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Hacking as a Metaphor: How #russianhacking Influenced 
Cybersecurity Discourse 

by Justin Flick

Introduction
 Cybersecurity has become one of the most 
discussed and least understood issues in American 
discourse.  As internet-connected technology 
continues to permeate further into the lives of 
everyday people, the interest and controversy 
around cybersecurity will continue to increase. 
Unfortunately, there is a significant amount of 
misinformation surrounding cybersecurity. This 
has recently come to a head during the 2016 United 
States presidential election, in which accusations 
of Russian hacking, unsecured email servers, and 
unauthorized wiretapping were flung around with 
impunity. Specifically, accusations that the Russian 
government “hacked” the election by releasing 
private Democratic National Convention (DNC) 
emails in order to change the outcome of the 
election have proliferated throughout mainstream 
news and created substantial confusion on what 
“hacking” really constitutes. The “Russian hacking,” 
as it has been termed, is arguably the most publicized 
cybersecurity incident in history, and has also been 
the catalyst for mainstream press to disseminate 
wildly sensationalized and inaccurate information 
regarding cybersecurity. 
 My research aims to analyze the implications of 
the discourse surrounding the Russian hacking and 
how this discourse reflects broader societal trends. In 
order to provide a holistic perspective for this analysis, 
I approached this idea from both a quantitative and 
qualitative perspective. The quantitative portion 
consisted of a Naïve-Bayes sentiment analysis of 
tweets (Twitter posts) containing #russiahacking 
and/or #russiagate. The qualitative portion looks at 
the use of the term “hacking” to describe this incident 
and draws on George Lakoff ’s contemporary theory 
of metaphor to examine how the term “hacking” 
itself has become shorthand for the metaphor 
of technology-as-magic. Though there are other 
metaphors for hacking, technology-as-magic will be 
analyzed as the central metaphor for hacking in this 

paper. Finally, I examine the implications of the use 
of this metaphor on both a macro- and micro-level 
through the lens of Allen C. Johnston and Merrill 
Warkentin’s Fear Appeals Model.

Background 
 The 2016 United States presidential election was 
a uniquely contentious affair between two candidates 
that were highly polarized by both the public and 
the mainstream media.  Politico Magazine called it 
“the dirtiest Presidential race since ’72” (Cummins, 
2016). Republican Donald J. Trump, empowered 
by an alt-right base, faced off against establishment 
Democrat Hillary Clinton. Clinton entered the 
campaign mired in a cybersecurity controversy. She 
had been under congressional investigation for the 
use of a private email server during her time as a 
Secretary of State during President Barack Obama’s 
first term. Throughout the initial stages of the 
campaign, Clinton and Trump faced off regarding 
these emails, and it continued to escalate and drive a 
national conversation on cybersecurity. 
 The Clinton email scandal would soon be 
relegated behind a larger cybersecurity scandal. 
In September of 2015, the Federal Bureau of 
Investigation informed the Democratic National 
Committee that one of their computers had been 
compromised by “the Dukes,” a cyberattack team 
linked to the government of Russia. The DNC thought 
this was a prank call (Sanger & Shane, 2016). Later, 
on June 15th, 2016, a hacker known as Guccifer 2.0 
leaked a large number of emails that had been stolen 
from the DNC. These included emails located on 
the DNC server, campaign strategy documents, and 
the vulnerabilities of the DNC network  (Nussbaum, 
2017). 

Qualitative Method
 Although there are many competing methods 
for rhetorical analysis, I find that metaphor analysis 
provides the unique benefits of both a) providing 



From Johnston and Warkentin (2010).

Hacking as a Metaphor

Aisthesis      Volume 10,  201939

a framing tool for the implication of rhetorical 
analysis and b) being easier to understand due to 
a conceptual reduction to a colloquialism. George 
Lakoff writes heavily about the use of metaphor as 
an explanation of rhetoric, and in fact argues that we 
frame our worldly experiences through metaphors. 
For example, it is not uncommon to refer to a period 
time where one is experiencing a large number of 
negative feelings as one being “down in the dumps.” 
In this way, the idea of being “down” becomes used 
metaphorically as a replacement for negative. In the 
same way, Lakoff argues that political discourse is 
rife with metaphor. He says, “We tend to understand 
the nation metaphorically in family terms: We have 
founding fathers. We send our sons and daughters to 
war. We have homeland security. The conservative 
and progressive worldviews dividing our country 
can most readily be understood in terms of moral 
worldviews that are encapsulated in two very different 
common forms of family life: The Nurturant Parent 
family (progressive) and the Strict Father family 
(conservative)” (Lakoff, 2016). Using this concept, 
we can frame political ideas as metaphor in order 
to easily understand the rhetoric used in political 
discourse and the implications therein. 
 Metaphor can be a productive lens for rhetorical 
analysis. However, what defines a metaphor? How 
does one classify an idea as a metaphor? Regarding 
these concerns, Rudolph Schmitt argues that Lakoff 
provides little functional guidance. Schmitt says, 
“the last publication of Lakoff and Johnson leads to 
the assumption that metaphorical models, forming 
the framework of collective thought, have already 
essentially been identified in their basic form. Both 
positions bypass the often difficult task of identifying 
metaphors and reconstructing their contextual 
meaning” (Schmitt 2005). Luckily, Schmitt offers 
a method for determining metaphor that is based 
upon Lakoff ’s work. The method offers 3 criteria that 
a word or phrase must meet in order to be considered 
metaphor. One can classify something as metaphor 
if:
 a. a word or phrase, strictly speaking, can be 
understood beyond the literal meaning in the  
context; and
 b. the literal meaning stems from an area of 
sensoric or cultural experience (source area), 
 c. which, however, is transferred to a second, 

often abstract, area (target area).Schmitt’s method 
offers an appropriate framework through which 
one can determine metaphor, as it offers praxis 
to Lakoff ’s theory. Later, I will argue that the term 
“hacking” fulfills these three requirements, and thus 
can be considered a metaphor on its own. 
 Once one has evaluated whether a term is 
metaphor, how does one evaluate the implications 
of the use of said metaphor? Although far more 
generalized methods exist, I argue that the Fear 
Appeals Model is the optimal model through 
which to evaluate the implications of cybersecurity 
discourse, including the metaphors contained 
therein. In this model, perceived threat risk refers to 
the how susceptible an individual believes he or she 
is to a cybersecurity threat (see Figure 1). 

 These perceived notions about the threat lead 
to a second set of cognitive evaluations under this 
model. In this case, perceived response efficacy refers 
to how effective an individual believes the prescribed 
response is. For example, when dealing with identity 
theft, the generally prescribed responses include 
contacting one’s bank and freezing accounts when 
one suspects they may be a victim of identity theft. 
In this case, this response is generally perceived as 
effective, and thus one moves to a tertiary level of 
cognition where one evaluates their own efficacy at 
implementing the prescribed solution. In the case 
of identity theft, for most individuals, it is not a 
monumental task to call one’s bank to freeze their 
accounts. Thus, individuals become more likely to 
respond in the prescribed way when they perceive it 
as effective and easy to do. 



From Deyasi et al. (2016).

Hacking as a Metaphor

Aisthesis      Volume 10,  201940

 Ultimately, the analysis prescribed by the Fear 
Appeals Model terminates in individuals performing 
certain behaviors. However, there is an independent 
variable in this model that must always be accounted 
for outside the tradition flow of the model: social 
influence. Social influence can be viewed as something 
resembling social norm, where the generally 
expected behavioral norms influence one’s behavior 
in response to a cybersecurity threat. Johnston and 
Warkentein’s third hypotheses of the Fear Appeals 
Model states, “social influence will have a positive 
effect on end user intentions to adopt recommended 
individual computer security actions.” As I will argue 
later in this paper, metaphor can modify multiple 
elements of the model, but at the very minimum, an 
analysis using this model should be able to justify 
metaphor as a social influence modifier, especially in 
the context of Lakoff ’s framing of metaphors as ideas 
“we live by.” 
 Ultimately, by utilizing this model, one could 
functionally evaluate macro-level influences on 
cybersecurity behavior. Traditionally, this model 
has been applied on a smaller scale to influencing 
factors in an organizational environment. However, 
as the model only intends to provide a framework 
for evaluating factors that ultimately influence both 
preventative and responsive cybersecurity behavior, 
there appears to be no issues with scaling the model 
to evaluate macro-level influences.  

Quantitative Method
 As a method for sentiment analysis, Naïve Bayes 
classification is a machine learning function intended 
to classify text based upon Bayes’ algorithm. This 
algorithm states 

where P(c|x)  is the posterior probability 
of  class  (target) given  predictor  (attribute) and 
P(x|c)  is the likelihood which is the probability 
of predictor given class (see Figure 2; Deyasi et al., 
2016).  As applied to text classification and sentiment 
analysis, Naïve Bayes relies upon the assumption 
that every input value is generated by first choosing 
a class label for that input value, and then generating 
each feature, entirely independent of every other 
feature (Bird et al., 2009).
 In order to perform the sentiment analysis, I 
had to overcome the challenge of acquiring a large 
enough dataset to justify my conclusions.  Twitter 
offers an Official API (Application Programming 
Interface) that allows one to scrape data directly from 
Twitter’s database, but it has a maximum limit on the 
number of records it will return and only allows one 
to access tweets from the past 7 days. I found these 
limits to be a roadblock to establishing a conclusive 
sentiment analysis, so I decided that I would have 
to develop my own software to collect the dataset, 
cleanse the data, and perform the sentiment analysis. 
 I chose to use Naïve-Bayes classification for the 
sentiment analysis for a couple different reasons. 
First, it was the most accessible from a technical 
perspective. Second, when applied to social media 
posts, it boasts an 80% accuracy rate in deducing the 
correct sentiment (Troussas et al., 2013). The Naïve-
Bayes methods assigns a polarity score between 
-1 and 1, based upon natural language processing, 
and the lexicon dictionaries it is trained with.  For 
the analysis and visualization of the data, I utilized 
Tableau to calculate maximum and minimum 
polarity scores, average polarity, and median polarity. 
Additionally, I labeled tweets with a sentiment score 
between -1 and -.001 as “Negative,” tweets with a 
sentiment score between .001 and 1 as “Positive” and 
tweets with a sentiment of 0 as “Neutral.”  
 The idea behind using sentiment analysis as a 
tool for analyzing rhetoric is simply that it could be 
helpful to have data justifying one’s interpretation of 



Hacking as a Metaphor

Aisthesis      Volume 10,  201941

rhetoric. Although there is an anonymization element 
built into Twitter, I specifically chose to further 
isolate the authors from their tweets by removing 
any Personally Identifiable Information (PII) from 
my dataset. In the end, I managed to collect a dataset 
spanning 5 years and almost 300,000 tweets. I would 
argue that such a large dataset would allow me to 
reduce the weight of potentially problematic tweets. 
 Although there can be some concerns about the 
accuracy of sentiment analysis, by utilizing a score 
based on every word in a tweet rather than simply 
taking the score from the strongest word in a tweet 
(as done in other sentiment analysis methods), this 
method ensures that positive or negative modifiers 
are taken into account. In the context of examining 
rhetoric surrounding the Russian hacking incident, it 
may seem puzzling to utilize a quantitative method to 
deduce the effects of rhetoric. However, the method 
I utilize for acquiring data and perform sentiment 
analysis on that data allowed me to gain insight into 
a far larger dataset compared to traditional rhetorical 
analysis. Using my scraper application, I collected 
a dataset of almost 300,000 tweets, each a unique 
piece of rhetoric that could be analyzed. However, 
by utilizing sentiment analysis on this data, I am able 
to offer an empirical justification for the analysis I 
perform on the discourse surrounding the hacking. 
I think this mixed-method approach allows me 
to draw a holistic conclusion about the rhetoric I 
examine.

Qualitative Results and Conclusions
 Is hacking truly a metaphor as I would position 
it? Allow me to work through Schmitt’s method. 
Schmitt (2005) first says that to be metaphor, “a 
word or phrase, strictly speaking, can be understood 
beyond the literal meaning in the context.” It would 
appear “hacking” fits this description perfectly. The 
term “hacker” is often associated with something 
almost akin to the wizards of fantasy lore, capable 
of inexplicable tasks that create almost supernatural 
effects on what we perceive as the “real world.” Shows 
like Mr. Robot, movies like War Games, and video 
games like Watchdogs offer a portrayal of hackers as 
capable of bringing down societal infrastructure with 
the press of a key, while offering little explanation for 
how this is done. Thus, when cybersecurity incidents 

occur in everyday life, they are quickly branded as 
a “hacking” even if they do not necessarily fit the 
technical definition of a hack. 
 Schmitt’s second qualification for metaphor 
indicates that “the literal meaning (of a word or 
phrase) stems from an area of sensoric or cultural 
experience (source area).” As computers and the 
internet have continued to permeate every aspect 
of society, cybersecurity and threats associated with 
it have equally became more ingrained in societal 
consciousness. At its most literal meaning, hacking 
can be defined as unauthorized entry into a computer 
system or network for nefarious purposes. Although 
there have been many high-profile hacking incidents 
(e.g., Stuxnet, the Mirari Botnet, Heartbleed, among 
others) that could be considered a shared cultural 
experience, the Russian hacking allegations are 
arguably the most publicized cybersecurity incident, 
and thus the largest share cultural experience from 
which we can derive literal meaning. Thus, as 
cultural experience shapes the literal perception of 
hacking in the minds of the general population, it 
should be able to stand as meeting Schmitt’s second 
qualification. 
 Finally, Schmitt’s third qualification states “which, 
however, is transferred to a second, often abstract, 
area (target area).” I would argue the “target area” of 
this metaphor can be the terminal interpretation of 
technology-as-magic. As previously stated, ordinary 
individuals apply the term “hacking” to actions that 
are not unauthorized compromise of a computer 
system or network. Non-technical individuals—
the majority of the population—often jump to the 
conclusion that they have been “hacked” when faced 
with computer errors they do not understand. Popular 
media refers to self-improvement tips as “life-hacks.”  
Individuals simply create a false correlation between 
vaguely scientific or technological concepts that they 
deem to be beyond their understanding as some 
sort of “nefarious trick” to overcome the cognitive 
dissonance of their own lack of understanding. 
Simply put, writing-off concepts as hacking creates 
an easy explanation for acts that would require a 
large amount of cognition to explain. Ultimately, I 
would argue that hacking easily fulfills Schmitt’s 
third qualification. 
 After examining the term “hacking” through 
Schmitt’s method, it is abundantly clear that we can 
classify “hacking” as a metaphor in and of itself. 



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Aisthesis      Volume 10,  201942

But what exactly is this metaphor? Hacking can be 
a metaphor for technology-as-magic. In the context 
of #russianhacking, applying the hacking metaphor 
to the entire incident beyond the literal hacking 
action allows one to come to an easy explanation 
for something as complex as the result of a United 
States presidential election. In the aftermath of 
Donald Trump’s election to the presidency, many 
individuals felt as though the result was inexplicable. 
Returning to the Russian hacking incident, utilizing 
the term “hacking” as a metaphor for technology-as-
magic allows individuals to explain the confluence 
of a multitude of factors that contributed to the 
outcome of the election. However, simply attributing 
an unexpected outcome to some computer wizard 
casting his hacking spells to influence something as 
important as an election takes far less cognitive work 
than sorting through factors like Clinton’s email 
scandal, Trump’s ability to motivate voter turnout, 
or the fact that mainstream media had pushed a 
narrative that pre-ordained Clinton as the next 
president, which arguably colored perceptions about 
how close the presidential race truly was. 
 What exactly do I mean when I use the phrase 
“technology-as-magic”? First, allow me to refer 
to one accepted definition of magic in religious 
scholarship and anthropology. Magic “consists of 
a control worked by humans over nature by use of 
spiritual forces, so that the end result is expected to 
lie within the will of the person or persons working 
the spell or the ritual. In theory anybody ought to be 
able to carry out either, but in practice most societies 
have produced specialist practitioners in both” 
(Hutton, 1993). In our modern, technology-driven 
society we have replaced control over spiritual forces 
with control over electronic forces. Much like the 
mages of ancient lore, “hackers” and those who are 
technologically skilled appear to have otherworldly 
abilities far beyond the reach of the common person.  
In reality, the skills required to perform these actions 
are able to be learned by almost anyone, but like 
Hutton says, most societies produce specialists in 
these fields. I use the term “magic” here to make a 
connection to pre-technological phenomenon. Often 
society attempts to position the issues and cultural 
concepts surrounding technology to be some sort of 
new frontier. In reality, it’s simply another medium 
for the reflection of human nature. While it may be 
easy to think that computer technology represents 

some new powerful threat to society, it truly only 
replicates previous cultural issues. 
 Now that it has been established that hacking 
can be viewed as a metaphor, I will discuss the 
implications of this metaphor through the lens of the 
Fear Appeals Model. Although the original model 
was specific to the user adoption of organizational 
security controls, I believe it has relevance to the 
implications of the hacking metaphor. Allow me 
to first examine the impact this would have on 
perceived threat risk and severity. As previously 
discussed, the hacking metaphor functions as a label 
for unexplainable technical phenomena, and the 
inexplicable nature of the literal meaning of hacking 
exponentially increases the perceived threat risk. 
When individuals are fed this metaphor on a societal 
level, they become socialized to feel at risk of a threat. 
Specifically, in the context of the Russian Hacking, 
an individual’s overall perceived threat susceptibility 
and response efficacy are affected by the use of the 
hacking metaphor to portray this incident. Consider 
this: If an outside sovereign government could 
“hack” an election, a cornerstone of United States 
democracy, what hope does an average person have 
for warding off cyberattacks? This is the power of the 
metaphor. Due to the inexplicable nature of these 
“hacking” incidents and the continued reliance upon 
technology in society, it is easy to see how it seems 
like the threat could be existential. In fact, the threat 
has been argued to be nuclear. In 2009, Jason Fritz 
argued that due to the inherent flaws in most nuclear 
command systems, it is not outside of the realm of 
possibility that hackers could provoke a nuclear war 
by confusing early detection systems. As this attack 
could be performed against multiple nuclear powers 
simultaneously, one could see the realization of the 
most destructive cold-war scenarios. Although this 
scenario seems far-fetched, it is not unreasonable 
that one could logically draw a path from election 
hacking to nuclear cyberattacks. However, this is 
exactly what the framing of hacking as a metaphor 
would criticize. It is undeniable that there are cyber 
threats. However, as we continue to misuse the term 
as a metaphor for the inexplicable, we give rise to 
paralyzing fears of fantastical doomsday scenarios. 
 These fantastical scenarios influence perceived 
response efficacy and self-efficacy. Frankly, when 
society wantonly uses terms like hacking, with all 
of the fearful connotations that word implies, to 



Hacking as a Metaphor

Aisthesis      Volume 10,  201943

describe incidents from political email leaks to 
someone guessing a weak password, we create a 
fear complex that inspires feelings of helplessness 
in the general population, who see the technical 
explanations of these issues as beyond their grasp. 
If anything, many individuals will feel that there 
is no response, much less an effective response. 
That’s the ultimate problem with hacking becoming 
a metaphor: it becomes applied to inappropriate 
scenarios that have real consequences. Specifically, as 
we misuse hacking as a metaphor for technology-as-
magic, we further multiply the perceived difficulty 
of acquiring the skills necessary to have a modicum 
of self-responsiveness to cyber threats. Technology, 
although capable of amazing things, is not magic; 
it’s all based on a series of logic that individuals can 
learn. The inappropriate use of the hacking metaphor 
is directly responsible for societal justifications of 
technical illiteracy. If non-technical individuals view 
computers as scary in part because there are hackers 
on the internet, it may lead these individuals to 
question whether they should bother to learn how to 
avoid risks while using the internet. 
In a world where we have to worry about other 
governments hacking our election, it becomes easy 
to fall into a trap of this thinking. 
 If one feels that they are going to be the victim 
of a cyberattack anyway, what motivation do they 
have to engage in time-consuming effective cyber 
behavior? Truthfully, they have none. They generally 
believe their best protection is the hope that a hacker 
will have little interest in their data.  On the other 
extreme, some individuals shun certain technologies 
due to security concerns. Although this response 
is less common, these individuals have such low 
perceived self-efficacy that the only way to protect 
themselves is to give up certain technologies, like 
social media websites. Sometimes, certain cyber 
risks promote a fad of shunning certain technology. 
For example, in 2016, when FBI director James 
Comey said that individuals should take precautions 
to protect their webcams from intrusion, millions 
of people began taping over their webcams, or even 
physically breaking them to ensure privacy (Hattem, 
2016). However, the far more common response is 
to engage in risky cyber behavior without pursuing 
behavioral change. This can lead to a number of 
consequences, including a higher rate of cyberattacks, 

especially if these individuals are pursuing risky 
cyber behaviors in situations where they have access 
to valuable information like their workplace. 
 Even if one does not pinpoint the metaphor as 
the primary input to the Fear Appeals Model, the 
phenomena discussed above culminates in factors 
that affect the social influence characteristic of the 
model. Thus, even while examining other concepts 
as the beginning input for the model, hacking as a 
metaphor ultimately still affects the terminal behavior 
in the ways previously described. It’s this concept that 
allows the analysis of the hacking metaphor within 
the Fear Appeals Model to serve a dual purpose. It 
first can serve to explain the implications of the use 
of the metaphor and create a logical path from the 
use of the metaphor to individual behavior. Second, 
when the hacking metaphor is applied through a 
lens of acting as a social influence, it allows one to 
use the model in future analysis of influences on 
cyber behavior with macro-social influence already 
accounted for. In this way, I hope my analysis can 
contribute to future research. 

Quantitative Results and Conclusions 
 The quantitative results are intended to 
supplement the qualitative analysis. I hypothesized 
that applying the hacking metaphor to the Russian 
interference colored perceptions of cybersecurity. 
While only 24.67% of tweets expressed a positive 
sentiment, those that were identified as positive 
were of stronger conviction, possessing both a 
large average polarity score and a higher media 
polarity (see Figure 3). This would indicate that 



Hacking as a Metaphor

Aisthesis      Volume 10,  2019

while sentiments of positivity are the minority of 
responses, those who expressed a positive sentiment 
felt stronger than those who expressed a negative 
sentiment. However, the fact that 75.33% of tweets 
were either negative or neutral indicates that overall 
response to #russianhacking can be considered 
negative. Considering the size of the dataset analyzed 
(300,000 tweets), I would argue that these results 
justify a supporting analysis. 
 We can also see the change in sentiment over 
time (see Figure 4). For the purposes of this analysis, 
we can disregard tweets prior to May of 2015, though 
it is interesting that there were such strong swings 
in polarity prior to media coverage of the Russian 
incident. It is ultimately telling that the change 
between months becomes far narrower when we 
analyze tweets from around the time #russianhacking 
was first publicized. In April 2016, we can see the 
lowest monthly polarity within the past year, as the 
average polarity score of all tweets in that month 
was -.0426. Conversely, in March of 2017—when I 
acquired my data set—it had spiked back into the 
positive range with a score of .0263. To explain this 
change of sentiment, one can look to the changing 
media narrative surrounding the Russian Hacking. 
In March, FBI director James Comey announced 
that the FBI was investigating connections between 
the Trump campaign and the Russian government 
(Rosenberg & Huetteman, 2017). It seems likely that 
individuals who were previously tweeting negative 
sentiments about #russianhacking were pleased with 
the announcement and thus began tweeting positive 
sentiments. However, as I removed Personally 
Identifiable Information from my dataset, I am 
unable to analyze where there are any repeat Twitter 
user in my data. 

 Luckily, it is easy to “drill-down” to acquire 
examples of specific tweets. Take for example, 
one tweet that scored a polarity score of -0.637. 
This tweet read “The evil of the #russianhacking 
is palpable. I can feel it building. Everything about 
this feels very bad.” When analyzed through the 
qualitative methods I utilized for my previous 
analysis, this tweet largely reflects that individual’s 
perceived threat risk. This reference to the Russian 
incident as “evil” conveys great fear of hacking, but it 
also sits within the framing of technology-as-magic. 
Many definitions of evil frame it within supernatural 
forces. (“Evil,” 2017) Thus, to call this event “evil” can 
imply the concept of technology-as-magic. 
 As another example, allow me to examine a tweet 
that possesses a polarity score of -1. This tweet reads 
“But this is the 1st time the CIA and FBI don’t seem to 
care - that’s a terrifying difference! #russianhacking.” 
If we once again look to the Fear Appeals Model, 
we can determine that this tweet expresses a low 
perceived response efficacy. If an individual is feeling 
scared about the potential scenario of an outside 
power hacking their democratic elections, and they 
also in turn feel that the agencies entrusted with 
protecting the nation from these sort of incidents 
are unable to successfully respond, it is only logical 
to assume they will be engaging in riskier cyber 
behavior due to a low perceived response efficacy. In 
the same vein, if an individual is looking only toward 
these government agencies to protect themselves, 
this would demonstrate a low perceived self-efficacy. 
Ultimately, I don’t intend for this section to replicate 
the qualitative analysis; however, these examples 
serve as a demonstration of the accuracy of the 
sentiment analysis. 

Conclusion
 After the media circus surrounding the 2016 
presidential election and the Russian hacking 
allegations, it seems necessary to re-evaluate how we 
utilize cybersecurity rhetoric in societal discourse. 
In order to examine this, I have first argued that the 
term “hacking” has become a metaphor on its own. 
In this analysis, I defined “hacking” as a metaphor 
for technology-as-magic and drew a comparison to 
medieval conceptions of magic to further illustrate 
this notion. Next, to evaluate the implications of 
the use of this metaphor, I utilized Johnston and 

44



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Aisthesis      Volume 10,  2019

Warkentin’s Fear Appeals Model to argue that the 
use of the hacking metaphor amplifies individuals’ 
perceived susceptibility to cyber threats while 
diminishing both their perceived response efficacy 
and perceived self-efficacy. Ultimately, I argue that 
this macro level discourse influences individual 
cyber behavior in ways that lead to riskier cyber 
behaviors. Finally, in order to offer empirical 
support for my analysis, I scraped Twitter to acquire 
a dataset of 300,000 tweets and then performed a 
sentiment analysis using a Naïve Bayes classifier in 
order to examine sentiment trends in response to 
#russiahacking and #russiagate. 
 As Lakoff argues that most metaphors have 
already been discovered, I argue that my research 
justifies the entry of “hacking” into the metaphor 
lexicon. Additionally, as it appears that the social 
influence factor of the Fear Appeals Model seems 
to be underexamined, I hope that the hacking 
metaphor can offer a point of praxis for future use of 
the model. As I have published the software I created 
for the quantitative portion of my research under 
an open source MIT license, I also hope that other 
researchers can find what I have created over the 
course of this study useful.
 For future research, it could be worthwhile to 
repeat my quantitative analysis with the usernames 
of individuals who composed the tweets. One could 
then analyze the change in sentiment of their tweets 
over time as an indication of evolving perspective. 
Additionally, although the Naïve Bayes classifier I 
utilized boasts a high accuracy rate, further research 
should be done into this classification to further 
account for lingual phenomena like sarcasm or 
modified syntax. In regard to the qualitative portion 
of my research, behavioral scholars could attempt 
to determine if the hacking metaphor has more of 
an impact on the Fear Appeals Model as a primary 
input or as an independent social influence variable.  
This would shed more light on how the metaphor 
functionally creates certain implications. 

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