Politics,Power and a Pandemic: Searching for Information and Accountability During a Twitter Infodemic

Mediatization and an Infodemic

We use the theory of mediatization to understand the cultural and social realities that led to a COVID-19 “infodemic” and the impact contemporary media have on cultural understandings of health and health care in the 21st century. Schulz (2004) defines mediatization as “the processes of social change in which the media play a key role,” and he breaks these processes into four types: Extension, substitution, amalgamation, and accommodation (p. 88–89). Extension addresses the ways that media technology “extend the natural communication capacities of human beings” beyond the time, space and expressive limits of individuals (Schulz, 2004, p. 88). Substitution addresses the ways that media “partly or completely substitute social activities and social institutions and thus change their character” (Schulz, 2004, p. 88). Amalgamation happens when media seamlessly blend with extant institutions, dissolving all boundaries between mediated activities, such as watching TV, and non-mediated activities, such as eating dinner (Schulz, 2004, p. 89). Finally, accommodation refers to the ways that non-media entities, such as politicians, change their communication strategies to adapt to new media formats, thus increasing their visibility and giving media businesses content that is “more newsworthy and conveniently formatted (Schulz, 2004, p. 89).

Although not named as such, the idea of mediatization can be traced to media ecology scholarship focusing the ways media technologies influence our social relationships with others and ourselves (McCluhan, 1964; Meyrowitz, 1985), and, in fact, our media become extensions of the human self. Humans, in effect, surrender themselves to new media technology and, as Schulz explains, become amalgamated with it to accommodate the new communication paradigms. Mediatization theory advances McCombs and Shaw’s (1974) agenda setting theory and its update, network agenda setting (Guo & McCombs, 2011a, 2011b), by showing the ways media not only changes communication paradigms, but also how it because an important part of entire social structures. Previous research on agenda setting has examined people's daily information-seeking behaviors about everything from politics to natural disasters to violent crime coverage (Guo & Vargo, 2015; Housholder et al., 2018; Ragas et al., 2014; Watson, 2017). Both media theory, as exemplified by the media ecologists, and agenda setting and other media effects models examine media as separate from social and cultural structures. However, “Media are no longer ‘outside’ society exerting a specific influence on the effect of culture and therefore on individuals. In our present media-saturated society media are inside society, part of the very fabric of culture; they have become ‘the cultural air we breathe’” (Hepp et al., 2010, pp. 223–224; Hepp, 2012; Hepp et al., 2015).

Mediatization is a useful theory for examining the phenomenon of bad health information during the coronavirus pandemic because it allows researchers a tool to specifically study the so-called “infodemic” spreading and changing cultural reactions to everything from how to prevent COVID-19 to masking to immunizations. An infodemic is “a tsunami of information—some accurate, some not—that spreads alongside an epidemic” (Ghebreyesus, 2020). The WHO's overview of the term notes, “An infodemic can intensify or lengthen outbreaks when people are unsure about what they need to do to protect their health and the health of people around them” (World Health Organization, 2021, para. 1). The WHO continues to emphasize the ways digital media grew and sustained the infodemic, which, in turn, undermined health care efforts throughout the pandemic: “With growing digitization—an expansion of social media and internet use—information can spread more rapidly. This can help to more quickly fill information voids but can also amplify harmful messages” (World Health Organization, 2021, para. 1).

The term “infodemic” was used as an umbrella term during the 2020–2021 coronavirus pandemic to refer to misinformation, disinformation, and malinformation. While many publications about the spread of bad information lump everything into the term “misinformation,” media literacy specialists use the terms “misinformation,” “disinformation,” and “malinformation,” as an antidote to the distracting and undefined accusation of “fake news.” Wardle and Derakhshan (2018) argue that the difference between the terms is in the intent of the information publisher, which includes individuals reposting articles on social media. People distributing misinformation, according to Wardle and Derakhshan (2018), believe the information is true, even though it is not; disseminators of disinformation, however, know what they are distributing is false. Disinformation “points to people being actively disinformed by malicious actors” (p. 55). While disinformation is completely false, malinformation has some basis in the truth; however, this truth is revealed or twisted to “inflict harm upon a person, organization or country” (Wardle & Derakhshan, 2018, p. 55). While Wardle and Derakhshan call this media environment “information disorder,” this article instead uses the WHO's term “infodemic” because it specifically refers to misinformation, disinformation, and malinformation about health issues.

Infodemics did not originate in tandem with the COVID-19 pandemic. There is an academic area of study dedicated to the identification, definition, and combat of infodemics called “infodemiology.” However, multiple authors pointed to the swell of misinformation, disinformation, and malinformation throughout the coronavirus pandemic, as both a new magnitude of an infodemic and a clear threat to public health (Cross-regional statement on ‘Infodemic' in the context of COVID-19, 2020; Briand, 2020; The Lancet, 2020; Understanding the infodemic andmisinformation in the fight against COVID-19, 2020; WHO, et al., 2021). While infodemics have been around long enough to spawn infodemiology as a subject area, the unique media environment and its relationship to the coronavirus pandemic spurred WHO to hold its first infodemics conference in June 2020. Attendees created a public health research agenda for infodemic management, including imperatives to understand the social and cultural contexts that lend themselves to the development and spread of an infodemic and establishing tools to derail the spread of an infodemic (World Health Organization c., 2021, para. 6–10).

COVID-19's Mediatized Social and Cultural Environs

The mediatized communication schema that created the COVID-19 infodemic worked across the social and cultural environments of both well-established media sources, such as broadcast TV, and newer sources, such as social media sites. As the ongoing proliferation of COVID-19 and its variants prompted international and domestic travel restrictions, preventative “social distancing,” and the curtailment of free association and routine business practices, it is both appropriate and necessary to address the role of social networking sites (SNS) in disseminating information (and misinformation) under pandemic conditions. In eliminating the prohibitive costs typically associated with creating and disseminating media content, SNS technology has disrupted the gatekeeping function of traditional media. This does not indicate that SNS technology has entirely eclipsed its asymmetric forbears in a totalizing fashion. Househ’s (2016) study on media dynamics during the 2014–2015 Ebola crisis found that media influenced the discussions on Twitter. During the 2009 H1N1 pandemic, Twitter users, while not immune to misinformation, tended to favor, redistribute, and cite/link content from news agencies (Chew & Eysenbach, 2010; McNeill et al., 2016). Studies of web search data have found that individuals are more likely to seek online information about a political issue or candidate after they have seen in a television ad (Housholder et al., 2018). In addition, people seek information online when their immediate social systems and media systems are uncertain and unresponsive to their information needs, as was the case during the COVID-19 pandemic (Adams, 2021; Watson, 2017).

While the idea of an infodemic did not originate with COVID-19, multiple researchers and public health specialists noted characteristics of the media environment during months when the pandemic was both new and rising in the public's consciousness as uniquely suited to creating the information tidal wave of an infodemic. Even before the pandemic swept across the globe, researchers had found that false information spread faster than true information on social media platforms (Coldewey, 2019; Dizikes, 2018; Tasnim et al., 2020; Vosoughi et al., 2018). Studies of audiences’ media biases prior to the pandemic also showed social media audiences, in particular, focus on information that is novel and negative (Coldewey, 2019; Gladstone, 2012).

Researchers also found that the cultural and social systems established on social media platforms encouraged the proliferation of false information (Pennycook & Rand, 2019). Social media provide “immediate, quantified feedback on the level of approval from one's social connections,” which encourages people to share content that will give them the strong and immediate feedback from their online social contacts (Pennycook et al., 2020, p. 777). Westerman et al. (2012) demonstrated that a user's perceived competence in a given subject matter may also correlate with their follower/follows ratio (Westerman et al., 2012). In the case of tweeting health messages, professional users, who were perceived as experts on a matter, with a high follower count were considered credible compared to a layperson with several followers. In the case of retweets, proximity to a source equaled trustworthiness of health messages shared; layperson users benefited from a high follower count and professional users from a low follower count (Lee & Sundar, 2013). In addition to follower counts and the immediate gratification of a “like” or a follow, social media mixes news content with content where accuracy is irrelevant, such as baby pictures; because of this, “people may habituate to a lower level of accuracy consideration when in a social media context” (Pennycook et al., 2020, p. 777).

Misinformation and Social Networks

While governments released muddled messages about COVID-19, people turned to social media for answers, and the misinformation found there filled in needs for reassurance and answers (Adams, 2021). However, SNS platforms, including microblogging sites like Twitter, complicate assessments of message and source credibility. To begin with, Twitter exerts a general dampening effect on the perceived credibility of news content, even when this content is generated and (initially) disseminated by traditional media sources (Schmierbach & Oeldorf-Hirsch, 2012). Beneath this deleterious effect, however, Twitter's unique affordances produce variation in message credibility. Perhaps owing to the frequency of information turnover, tweets referencing trending and/or breaking news topics are more likely to be perceived as credible (Shariff et al., 2017). Perceived message credibility is also positively correlated with number of “retweets”; Twitter users are likely to perceive high-virality tweets as more believable and, following from this, are more likely to further disseminate them (Kim, 2018; Lee & Oh, 2017). It is unclear whether this cascading virality is always available to news agencies, though, with some studies producing an affirmative result (Kim, 2018), and others negative (Li & Sakamoto, 2015).

Given the affordances Twitter and other SNS platforms offer to social movements, it is also worth exploring the relationship between discourse on these platforms and online/offline political engagement and cooperation with government imperatives. This relationship becomes especially relevant in cases where a public good demands the sublimation of individual liberties. In the case of a pandemic, for instance, an entrenched culture of individualism and individual health solutions is likely to disrupt notions of collective responsibility for the wellbeing of vulnerable populations (Stephenson et al., 2014). Citizens’ community-mindedness may encourage cooperation with social distancing procedures, but they are also prone to distrust of government intentions and suspicion of decision-making processes which exclude and/or fail to educate them (Baum et al., 2009). Faith in governance is predictive of compliance with community mitigation strategies (Blendon et al., 2008) and the adoption of select health- protective behaviors such as face mask wearing, hand washing, and vaccinating (Chuang et al., 2015). Indeed, pandemics may catalyze political mobilization by throwing into sharp relief the tensions between governmental authority, civil society, and private industry (Densham, 2006).

The Lancet's Infectious Diseases editorial board (2020), noted that poor information dissemination practices by experts, including scientists, public health experts, and governments, led directly to the COVID-19 infodemic. Those poor broadcasting practices included distributing scientific information before it had been thoroughly peer reviewed and the practice of governments putting political interests ahead of evidenced-based decision-making. “Consequently,” the editorial board wrote, “incoherent government messaging and reversals in recommendations on the basis of newly emerging evidence…can be misconstrued as incompetence…Such miscommunication is not helped by the mass media, which are often guilty of favoring quick, sensationalist reporting rather than carefully worded scientific messages with balanced interpretation” (The Lancet Infectious Disease, 2020, para. 2).

Media companies and health officials tried to control the social networks’ distribution of bad information during the COVID-19 pandemic. WHO recognized big tech companies’ role in stemming the infodemic tide early in the pandemic and brought together 30 of Silicon Valley's tech communication companies to discuss how to “build support for WHO to keep people safe and informed about COVID-19” (World Health Organization b., 2021). Social media companies, including Facebook, Twitter, and YouTube, indicated that they had a variety of strategies for flagging or removing false information about the pandemic as early as March 2020 (Chakravorti, 2020). However, some of these same companies, Facebook, along with Google, Apple, and Amazon—enabled disinformation networks to form and rise to the top of online search results (Adams, 2021, para. 4–5).

Hashtagging an Infodemic

The mining of SNS discourse for news content allows its participants a greater measure of influence over news routines and production than that enjoyed by the relatively passive consumers of print and broadcast news. Twitter hashtags, designed to streamline discourse on the platform, are particularly well-suited to this form of exchange. Activists affiliated with such diverse causes as Black Lives Matter, #MeToo, and the Arab Uprisings have mobilized hashtags to frame their issues of choice and bolster offline organizing while also “feeding pre-framed ideas and stories to mainstream media outlets” (LaPoe et al., 2020, p. 3). Under favorable conditions, a hashtag may even internationalize local movements, as occurred with #MeToo (LaPoe et al., 2020).

Hashtags—key words and phrases that social media influencers use to organize posts, garner attention, enter into conversations, and spread information—operated to both inform and disinform publics about COVID-19. While WHO “increasingly relied on non-traditional media from Facebook to WhatsApp to keep people safe from coronavirus,” media literacy nonprofit The Poynter Institute united more than 100 fact checkers from around the world under the umbrella #CoronaVirusFacts (Corona VirusFactsAlliance, N.D.). Finland classified social media influencers as essential workers during the pandemic and kept them informed with facts through the official #faktaakoronasta (facts about COVID-19) newsletter. In spite of non-profit and government health information leaders concentrating on trying to improve social media information, Bridgman et al. (2020) found a direct relationship between people's consumption of news media through traditional sources and through social media. In fact, misinformation and disinformation circulated on Twitter, specifically, lead to “behaviors and attitudes that potentially magnify the scale and lethality of COVID-19” (p. 1).

While governments and NGOs were organizing influencers and fact checkers to stem the tide of misinformation and disinformation, individual users found hashtags of their own to help fulfill their need for information and reassurance. The Pan American Health Organization (Understanding the infodemic and misinformation in the fight against COVID-19, 2020) and the Center for Health Informatics at the University of Illinois found that almost 550 million tweets sent during March 2020 were marked with the hashtags: #Coronavirus, #Covid19, #Covid-19, #Covid_19, #FlattenTheCurve, #Pandemic. Earlier, Maurer and Holbach (2016) noted search terms or hashtags people use in times of uncertainty, such as the COVID-19 pandemic, may be used to indicate the public's primary concerns and agendas. This article builds on this research by examining tweets associated with the terms “COVID-19” and “Coronavirus” to reveal Twitter users’ most-sought-after information, their conversations as well as potentially their interaction with misinformation and disinformation. Therefore, our research questions include:

RQ1: “What Words Emerged in Conjunction with #COVID19 and #Coronavirus in Early March 2020?”

Our analysis found seven salient terms, in terms of usage, connected #COVID-19” and “#Coronavirus.” The terms included “Cases,” “People,” “Trump,” “Health,” “New,” “Deaths,” and “Spread.” #Covid19 and #Coronavirus hashtags appeared to allow Twitter users in the early weeks of the U.S. pandemic to discover information and ask elected officials to become the information leaders they needed. Within these words, the power of politics was clear. People were uncertain and afraid, and they needed help to understand the scope of the pandemic's impacts on their lives and the broader social and cultural structures they relied on for information. They turned to social media to fill their need for information.

#COVID19 and #Coronavirus

An examination of our first base hashtag #COVID19 shows people disseminating data but not always attaching sources to that data, leaving users to rely on social media culture's reliance on likes, retweets, and the apparent immediacy of data to judge its accuracy. The term “#Covid19” appeared in the data 1.66% (N = 416,584) of the time. Analysis of the term within the word tree revealed that the hashtag frequently appeared in tweets alongside “#coronavirus” and, less commonly, “#COVID2019,” as well as in phrases such as “surge of,” “symptoms of,” “spread of,” and “case/cases of.” When not followed by a secondary hashtag, it typically appeared before words such as “cases” or “outbreak.”

Many of these tweets provided information, but they did not always provide sources for that data, which meant people relied on their mediatized social and cultural networks to present truthful information. For example, this tweet gives data about the outbreak in Iran, but the wording—particularly the use of “shocking”—appeared to plays to reader's uncertainty through its data interpretation of events, which, as was explained earlier, is why many people seek information on the internet:

The numbers for #coronavirus in #Iran continue to be shocking In the past 24 h 1178 new cases Total cases 16169 135 dead in the past 24 h, total dead 988 In the last 4 days over a 100 people a day have died of and over a 1000 people a day have contracted #COVID19 9 (Arouzi, 2020).

Cases

Twitter users particularly sought information on the pandemic's spread and its potential to impact and harm their individual health. The term “cases” appeared in the data .77% (N = 200,459) of the time. Oftentimes, users paired the word “cases” with tags such as “#coronavirus” or “#covid19.” It also appeared in phrases such as “new cases” or “confirmed cases.” “Cases” was normatively followed by “in,” denoting a geographic location, or “of” as in “of coronavirus.”

Two examples of tweets containing this hashtag are included below. As noted in our discussion of people who used #COVID19 alone, these tweets often neglected to provide a reliable source for their data, as is the case in the second tweet provided here. The first tweet author provides a link as a source.

MORE THAN 600 CASES: NY Reports First Two #Coronavirus Deaths and Governor Cuomo Says “TENS OF THOUSANDS IN NY” Likely Have It. https://t.co/G7goRmOoFQ (Higgins, 2020a)

People

The term “People” appeared in the data 0.73% (N = 190,045) of the time, commonly occurring after “of” phrases including “gatherings/groups of,” “number/numbers of,” and “hundreds/thousands of.” It also appeared in “the” phrases such as “all the” and “of the.” The phrase “young people” was less common, but nonetheless occurred with notable frequency. “People” was most often followed by “and,” “are,” “have,” “in” (sometimes, but not always denoting a location), “of” (typically denoting a location), “on,” “to” (stop, stay, go, etc.), “who” (have, are, etc.), “will,” and “WITH.” Two examples of tweets containing this term are included below. Like previous search terms, the results of #People returned testing and case counts from across the globe. However, this search also yielded information about how people could see and interact safely with others through previously established in-person social and cultural networks:

India is testing 6.8 samples per million people. South Korea is testing 4831 people per million, China, 2820 per million and Bahrain 6165 people per million. India must ramp testing to prevent full outbreak of #coronavirus South Korea is testing 12,000 people every day (Sharma, 2020).

Trump

The then President of the United States appeared in the analysis in two ways: people tweeting directly to him or people tweeting about him. To understand the salience of both actions, we have broken out his name from his Twitter account. When discussing the president, the term “trump” appeared in the data .73% (N = 190,045) of the time. As one may imagine, it often was connected to his name typically co-occurred with “Donald,” and “President” and “The.” Less often, it followed “with,” “to,” “of,” “by,” and “a.” “Trump” typically preceded occurrences of “'s” (as in response, failure, fault, etc.), “2020,” “administration,” “and,” “is” (is a threat, is a racist, is a disgrace, etc. OR is my President) or “Supporter” (or “supporters”). Two examples illustrating the use of “Trump” are below.

President Trump's decision to declare a national emergency over the #coronavirus pandemic is the right call. It will free up much needed resource and allow for a more flexible and aggressive response (Graham, 2020).

Health

The term “health” appeared in the tweets as much as “realdonaldtrump,” proportionately at .39% (N = 101,531) considering all other terms within the data. Twitter users often groups the word “health” “crisis”, “emergency”, “care’ and “public.” This tweet indirectly references the infodemic rising in conjunction with the pandemic and encourages people to seek reliable health information sources, such as the United Nations Children's Fund (UNICEF):

Misinformation during a health crisis can leave us unprotected, spread fear & panic. Knowing the facts is key to protecting yourself & your family. Visit the @UNICEF website for reliable information on how to talk to children about #coronavirus: https://t.co/jeEBZXBeWn #COVID19 (Chopra, 2020).

New

“New” also appeared in the same proportional amount as “@realdonaldtrump” and “health” .39% (N = 101,531) of the time. The term “new” became grouped through tweets with “coronavirus guidelines”, “cases” and “research”. The term “new” also updated users on the latest outbreaks and governments response to the crisis. The term “new” shows information-seekers adapting quickly to the norms of their new mediatized networks. Twitter, in particular, relies on new and immediate information to fill its platform, and tweets purporting to reveal the newest research or case counts rose to the top of the dataset.

New research into #coronavirus shows that they may also invade the central nervous system inducing neurological diseases. SARS- CoV infection has been reported in brains from both patients & experimental animals, where the brainstem was heavily infected https://t.co/SLTXCew53H (feedingtubepaul, 2020).

Deaths

This search term, perhaps more than any other, revealed information-seeker's fear of the virus and their need for reassurance through their information-seeking. The term “deaths” was used in the data .32% (N = 83,308) of the time. Although there were many ways “deaths” was used on Twitter, NVivo was able to relate deaths to the coronavirus. The most common way included “coronavirus deaths”, “COVID-19 deaths” and “deaths from covid19,” such as the examples below.

#CoronaVirus Outside of China - 101,844 cases and 3,948 deaths. To date a total of 7,174 deaths and 182,725 total #covid19 cases have been confirmed worldwide. #CoronaVirusOutbreak https://t.co/7B4F8EH8nf (Higgins, 2020b)

Spread

Twitter users mentioned spread .30% (N = 78,101) of the time, when examining all other words within the data. Users noted “prevent the spread”, “stop the spread” and “spread of coronavirus,” when referencing the term.

The EU will close all external borders for 30 days starting tomorrow at 12:00 to prevent spread of #coronavirus. European countries on lockdown as of 16 March due to #COVID19: - Italy - Spain - France - Czech Republic (Global Health Strategies, 2020).

RQ2: What Conversations Related to #Covid19 or #Coronavirus Were More Likely to Have Information with a Political Motivation Rather Than Public Health Promotion?

Our political activism-themed conversations revolved closely around users’ either criticizing the U.S. government's response to the pandemic or, as quoted above, people voicing their on-going support for former President Trump. A statement on the COVID-19 infodemic co-authored by 13 countries noted secondary effects of the infodemic that could destabilize multiple countries, including “[enabling] the spread of disinformation, fake news and doctored videos to foment violence and divide communities” (Cross-regional statement on ‘Infodemic' in the context of COVID-19, 2020). For this reason, the countries argued “it is critical states counter misinformation as a toxic driver of secondary impacts of the pandemic,” which include “the risk of conflict, violence, human rights violations, and mass atrocities” (Cross-regional statement on ‘Infodemic' in the context of COVID-19, 2020). In addition, the conversations circulating around former President Trump and the U.S. government's response to the COVID-19 pandemic suggests social media as a significant avenue for influencing public policy on significant issues related to healthcare and potentially curbing infodemic-related misinformation and disinformation, in particular.

While Facebook and Instagram, which have the same ownership, have the most defined policies of the major U.S. social media platforms and apps when it comes to public health disinformation and misinformation, they are still relying on “third-party fact-checkers and health authorities flagging problematic content” (Chakravorti, 2020, para. 6). Several researchers have noted that flagging COVID-19 infodemic content has been particularly problematic because thought leaders ranging from former U.S. President Trump to computer security systems developer John McAfee to billionaire entrepreneur Elon Musk shared false information with their millions of followers (Chakravorti, 2020; Sweeney, 2021). Trump was banned for life from Twitter in January 2021 after Trump supporters stormed the United States Capitol Building in a poorly organized and failed attempt at a coup (Twitter, Inc., 2020). Prior to that ban, though, “he was singularly responsible for mainstreaming COVID-19 disinformation in this country and within our government” (Sweeney, 2021, para. 16).

RQ3: What do the Subconversations Related to #Covid19 or #Coronavirus Reveal About Twitter Users’ Healthcare Concerns and Needs in the Early Days After the Pandemic Became a major Part of the Public Conversation in the United States?

The overall terms emerging from this study of COVID-19 and Coronavirus suggest early-March 2020 Twitter conversations regarding the pandemic helped individuals manage their own uncertainty as government and health leaders released seemingly conflicting data. This is in line with previous research examining social media chatter surrounding Ebola and Zika virus outbreak (Dalrymple et al., 2016; Gui et al., 2017). In a time of global crisis that encapsulates chaos and panic, it is hardly surprising that most conversations and information exchange surrounds ways to manage the situation. As the COVID-19 pandemic continues to move through phases with vaccines and virus variations, media literacy about the parallel infodemic will be just as necessary for strengthening mediatized communities seeking and spreading information and misinformation, and for amplifying the voices of reliable governmental and NGO public health campaigns.

Limitations and Future Research

This study focused on a short period of time at the beginning of the COVID-19 pandemic in the United States, specifically. The infodemic monikers identified here varied slightly from ones that were found by Rovetta and Bhagavathula (2020) and the Pan American Health Organization (Understanding the infodemic and misinformation in the fight against COVID-19, 2020). Those studies and this one covered the early part of the pandemic in a variety of depths. Future research should examine infodemic hashtags – and terms associated with them – closer to the end of 2020 when the United States and European countries started giving vaccines emergency approval; and through the first half of 2021, considering as the various vaccines rolled out, some were halted temporarily to investigate rare instances of potentially severe side effects. In addition, infodemic monikers related to 2021 COVID-19 variations, which became dominant transmission strains, would show whether internet citizens continued to seek reassurance and control over the virus or whether they had turned to other topics as audiences experienced mass pandemic burn out.

While supplementing the current methodology with social network analysis and a quantitative content analysis would provide more confidence in our findings, our research utilizes a unique methodology with NVivo software and provides a good overview in an initial investigation of social media chatter at the beginning of the global pandemic. Future research should continue this work in this area and examine further by comparing the differences in conversations based on geographical location from where the tweets are originating and other demographic factors. Triangulation of methodologies may shed light on some other significant items. Finally, investigating a wide variety of social media platforms, including more visual platforms, such as TikTok, YouTube, and Instagram, may further inform the findings.