijen, 2018) and the prevalence of negative emotions in conspiracies on text-intensive social media platforms (Fong et al., 2021; Gerts et al., 2021), trust (including distrust) and fear were commonly used in conspiracy theories on YouTube.

Emotion use is contingent on conspiracy issue framing

When comparatively breaking down the use of emotions in conspiracy versus debunking videos along with different conspiracy issue framings (RQ3), an interesting pattern emerged. As Figure 2 shows, when the issue framing of a COVID-19 conspiracy is related to geopolitics, such as claiming state actors using COVID-19 as a bioweapon, conspiracy videos tend to use more emotional words compared to debunking videos. This is also true when the issue framing of a COVID-19 conspiracy is about modern technology. Differently, when the issue framing of a COVID-19 conspiracy is related to distrust of social and political elites (e.g. Bill Gates, Judy Mikovits, and QAnon), debunking videos used slightly more emotional narratives than conspiracy videos. This contrasting pattern suggests that when message campaigns aim to use emotional narratives to debunk conspiracies, they need to attend to the variations in how the use of emotion is contingent on the issue framings of conspiracies.

Figure 2.

Emotion use in conspiracy videos and debunking videos by issue framings.

The dilemma of emotion uses for debunking videos on user engagement

We found mixed evidence for H1. Conspiracy videos received significantly less views compared to debunking videos (β = −1.03, p = .03). However, they received more likes from users than debunking videos (β = 0.015, p = .04). Examining the interaction between emotion use and the type of video on user engagement, we found a dilemma in the use of trust-related emotions for debunking videos (RQ4). As shown in Figure 3, for conspiracy videos, the use of trust-related emotions in videos is associated with more views from users (blue line in the right panel). However, for debunking videos, the use of trust-related emotions in videos is associated with fewer views (red line in the right panel). This pattern is the opposite when it comes to the number of dislikes from users (left panel). For conspiracy videos, the use of trust-related emotions is not significantly associated with the number of dislikes (blue line in the left panel). Yet, for debunking videos, the use of trust-related emotions in videos is associated with fewer dislikes (red line in the left panel).

Figure 3.

Use of trust-related emotions and user engagement with conspiracy and debunking videos.

Therefore, our findings reveal a dilemma facing the debunking message design as we observed contrast between how the use of trust emotions is associated with the number of views versus the number of likes. On the one hand, if a debunking video wants to reach a larger audience (i.e. more views), it needs to use fewer emotional words related to trust. On the other hand, the use of trust words is associated with fewer dislikes of the videos (i.e. more favorable attitudes). In a word, the use of fewer trust words, though might boost views, could be associated with less favorable attitudes (i.e. more dislikes). Our finding of how trust-related emotion can be associated with various user engagement metrics in opposite directions poses a challenge for misinformation correction as the correction needs to trade-off different types of user engagement metrics.

Discussion and conclusion

How our findings shed new light on the role of emotion in conspiracy theories and debunking messages

Grounded in the emotion-as-frame model and the classic literature on conspiracy theories, our article revealed that both conspiracy and debunking videos on YouTube about COVID-19 carried more emotional frames related to fear and trust than other emotions, such as joy or disgust. While conspiracy videos used fear and distrust to undermine the scientific authority in established institutions and to fuel a culture of paranoia, debunking videos called for citizens to acquire scientific knowledge related to COVID-19 and to inspire people to mitigate fear aroused by conspiracy theories. We further demonstrated that conspiracy theories conveyed more emotional cues than debunking videos in general, and especially trust and fear-related emotions were utilized to a greater degree by both types of videos. When breaking down the use of emotional frames by the issue framing of conspiracy theories related to COVID-19, we found that when country actors or modern technology were the objects of blame, conspiracy videos used more words related to fear and trust than debunking videos. However, when social and political elites were the objects of blame, debunking videos used more words related to fear and trust than conspiracy videos.

Through revealing the use of emotion in conspiracy videos and debunking videos and their relationship with user engagement, our article contributes to misinformation and emotion studies in several ways. Scholars of misinformation have extensively examined the spread of misinformation, but only a limited amount of research studied the role of debunking messages in organic settings, such as social media (Pasquetto et al., 2020). Moreover, most studies have focused on analyzing misinformation on text-based platforms, such as Twitter and Facebook, even though conspiracy theories are prevalent on video platforms. Building on the classic framing theory, especially the emotion as frames model (Goffman, 1974; Nabi, 2003), our study examined YouTube conspiracy and debunking videos to provide novel insights into misinformation research by uncovering how emotional frames are used not only in conspiracy theories but also in debunking messages that counterargue conspiracy theories. Different from most studies that only examine the frequency of how emotions are used on social media, we took a step further to conduct a theme analysis to uncover the contexts of how emotions are used. Our finding that conspiracy theories use more emotion related to fear and trust reinforces the existing literature on the paranoid culture of conspiracy (Bale, 2007). We also bring new knowledge to this literature by revealing that emotion use is contingent on the issue framing of conspiracies. We demonstrated that emotional frames are contingent on who to blame in conspiracy theories and who to protect in debunking messages on social media platforms. This more nuanced understanding of the heterogeneous use of emotions in conspiracy theories and debunking messages advances our knowledge of how emotion and issue framing can interact to engage the public on video platforms such as YouTube.

Implications for misinformation correction on social media

This article revealed a dilemma facing debunking video design: emotion use has contrasting associations with various types of user engagement. While more use of emotions such as trust-related narratives is associated with fewer views, it is associated with more likes. This dilemma, the contrasting role emotion can play in various user engagement metrics facing debunking video design, offers both theoretical and practical implications for misinformation correction and public engagement on social media.

Theoretically, our findings respond to three important issues. First, many studies on debunking and misinformation correction focus on people’s attitudinal and behavior changes in the experiment setting. Absent in those studies is what our work highlights as a fundamental approach to understanding the connection between misinformation correction and people’s attention: harnessing the digital trace data to examine how the public engages with misinformation and debunking messages. As we pointed out in the literature review section, a key feature of social media is its operational model of the attention economy, where user attention helps generate traffic and revenue for content creators. Motivated by communication works that demonstrate a positive relationship between emotion and attention in the experimental setting, our works expand this exploration by analyzing digital trace data, which allows us to study attention in an organic setting. We found similar patterns in the relationship between emotion and attention that corroborate the findings in experimental contexts. This approach joins a rising number of works that utilize digital trace data (e.g. hyperlinks, user metrics) to uncover patterns about how people communicate and engage with political and science content and with other publics in digital space (Freelon, 2014; Papacharissi, 2002).

Second, our findings contribute to studies that examine emotion on social media. We found differences concerning the role of emotions in the number of views compared to likes and dislikes. This finding first responded to the conceptual differences in various user behaviors, as Khan (2017) stated in his article. He showed how passive consumption could be different from active consumption on social media; while passive consumption is often captured in user attention metrics such as the number of views, active participation requires users to take more cognitive effort by not only viewing the videos but also by expressing their opinion through clicking likes/dislikes or leaving comments. We observed that for debunking videos, the more trust-related words they used, the fewer users viewed these videos, but users expressed more favorable attitudes if they viewed these videos. This contrasting role echoes with other empirical works that show the different roles of emotional languages on various user behaviors on social media. Brader (2006) and Marcus et al. (2000) found how positive emotions could garner less attention, and we also found that the use of positive emotion languages such as trust-related words garnered fewer views on YouTube. Our results also align with recent empirical work that demonstrated the role of emotions on the type of user behaviors. For instance, Yu (2014) found that although posts with high arousal and positivity are associated with more liking on Facebook, there is no association between these emotions and the number of comments a post received.

Limitation and future work

We want to acknowledge several limitations of our study. First, our study is based on observational data. There could be other alternative explanations besides the use of emotional language that might drive user engagement. For instance, other scholarships have examined the role of gender, race, and ethnicity of the speakers of videos on public engagement (Amarasekara and Grant, 2018). Image-level factors such as the use of color (Chen et al., 2022) might also explain how users pay attention to and engage with these videos.

Second, while our study extensively focused on textual frames of YouTube videos when investigating emotions, visual dimensions of YouTube videos are also crucial to understanding how conspiracy videos manipulated persuasion as demonstrated in Chen et al.’s (2022) latest work. Future work could consider the multi-modality of videos when examining emotional frames (Kim et al., 2022). The exploration between emotion and attention in misinformation studies is just at the beginning. For future scholarship, it will be interesting to investigate how the relationship between emotion and attention might differ across various science and health issues, in addition to the COVID-19 conspiracies we studied in this article. Future work can also deploy field experiments on social media platforms to examine the causal relationship between various emotions and user engagement which can inform effective debunking strategies to correct misinformation on social media.

Supplemental Material

sj-docx-1-nms-10.1177_14614448221105877 – Supplemental material for The use of emotions in conspiracy and debunking videos to engage publics on YouTube

Supplemental material, sj-docx-1-nms-10.1177_14614448221105877 for The use of emotions in conspiracy and debunking videos to engage publics on YouTube by Sang Jung Kim and Kaiping Chen in New Media & Society

Footnotes

Acknowledgements

Sang Jung Kim and Kaiping Chen contributed equally to this article. We would like to thank Qiantong Gao for her research assistance in data collection and video classification. Kaiping Chen would like to thank the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Sang Jung Kim

Kaiping Chen

Supplemental material

Supplemental material for this article is available online.

Author biographies

Sang Jung Kim is a Ph.D. student in the School of Journalism and Mass Communication at the University of Wisconsin–Madison. She studies the interaction between technology, politics, and social identity. She explores the identities of message creators and message receivers on social media platforms—including racial identity, gender identity, and political identity—and utilizes both experimental methods and computational approaches to understand how consumers and creators of such content introduce and are impacted by biases. Her works have been published in Information, Communication & Society, International Journal of Press/Politics, and Journal of Computer Mediated Communication. Her email is: skim789@wisc.edu

Kaiping Chen is an Assistant Professor in Computational Communication in the Department of Life Sciences Communication at the University of Wisconsin–Madison. Her research applies data science and interviews to study public deliberation, science communication, and information campaigns in the United States and China. Her work has been supported by the National Science Foundation and was published in flagship journals across disciplines including American Political Science Review, Journal of Communication, New Media & Society, Public Opinion Quarterly, International Journal of Press/Politics, Public Understanding of Science, Harvard Kennedy School Misinformation Review, and PNAS, among others. For more information about her research, see . Her email is: kchen67@wisc.edu

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