[Marketing] Fallacies Involving Virality

Introduction to research backed ways to induce rapid content sharing

It isn’t luck. It’s not magic. And it’s not random. Going viral is a science. Sharing content has existed as far back as prehistory where people would exchange stories, and songs with each other orally. The proliferation of the personal computer and the internet further enabled people to send things to each other, widely forwarded emails, are some of the first instances of internet virality and predate most social networks. More recent instances of virality include Thavalakai's Little Superstar, and the ALS Ice Bucket Challenge which stemmed from a small group of friends in Boston and bloomed into 17 million people participating and $115 million raised for the ALS Association. Furthermore, social media sites, through additional functionalities, made possible by way of technological advancements such as video sharing, have facilitated a phenenomon called internet virality. A specific phenomenon within science, medicine, and or technology, that the public holds misconceptions of is memetics also known as the viral phenomenon, the phenomenon where people share content with other people and in this instance over the internet. A better understanding of internet virality is vital to not just the public but particular groups such as independent artists and social media influencers who could benefit from heightened exposure at marginal costs.

Misconceptions

First, a group that has been interacting with social media their entire lives are university students. They have experienced countless viral trends and through their own social media accounts have figured out ways to induce virality. However, most are unfamiliar with the fact that there are actual formulas, specifically the virality formula, that can model specifically how content goes viral. A few months ago, a survey was administered to a group of college students that found that 2/10 students could define a viral coefficient. A conclusion could be made that this is indicative of widespread public misconception regarding virality as most of the survey participants being digitally native. This implies that most of the public who isn’t digitally native lacks knowledge of the core aspect of virality. Students have a unique perspective on virality, they may know a lot more about it than the typical researcher by just existing and engaging with various media throughout much of their entire lives. A common misconception is that it is impossible to create the exact conditions under which content could go viral. However, both the generations that have grown up with social media, and some researchers believe that it actually may be possible.

Second, experts have also acknowledged the misconceptions regarding virality. Alyssa Rosenberg is a Washington Post writer who focuses on the intersection of culture and politics, she describes how it’s easy to ignore the labor involved in “viral” content: A “virus” spreads indiscriminately, as opposed through the work of its creators. As Rosenberg says, a misconception about virality is that it is beyond the creator’s capability to make their content go viral. However, as will be touched on later, researchers have outlined factors that could increase both the content’s ability to fulfill a want and its likelihood of imitation, and therefore it is possible to actively make content go viral.

Other reasons for the misconceptions around virality are seeded through misunderstandings of research. Lars Kai Hansen and Adam Arvidsson are applied computer science and media studies researchers respectively at the Technical University of Denmark. They determine that their conclusion is that positive information is diffused more appears to be in contrast with classic theory of diffusion in news media (Galtung and Ruge, 1965) emphasizing negative effect as promoting propagation. Another example of a misconception around virality is that negative news is likely to go viral. While this is true within risky contexts, unlike other misconceptions, this misconception is seeded by research but stems from not characterizing the type of content. In non-news content, as affirmed by Hansen and Arvidsson, and will be expanded on later, negative news is unlikely to go viral, however with news-related content, negative news is prone to going viral. Overall, some of the elements that cause misconceptions have been established.

How it works

Before proceeding, it’s imperative to examine what happens when content is first viewed by the user. Ines Brusch and Thomas Reichstein are researchers from the Institute of Business and Economics, within Brandenburg University of Technology Cottbus - Senftenberg, they conclude that the decision‐making process is basically divided into two stages. In the first decision stage, individuals decide whether content should be considered. When individuals agree to view the content, they decide in the second stage whether they want to interact with it … The framework conditions themselves are categorized into receiving framework conditions (e.g., Who sent this content?), created framework conditions (e.g., What is in the heading of the content?) or random framework conditions (e.g., Is his topic currently a trend?). When the audience first sees content, there's a process where the person viewing it decides whether or not it should be considered, then a proceeding process where they decide if they should interact with it. In addition to the content, and the viewer’s goal, framework conditions are extremely important as they leverage credibility to allow the person viewing the content to prioritize whether it's important and if they'd like to interact with or not. In terms of virality, something being sent from a friend has a positive effect when the receiving user evaluates whether or not to interact with the content, however, according to Brusch, regardless of how close the relationship of the sender to the viewer, if the content is poor, the content will not be interacted with at all. Lastly, Brusch’s research establishes why virality is so effective.

Subsequently, it’s crucial to consider the definition of virality. Tony D Sampson is a British theorist who focuses on media technology, he describes virality as “forces of imitation–suggestibility that spread through a network and that by way of a convergent imitative encounter captures the fragments of another’s desire.” Essentially, as content travels through a network, two things accelerate its spread: its ability to fulfill the audience’s want and it's ability to be imitated. As a result, it could be discerned that this pair of components form the basis of virality.

Next, it’s important to have a way to measure virality. In fact, Sophia Bernazzani a senior marketing manager at Hubspot explains the model for virality:

In rough language, the model states that the total number of new conversions (purchases, views etc.), C(T) is given by multiplying the number of customers at the beginning, C(0) with the viral coefficient, K

C(0)K=C(T)C(0)* K=C(T)

The virality coefficient is the likelihood of someone to share something, and get a desired action from another person such as a view or a like. Blockers, for example, a content geofence or a paywall and other things may hinder the content’s ability to be seen and therefore reduce its viral coefficient. More so, since the viral coefficient gauges the audience’s want and the content’s ability to be shared, the virality coefficient could be used to quantify Sampson’s characterization of virality. Overall, the virality coefficient allows the virality of content to be surveyed and represented in a variable.

Further, there exist ways to increase the virality coefficient. Jonah Berger and Katherine Milkman are marketing and operations researchers at the University of Pennsylvania, they describe how “consistent with our theorizing, online content that evoked high-arousal emotions was more viral, regardless of whether those emotions were of a positive (i.e., awe) or negative (i.e., anger or anxiety) nature. Online content that evoked more of a deactivating emotion (i.e., sadness), however, was actually less likely to be viral.” As Milkman and Berger discovered, emotions play a role in increasing the virality coefficient with some emotions such as sadness stifling it and others linked to high arousal increasing the likelihood of something being shared, which is also consistent with Sampon's definition of virality, that states that content that captures our desires are more likely to go viral. Consequently, Berger and Milkman’s research presents one way to raise the virality coefficient, by producing content that avoids or evokes certain emotions.

Conversely, it’s possible to decrease the virality coefficient of content. Gerard Tellis is the director of the Institute for Outlier Research in Business at USC, states that “Information-focused content has a significantly negative effect on sharing, except in risky contexts”. Informative posts are typically detrimental to virality unless the context includes risk, where information is dire and provokes a sense of urgency. In fact, this is also compatible with Sampson’s definition of virality that states something is likely to go viral if it fulfills a human need or is easy to share. A practical example of this could be seen in Boston University’s subreddit. In the subreddit’s ten-year history, the top post among over 12,000 posts is merely seven days old and involves news around testing. It could be assumed that details regarding testing are serious to students, therefore it corresponds with Sampon’s definition of virality which states that if something fulfills a human want it will be shared more. This could be explained through access to information having the potential to keep people safe and imitating others, in this case, through sharing the content further, is easy since it’s extremely relevant and helpful to the people receiving it. In the end, there exist ways to decrease the virality coefficient that are important to be conscious of.

Who should care

Adjacent to the previous points, it’s essential to layout specific groups that would benefit from an increased understanding of internet virality. According to Alphabet's fourth-quarter earnings report, the total revenue for Youtube Ads in 2020 was 15 billion in annual ad revenue. The revenue of one platform, Youtube, alone being greater than a more traditional media conglomerate such as CBS's with its $14.7 billion in annual revenue, not only gives an idea of the magnitude of proceeds involved in user content but illustrates how the tides are shifting towards user-generated content on internet platforms such as Youtube. Additionally, considering a figure from Mic, a media company based in New York that caters to millennials that states “mega-stars like Selena Gomez and Kylie Jenner pull in $800,000 and $1,000,000 per post, respectively (they both have more than 100 million followers), while those with the smallest following on the list (a mere one million) charge $1,300 to $3,000 per post.” The gap between the pay-per-post for megastars and smaller influencers leads to the assumption that if influencers increased their follower count they could possibly yield higher commissions for posts. On the influencer and content platform side, increased exposure results in increased revenue.

Next, another particular group within the general public that could benefit from an understanding of virality are independent artists. In this quote by Paige Leskin a tech reporter at Business Insider, Leskin describes how “‘Old Town Road’ was intended to go viral, with catchy lyrics and ‘quotable lines that people want to use as captions’”. Through composing and releasing Old Town Road in such a way that it sticks to the listener’s mind, and is easy to share, Lil Nas X actively implements some of the virality producing techniques and research that were touched on previously. Ultimately, Lil Nas X approach as an independent artist garnered a lot of exposure at very little cost.

Likewise, there are specific benefits to not holding the misconception that virality is random. Lilian Weng is a Complex Systems and Networks researcher at OpenAI, in her paper published in the journal Nature, it's demonstrated that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. While this won't increase virality directly, knowledge of the amount communities the content imbues unlocks the ability to gauge if the content has a chance at being viral early on. To conclude, through being able to see which content will go viral, content creators are able to make the determination to course correct and prevent wasted resources on content producing approaches that may not lead to virality.

Correspondingly, there are more practical examples of research around virality increasing how much content is shared. Berger and Milkman previously introduced in this article decide to apply their research to marketing and found that while marketers often produce content that paints their product in a positive light, our results suggest that content will be more likely to be shared if it evokes high-arousal emotions. This is beneficial because as opposed to a content creator spending resources on content that simply casts them a positive light, Berger and Milkman not only eliminate a misconception but outline how it's comparitvley ideal to focus on provoking emotion if the content creator's goal is virality. Not to mention, as a consequence of creating content that provokes emotion, it's more likely for the byproduct of releasing the content to be an ever-extending tree branch of sharing, by means of creating an ideal framework condition. As outlined by Hansen and Arvidsson, the power behind this is rooted in the added credibility of the content being introduced friend to friend rather than advertiser to a friend, thus increasing the amount of interaction with the content. Abandoning misconceptions that virality is random or something that can’t be actively influenced could unleash the power of virality and lead to content that has far more reach.

In addition, in terms of practical examples of benefits, in 2019 there was a dance challenge that took over the country. Dan Adler a staff writer at the Vanity Fair described how it started with a few faint, unmistakable banjo plucks, and it’s still going. After being quietly and independently released in December and picking up traction as an unfailing meme, Lil Nas X’s “Old Town Road” is now distributed by Columbia Records and has been No. 1 on Billboard’s Hot 100 for the last 11 weeks. Other experts on virality such as Leskin of Business Insider have concluded that Lil Nas X propelling himself from relative obscurity as an artist to having a billboard song of the year could be attributed through his understanding of internet virality. To dig deeper, consider Montero Hill’s background as an internet personality, being able to garner six-digit followers for his twitter meme page that preceded his song Old Town Road, also take into account how his song was paired with a dance challenge which made it easy to imitate. The results of Old Town Road are emblematic of how an understanding of virality could benefit artists through pulling in exposure at marginal costs. In the end, it’s not a one-off incident, although it wasn’t designed for virality (as it wasn’t explicitly encouraged to be imitated), and it wasn’t created by an independent artist, Miley Cyrus’s Party In the USA and the parody videos it spurned, further support the importance of virality in terms of generating exposure for artists.

Yup, it's still pretty random

In contrast of everything that’s been stated up until this point, virality is in someways random. Robert Wayne is President of Wayne Communications and a former contributor to Forbes. He stated that Engineering social epidemics is a fantasy. Outside of a vacuum such as those utilized in research studies, it would be impossible to engineer virality because that would require perfect conditions that assumably can't even be accurately measured at this current moment. In fact, implementing these techniques won’t be the panacea that makes content go viral. Especially when you consider changes to algorithms of major platforms that make virality elusive. However, as illustrated by Berger, Weng, Milkman and many of the other researchers featured so far, it’s safe to assume that implementing these techniques could increase your chances of going viral and at the very least increase engagement.

My experience with virality

Finally, while I’ve never gone viral, online marketing has always been a very useful skill. My initial venture into online growth was in 6th grade when I started a Youtube channel to show other players of a particular Facebook game how to get past harder levels. I noticed how I was able to increase the views on a video with stagnant viewership from roughly 10,000 to roughly 80,000 by tagging it more, therefore allowing it to gain more exposure through being recommended more often. Based on my experiences with increasing engagement, going viral involves trial and error, forming a hypothesis, experimenting, noticing patterns, implementing those patterns to gain more engagement, forming a hypothesis, and experimenting again etc. The point of this paper wasn’t to just to be an introduction to virality encouraging techniques but to dispel the notion that virality is random and enable readers to experiment and discover their own patterns, to then implement and expand on.

Concluding

In conclusion, a specific phenomenon within science, medicine, and or technology, that the public holds misconceptions of is memetics also known as the viral phenomenon, the phenomenon where people share content with other people and in this instance over the internet. A better understanding of internet virality is vital to not just the public but particular groups such as independent artists and social media influencers who could benefit from heightened exposure at marginal costs. The conditions of virality can be manufactured through provoking emotions, focusing on positive news or sensitive negative news, and overarchingly, achieving a human want while being easy to share. Furthermore, the points touched on in this paper, simply reevaluate a time tested phenomenon in a modern context. On one hand, the things we know about virality might differ as time progresses. If you pause and consider how the way we see advertisements is different, the way we listen to music has changed, and social media platforms share individual posts differently than before. On the other hand, one thing remains constant, the immense value in fulfilling a human want while also being easy to share.

/boston

Introduction to research backed ways to induce rapid content sharing

It isn’t luck. It’s not magic. And it’s not random. Going viral is a science. Sharing content has existed as far back as prehistory where people would exchange stories, and songs with each other orally. The proliferation of the personal computer and the internet further enabled people to send things to each other, widely forwarded emails, are some of the first instances of internet virality and predate most social networks. More recent instances of virality include Thavalakai's Little Superstar, and the ALS Ice Bucket Challenge which stemmed from a small group of friends in Boston and bloomed into 17 million people participating and $115 million raised for the ALS Association. Furthermore, social media sites, through additional functionalities, made possible by way of technological advancements such as video sharing, have facilitated a phenenomon called internet virality. A specific phenomenon within science, medicine, and or technology, that the public holds misconceptions of is memetics also known as the viral phenomenon, the phenomenon where people share content with other people and in this instance over the internet. A better understanding of internet virality is vital to not just the public but particular groups such as independent artists and social media influencers who could benefit from heightened exposure at marginal costs.

Misconceptions

First, a group that has been interacting with social media their entire lives are university students. They have experienced countless viral trends and through their own social media accounts have figured out ways to induce virality. However, most are unfamiliar with the fact that there are actual formulas, specifically the virality formula, that can model how content goes viral. A few months ago, a survey was administered to a group of college students that found that 2/10 students could define a viral coefficient. A conclusion could be made that this is indicative of widespread public misconception regarding virality as most of the survey participants being digitally native. This implies that most of the public who isn’t digitally native lacks knowledge of the core aspect of virality. Students have a unique perspective on virality, they may know a lot more about it than the typical researcher by just existing and engaging with various media throughout much of their entire lives. A common misconception is that it is impossible to create the exact conditions under which content could go viral. However, both the generations that have grown up with social media, and some researchers believe that it actually may be possible.

Second, experts have also acknowledged the misconceptions regarding virality. Alyssa Rosenberg is a Washington Post writer who focuses on the intersection of culture and politics, she describes how it’s easy to ignore the labor involved in “viral” content: A “virus” spreads indiscriminately, as opposed through the work of its creators. As Rosenberg says, a misconception about virality is that it is beyond the creator’s capability to make their content go viral. However, as will be touched on later, researchers have outlined factors that could increase both the content’s ability to fulfill a want and its likelihood of imitation, and therefore it is possible to actively make content go viral.

Other reasons for the misconceptions around virality are seeded through misunderstandings of research. Lars Kai Hansen and Adam Arvidsson are applied computer science and media studies researchers respectively at the Technical University of Denmark. In their conclusion they determine that positive information that is diffused more appears to be in contrast with classic theory of diffusion in news media (Galtung and Ruge, 1965) emphasizing negative effect as promoting propagation. Another example of a misconception around virality is that negative news is likely to go viral. While this is true within a risky context, unlike other misconceptions, this misconception is seeded by research however, the misconception stems from not characterizing the type of content. In non-news content, as affirmed by Hansen and Arvidsson, and will be expanded on later, negative news is unlikely to go viral, however with news-related content, negative news is prone to going viral. Overall, some of the elements that cause misconceptions have been established.

How it works

Before proceeding, it’s imperative to examine what happens when content is first viewed by the user. Ines Brusch and Thomas Reichstein are researchers from the Institute of Business and Economics, within Brandenburg University of Technology Cottbus - Senftenberg, they conclude that the decision‐making process is basically divided into two stages. In the first decision stage, individuals decide whether content should be considered. When individuals agree to view the content, they decide in the second stage whether they want to interact with it … The framework conditions themselves are categorized into receiving framework conditions (e.g., Who sent this content?), created framework conditions (e.g., What is in the heading of the content?) or random framework conditions (e.g., Is his topic currently a trend?). When the audience first sees content, there's a process where the person viewing it decides whether or not it should be considered, then a proceeding process where they decide if they should interact with it. In addition to the content, and the viewer’s goal, framework conditions are extremely important as they leverage credibility to allow the person viewing the content to prioritize whether it's important and if they'd like to interact with or not. In terms of virality, something being sent from a friend has a positive effect when the receiving user evaluates whether or not to interact with the content, however, according to Brusch, regardless of how close the relationship of the sender to the viewer, if the content is poor, the content will not be interacted with at all. Lastly, Brusch’s research establishes why virality is so effective.

Subsequently, it’s crucial to consider the definition of virality. Tony D Sampson is a British theorist who focuses on media technology, he describes virality as “forces of imitation–suggestibility that spread through a network and that by way of a convergent imitative encounter captures the fragments of another’s desire.” Essentially, as content travels through a network, two things accelerate its spread: its ability to fulfill the audience’s want and it's ability to be imitated. As a result, it could be discerned that this pair of components form the basis of virality.

Next, it’s important to have a way to measure virality. In fact, Sophia Bernazzani a senior marketing manager at Hubspot explains the model for virality:

In rough language, the model states that the total number of new conversions (purchases, views etc.), C(T) is given by multiplying the number of customers at the beginning, C(0) with the viral coefficient, K

C(0)K=C(T)C(0)* K=C(T)

The virality coefficient is the likelihood of someone to share something, and get a desired action from another person such as a view or a like. Blockers, for example, a content geofence or a paywall and other things may hinder the content’s ability to be seen and therefore reduce its viral coefficient. More so, since the viral coefficient gauges the audience’s want and the content’s ability to be shared, the virality coefficient could be used to quantify Sampson’s characterization of virality. Overall, the virality coefficient allows the virality of content to be surveyed and represented in a variable.

Further, there exist ways to increase the virality coefficient. Jonah Berger and Katherine Milkman are marketing and operations researchers at the University of Pennsylvania, they describe how “consistent with our theorizing, online content that evoked high-arousal emotions was more viral, regardless of whether those emotions were of a positive (i.e., awe) or negative (i.e., anger or anxiety) nature. Online content that evoked more of a deactivating emotion (i.e., sadness), however, was actually less likely to be viral.” As Milkman and Berger discovered, emotions play a role in increasing the virality coefficient with some emotions such as sadness stifling it and others linked to high arousal increasing the likelihood of something being shared, which is also consistent with Sampon's definition of virality, that states that content that captures our desires are more likely to go viral. Consequently, Berger and Milkman’s research presents one way to raise the virality coefficient, by producing content that avoids or evokes certain emotions.

Conversely, it’s possible to decrease the virality coefficient of content. Gerard Tellis is the director of the Institute for Outlier Research in Business at USC, states that “Information-focused content has a significantly negative effect on sharing, except in risky contexts”. Informative posts are typically detrimental to virality unless the context includes risk, where information is dire and provokes a sense of urgency. In fact, this is also compatible with Sampson’s definition of virality that states something is likely to go viral if it fulfills a human need or is easy to share. A practical example of this could be seen in Boston University’s subreddit. In the subreddit’s ten-year history, the top post among over 12,000 posts is merely seven days old and involves news around testing. It could be assumed that details regarding testing are serious to students, therefore it corresponds with Sampon’s definition of virality which states that if something fulfills a human want it will be shared more. This could be explained through access to information having the potential to keep people safe and imitating others, in this case, through sharing the content further, is easy since it’s extremely relevant and helpful to the people receiving it. In the end, there exist ways to decrease the virality coefficient that are important to be conscious of.

Who should care

Adjacent to the previous points, it’s essential to layout specific groups that would benefit from an increased understanding of internet virality. According to Alphabet's fourth-quarter earnings report, the total revenue for Youtube Ads in 2020 was 15 billion in annual ad revenue. The revenue of one platform, Youtube, alone being greater than a more traditional media conglomerate such as CBS's with its $14.7 billion in annual revenue, not only gives an idea of the magnitude of proceeds involved in user content but illustrates how the tides are shifting towards user-generated content on internet platforms such as Youtube. Additionally, considering a figure from Mic, a media company based in New York that caters to millennials that states “mega-stars like Selena Gomez and Kylie Jenner pull in $800,000 and $1,000,000 per post, respectively (they both have more than 100 million followers), while those with the smallest following on the list (a mere one million) charge $1,300 to $3,000 per post.” The gap between the pay-per-post for megastars and smaller influencers leads to the assumption that if influencers increased their follower count they could possibly yield higher commissions for posts. On the influencer and content platform side, increased exposure results in increased revenue.

Next, another particular group within the general public that could benefit from an understanding of virality are independent artists. In this quote by Paige Leskin a tech reporter at Business Insider, Leskin describes how “‘Old Town Road’ was intended to go viral, with catchy lyrics and ‘quotable lines that people want to use as captions’”. Through composing and releasing Old Town Road in such a way that it sticks to the listener’s mind, and is easy to share, Lil Nas X actively implements some of the virality producing techniques and research that were touched on previously. Ultimately, Lil Nas X approach as an independent artist garnered a lot of exposure at very little cost.

Likewise, there are specific benefits to not holding the misconception that virality is random. Lilian Weng is a Complex Systems and Networks researcher at OpenAI, in her paper published in the journal Nature, it's demonstrated that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. While this won't increase virality directly, knowledge of the amount communities the content imbues unlocks the ability to gauge if the content has a chance at being viral early on. To conclude, through being able to see which content will go viral, content creators are able to make the determination to course correct and prevent wasted resources on content producing approaches that may not lead to virality.

Correspondingly, there are more practical examples of research around virality increasing how much content is shared. Berger and Milkman previously introduced in this article decide to apply their research to marketing and found that while marketers often produce content that paints their product in a positive light, our results suggest that content will be more likely to be shared if it evokes high-arousal emotions. This is beneficial because as opposed to a content creator spending resources on content that simply casts them a positive light, Berger and Milkman not only eliminate a misconception but outline how it's comparitvley ideal to focus on provoking emotion if the content creator's goal is virality. Not to mention, as a consequence of creating content that provokes emotion, it's more likely for the byproduct of releasing the content to be an ever-extending tree branch of sharing, by means of creating an ideal framework condition. As outlined by Hansen and Arvidsson, the power behind this is rooted in the added credibility of the content being introduced friend to friend rather than advertiser to a friend, thus increasing the amount of interaction with the content. Abandoning misconceptions that virality is random or something that can’t be actively influenced could unleash the power of virality and lead to content that has far more reach.

In addition, in terms of practical examples of benefits, in 2019 there was a dance challenge that took over the country. Dan Adler a staff writer at the Vanity Fair described how it started with a few faint, unmistakable banjo plucks, and it’s still going. After being quietly and independently released in December and picking up traction as an unfailing meme, Lil Nas X’s “Old Town Road” is now distributed by Columbia Records and has been No. 1 on Billboard’s Hot 100 for the last 11 weeks. Other experts on virality such as Leskin of Business Insider have concluded that Lil Nas X propelling himself from relative obscurity as an artist to having a billboard song of the year could be attributed through his understanding of internet virality. To dig deeper, consider Montero Hill’s background as an internet personality, being able to garner six-digit followers for his twitter meme page that preceded his song Old Town Road, also take into account how his song was paired with a dance challenge which made it easy to imitate. The results of Old Town Road are emblematic of how an understanding of virality could benefit artists through pulling in exposure at marginal costs. In the end, it’s not a one-off incident, although it wasn’t designed for virality (as it wasn’t explicitly encouraged to be imitated), and it wasn’t created by an independent artist, Miley Cyrus’s Party In the USA and the parody videos it spurned, further support the importance of virality in terms of generating exposure for artists.

Yup, it's still pretty random

In contrast of everything that’s been stated up until this point, virality is in someways random. Robert Wayne is President of Wayne Communications and a former contributor to Forbes. He stated that Engineering social epidemics is a fantasy. Outside of a vacuum such as those utilized in research studies, it would be impossible to engineer virality because that would require perfect conditions that assumably can't even be accurately measured at this current moment. In fact, implementing these techniques won’t be the panacea that makes content go viral. Especially when you consider changes to algorithms of major platforms that make virality elusive. However, as illustrated by Berger, Weng, Milkman and many of the other researchers featured so far, it’s safe to assume that implementing these techniques could increase your chances of going viral and at the very least increase engagement.

My experience with virality

Finally, while I’ve never gone viral, online marketing has always been a very useful skill. My initial venture into online growth was in 6th grade when I started a Youtube channel to show other players of a particular Facebook game how to get past harder levels. I noticed how I was able to increase the views on a video with stagnant viewership from roughly 10,000 to roughly 80,000 by tagging it more, therefore allowing it to gain more exposure through being recommended more often. Based on my experiences with increasing engagement, going viral involves trial and error, forming a hypothesis, experimenting, noticing patterns, implementing those patterns to gain more engagement, forming a hypothesis, and experimenting again etc. The point of this paper wasn’t to just to be an introduction to virality encouraging techniques but to dispel the notion that virality is random and enable readers to experiment and discover their own patterns, to then implement and expand on.

Concluding

In conclusion, a specific phenomenon within science, medicine, and or technology, that the public holds misconceptions of is memetics also known as the viral phenomenon, the phenomenon where people share content with other people and in this instance over the internet. A better understanding of internet virality is vital to not just the public but particular groups such as independent artists and social media influencers who could benefit from heightened exposure at marginal costs. The conditions of virality can be manufactured through provoking emotions, focusing on positive news or sensitive negative news, and overarchingly, achieving a human want while being easy to share. Furthermore, the points touched on in this paper, simply reevaluate a time tested phenomenon in a modern context. On one hand, the things we know about virality might differ as time progresses. If you pause and consider how the way we see advertisements is different, the way we listen to music has changed, and social media platforms share individual posts differently than before. On the other hand, one thing remains constant, the immense value in fulfilling a human want while also being easy to share.

Introduction to research backed ways to induce rapid content sharing

It isn’t luck. It’s not magic. And it’s not random. Going viral is a science. Sharing content has existed as far back as prehistory where people would exchange stories, and songs with each other orally. The proliferation of the personal computer and the internet further enabled people to send things to each other, widely forwarded emails, are some of the first instances of internet virality and predate most social networks. More recent instances of virality include Thavalakai's Little Superstar, and the ALS Ice Bucket Challenge which stemmed from a small group of friends in Boston and bloomed into 17 million people participating and $115 million raised for the ALS Association. Furthermore, social media sites, through additional functionalities, made possible by way of technological advancements such as video sharing, have facilitated a phenenomon called internet virality. A specific phenomenon within science, medicine, and or technology, that the public holds misconceptions of is memetics also known as the viral phenomenon, the phenomenon where people share content with other people and in this instance over the internet. A better understanding of internet virality is vital to not just the public but particular groups such as independent artists and social media influencers who could benefit from heightened exposure at marginal costs.

Misconceptions

First, a group that has been interacting with social media their entire lives are university students. They have experienced countless viral trends and through their own social media accounts have figured out ways to induce virality. However, most are unfamiliar with the fact that there are actual formulas, specifically the virality formula, that can model how content goes viral. A few months ago, a survey was administered to a group of college students that found that 2/10 students could define a viral coefficient. A conclusion could be made that this is indicative of widespread public misconception regarding virality as most of the survey participants being digitally native. This implies that most of the public who isn’t digitally native lacks knowledge of the core aspect of virality. Students have a unique perspective on virality, they may know a lot more about it than the typical researcher by just existing and engaging with various media throughout much of their entire lives. A common misconception is that it is impossible to create the exact conditions under which content could go viral. However, both the generations that have grown up with social media, and some researchers believe that it actually may be possible.

Second, experts have also acknowledged the misconceptions regarding virality. Alyssa Rosenberg is a Washington Post writer who focuses on the intersection of culture and politics, she describes how it’s easy to ignore the labor involved in “viral” content: A “virus” spreads indiscriminately, as opposed through the work of its creators. As Rosenberg says, a misconception about virality is that it is beyond the creator’s capability to make their content go viral. However, as will be touched on later, researchers have outlined factors that could increase both the content’s ability to fulfill a want and its likelihood of imitation, and therefore it is possible to actively make content go viral.

Other reasons for the misconceptions around virality are seeded through misunderstandings of research. Lars Kai Hansen and Adam Arvidsson are applied computer science and media studies researchers respectively at the Technical University of Denmark. In their conclusion they determine that positive information that is diffused more appears to be in contrast with classic theory of diffusion in news media (Galtung and Ruge, 1965) emphasizing negative effect as promoting propagation. Another example of a misconception around virality is that negative news is likely to go viral. While this is true within a risky context, unlike other misconceptions, this misconception is seeded by research, however, the misconception stems from not characterizing the type of content. In non-news content, as affirmed by Hansen and Arvidsson, and will be expanded on later, negative news is unlikely to go viral, however with news-related content, negative news is prone to going viral. Overall, some of the elements that cause misconceptions have been established.

How it works

Before proceeding, it’s imperative to examine what happens when content is first viewed by the user. Ines Brusch and Thomas Reichstein are researchers from the Institute of Business and Economics, within Brandenburg University of Technology Cottbus - Senftenberg, they conclude that the decision‐making process is basically divided into two stages. In the first decision stage, individuals decide whether content should be considered. When individuals agree to view the content, they decide in the second stage whether they want to interact with it … The framework conditions themselves are categorized into receiving framework conditions (e.g., Who sent this content?), created framework conditions (e.g., What is in the heading of the content?) or random framework conditions (e.g., Is his topic currently a trend?). When the audience first sees content, there's a process where the person viewing it decides whether or not it should be considered, then a proceeding process where they decide if they should interact with it. In addition to the content, and the viewer’s goal, framework conditions are extremely important as they leverage credibility to allow the person viewing the content to prioritize whether it's important and if they'd like to interact with or not. In terms of virality, something being sent from a friend has a positive effect when the receiving user evaluates whether or not to interact with the content, however, according to Brusch, regardless of how close the relationship of the sender to the viewer, if the content is poor, the content will not be interacted with at all. Lastly, Brusch’s research establishes why virality is so effective.

Subsequently, it’s crucial to consider the definition of virality. Tony D Sampson is a British theorist who focuses on media technology, he describes virality as “forces of imitation–suggestibility that spread through a network and that by way of a convergent imitative encounter captures the fragments of another’s desire.” Essentially, as content travels through a network, two things accelerate its spread: its ability to fulfill the audience’s want and it's ability to be imitated. As a result, it could be discerned that this pair of components form the basis of virality.

Next, it’s important to have a way to measure virality. In fact, Sophia Bernazzani a senior marketing manager at Hubspot explains the model for virality:

In rough language, the model states that the total number of new conversions (purchases, views etc.), C(T) is given by multiplying the number of customers at the beginning, C(0) with the viral coefficient, K

C(0)K=C(T)C(0)* K=C(T)

The virality coefficient is the likelihood of someone to share something, and get a desired action from another person such as a view or a like. Blockers, for example, a content geofence or a paywall and other things may hinder the content’s ability to be seen and therefore reduce its viral coefficient. More so, since the viral coefficient gauges the audience’s want and the content’s ability to be shared, the virality coefficient could be used to quantify Sampson’s characterization of virality. Overall, the virality coefficient allows the virality of content to be surveyed and represented in a variable.

Further, there exist ways to increase the virality coefficient. Jonah Berger and Katherine Milkman are marketing and operations researchers at the University of Pennsylvania, they describe how “consistent with our theorizing, online content that evoked high-arousal emotions was more viral, regardless of whether those emotions were of a positive (i.e., awe) or negative (i.e., anger or anxiety) nature. Online content that evoked more of a deactivating emotion (i.e., sadness), however, was actually less likely to be viral.” As Milkman and Berger discovered, emotions play a role in increasing the virality coefficient with some emotions such as sadness stifling it and others linked to high arousal increasing the likelihood of something being shared, which is also consistent with Sampon's definition of virality, that states that content that captures our desires are more likely to go viral. Consequently, Berger and Milkman’s research presents one way to raise the virality coefficient, by producing content that avoids or evokes certain emotions.

Conversely, it’s possible to decrease the virality coefficient of content. Gerard Tellis is the director of the Institute for Outlier Research in Business at USC, states that “Information-focused content has a significantly negative effect on sharing, except in risky contexts”. Informative posts are typically detrimental to virality unless the context includes risk, where information is dire and provokes a sense of urgency. In fact, this is also compatible with Sampson’s definition of virality that states something is likely to go viral if it fulfills a human need or is easy to share. A practical example of this could be seen in Boston University’s subreddit. In the subreddit’s ten-year history, the top post among over 12,000 posts is merely seven days old and involves news around testing. It could be assumed that details regarding testing are serious to students, therefore it corresponds with Sampon’s definition of virality which states that if something fulfills a human want it will be shared more. This could be explained through access to information having the potential to keep people safe and imitating others, in this case, through sharing the content further, is easy since it’s extremely relevant and helpful to the people receiving it. In the end, there exist ways to decrease the virality coefficient that are important to be conscious of.

Who should care

Adjacent to the previous points, it’s essential to layout specific groups that would benefit from an increased understanding of internet virality. According to Alphabet's fourth-quarter earnings report, the total revenue for Youtube Ads in 2020 was 15 billion in annual ad revenue. The revenue of one platform, Youtube, alone being greater than a more traditional media conglomerate such as CBS's with its $14.7 billion in annual revenue, not only gives an idea of the magnitude of proceeds involved in user content but illustrates how the tides are shifting towards user-generated content on internet platforms such as Youtube. Additionally, considering a figure from Mic, a media company based in New York that caters to millennials that states “mega-stars like Selena Gomez and Kylie Jenner pull in $800,000 and $1,000,000 per post, respectively (they both have more than 100 million followers), while those with the smallest following on the list (a mere one million) charge $1,300 to $3,000 per post.” The gap between the pay-per-post for megastars and smaller influencers leads to the assumption that if influencers increased their follower count they could possibly yield higher commissions for posts. On the influencer and content platform side, increased exposure results in increased revenue.

Next, another particular group within the general public that could benefit from an understanding of virality are independent artists. In this quote by Paige Leskin a tech reporter at Business Insider, Leskin describes how “‘Old Town Road’ was intended to go viral, with catchy lyrics and ‘quotable lines that people want to use as captions’”. Through composing and releasing Old Town Road in such a way that it sticks to the listener’s mind, and is easy to share, Lil Nas X actively implements some of the virality producing techniques and research that were touched on previously. Ultimately, Lil Nas X approach as an independent artist garnered a lot of exposure at very little cost.

Likewise, there are specific benefits to not holding the misconception that virality is random. Lilian Weng is a Complex Systems and Networks researcher at OpenAI, in her paper published in the journal Nature, it's demonstrated that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. While this won't increase virality directly, knowledge of the amount communities the content imbues unlocks the ability to gauge if the content has a chance at being viral early on. To conclude, through being able to see which content will go viral, content creators are able to make the determination to course correct and prevent wasted resources on content producing approaches that may not lead to virality.

Correspondingly, there are more practical examples of research around virality increasing how much content is shared. Berger and Milkman previously introduced in this article decide to apply their research to marketing and found that while marketers often produce content that paints their product in a positive light, our results suggest that content will be more likely to be shared if it evokes high-arousal emotions. This is beneficial because as opposed to a content creator spending resources on content that simply casts them a positive light, Berger and Milkman not only eliminate a misconception but outline how it's comparitvley ideal to focus on provoking emotion if the content creator's goal is virality. Not to mention, as a consequence of creating content that provokes emotion, it's more likely for the byproduct of releasing the content to be an ever-extending tree branch of sharing, by means of creating an ideal framework condition. As outlined by Hansen and Arvidsson, the power behind this is rooted in the added credibility of the content being introduced friend to friend rather than advertiser to a friend, thus increasing the amount of interaction with the content. Abandoning misconceptions that virality is random or something that can’t be actively influenced could unleash the power of virality and lead to content that has far more reach.

In addition, in terms of practical examples of benefits, in 2019 there was a dance challenge that took over the country. Dan Adler a staff writer at the Vanity Fair described how it started with a few faint, unmistakable banjo plucks, and it’s still going. After being quietly and independently released in December and picking up traction as an unfailing meme, Lil Nas X’s “Old Town Road” is now distributed by Columbia Records and has been No. 1 on Billboard’s Hot 100 for the last 11 weeks. Other experts on virality such as Leskin of Business Insider have concluded that Lil Nas X propelling himself from relative obscurity as an artist to having a billboard song of the year could be attributed through his understanding of internet virality. To dig deeper, consider Montero Hill’s background as an internet personality, being able to garner six-digit followers for his twitter meme page that preceded his song Old Town Road, also take into account how his song was paired with a dance challenge which made it easy to imitate. The results of Old Town Road are emblematic of how an understanding of virality could benefit artists through pulling in exposure at marginal costs. In the end, it’s not a one-off incident, although it wasn’t designed for virality (as it wasn’t explicitly encouraged to be imitated), and it wasn’t created by an independent artist, Miley Cyrus’s Party In the USA and the parody videos it spurned, further support the importance of virality in terms of generating exposure for artists.

Yup, it's still pretty random

In contrast of everything that’s been stated up until this point, virality is in someways random. Robert Wayne is President of Wayne Communications and a former contributor to Forbes. He stated that Engineering social epidemics is a fantasy. Outside of a vacuum such as those utilized in research studies, it would be impossible to engineer virality because that would require perfect conditions that assumably can't even be accurately measured at this current moment. In fact, implementing these techniques won’t be the panacea that makes content go viral. Especially when you consider changes to algorithms of major platforms that make virality elusive. However, as illustrated by Berger, Weng, Milkman and many of the other researchers featured so far, it’s safe to assume that implementing these techniques could increase your chances of going viral and at the very least increase engagement.

My experience with virality

Finally, while I’ve never gone viral, online marketing has always been a very useful skill. My initial venture into online growth was in 6th grade when I started a Youtube channel to show other players of a particular Facebook game how to get past harder levels. I noticed how I was able to increase the views on a video with stagnant viewership from roughly 10,000 to roughly 80,000 by tagging it more, therefore allowing it to gain more exposure through being recommended more often. Based on my experiences with increasing engagement, going viral involves trial and error, forming a hypothesis, experimenting, noticing patterns, implementing those patterns to gain more engagement, forming a hypothesis, and experimenting again etc. The point of this paper wasn’t to just to be an introduction to virality encouraging techniques but to dispel the notion that virality is random and enable readers to experiment and discover their own patterns, to then implement and expand on.

Concluding

In conclusion, a specific phenomenon within science, medicine, and or technology, that the public holds misconceptions of is memetics also known as the viral phenomenon, the phenomenon where people share content with other people and in this instance over the internet. A better understanding of internet virality is vital to not just the public but particular groups such as independent artists and social media influencers who could benefit from heightened exposure at marginal costs. The conditions of virality can be manufactured through provoking emotions, focusing on positive news or sensitive negative news, and overarchingly, achieving a human want while being easy to share. Furthermore, the points touched on in this paper, simply reevaluate a time tested phenomenon in a modern context. On one hand, the things we know about virality might differ as time progresses. If you pause and consider how the way we see advertisements is different, the way we listen to music has changed, and social media platforms share individual posts differently than before. On the other hand, one thing remains constant, the immense value in fulfilling a human want while also being easy to share.