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Cheaters on Twitter: an analysis of engagement approaches of
contract cheating services
Alexander Amigud
Center for the Study of Social Processes, Toronto, Canada
ABSTRACT
This paper presents the results of an exploratory study that examined
engagement approaches of contract cheating services on the Twitter
platform. The literature portrays the grey academic market as an
invisible hand that magically delivers academic content at the click of a
button, which leaves a wide gap in our understanding of their outreach
efforts. To this end, data from 71 contractors and 12,701 users were
analysed to describe what academics and administrators are up against.
The results suggest that contractors employ automation tools to
generate leads, specific to their subject area, which denotes market
segmentation and competition. This study is part of a larger project
aimed at minimizing academic misconduct; it discusses implications for
educational practice and offers recommendations for prevention of
contract cheating.
KEYWORDS
Higher education; academic
integrity; contract cheating;
plagiarism; collusion
Introduction
The ubiquity of social media has been extensively discussed in the literature. Its interactive features
simplify dissemination of information and collection of corresponding feedback. It has been inte-
grated into academic services to provide student support, facilitate collaborative learning, and
promote engagement (Grosseck and Holotescu 2008; Junco, Heiberger, and Loken 2011; Gülbahar
et al. 2017). Social media is also being used as a marketing platform to promote contract cheating
services to students (Rowland et al. 2018). Contract cheating is arguably the most challenging
issue faced by academic practitioners. It is defined by Clarke and Lancaster (2006)as‘the submission
of work by students for academic credit which the students have paid contractors to write for them’.
Academic integrity approaches and technologies that address the issue of authorship by mapping
learner identities to their academic work are selectively employed by institutions (Amigud et al.
2018). Due to their resource intensiveness, they are generally reserved for high stakes assessments.
This leaves a wide gap in the detection of academic misconduct and contract cheating in particular.
This gap, coupled with pressure to succeed, fuels the demand for original content, which translates
into business opportunities to satisfy the demand. Social media became a double-edge sword –the
very interactive functionality of the social media that helps facilitate communication and collabor-
ation is turned to identify and provide immediate response to students seeking to outsource aca-
demic work.
Much of the literature discusses the problem of contract cheating in terms of ethical conduct and
student and instructor perceptions (Walker and Townley 2012; Sivasubramaniam, Kostelidou, and
Ramachandran 2016; Draper and Newton 2017; Tauginienėand Jurkevičius 2017; Harper et al.
2018). From a handful of empirical studies, we know that there is an abundance of contract cheating
© 2019 Society for Research into Higher Education
CONTACT Alexander Amigud aamigud@uoc.edu
STUDIES IN HIGHER EDUCATION
https://doi.org/10.1080/03075079.2018.1564258
services, that they are business ventures with an online presence, and that they employ a range of
persuasive features such as refunds and 24/7 support to make their services appeal to customers
(Rowland et al. 2018; Ellis, Zucker, and Randall 2018). What we do not know is how contractors go
about engaging with students. Essays, research papers and math homework assignments are not
going to sell themselves and there is more than one player offering the service, which entails com-
petition and therefore variance in engagement techniques. The current gap in the literature is a lack
of information on contract cheating services social media engagement and the means to counter
their pervasive influence.
This research is important and timely because first, contract cheating poses a threat to the credi-
bility of the academic enterprise, and, second, it increases our understanding of operating structures
of service providers leveraging social media to maintain customer engagement.
Research questions
This study explores three research questions:
(1) What are the characteristics of Twitter accounts that offer contract cheating services?
(2) How do contract cheating services identify potential customers?
(3) How do the engagement approaches vary across contract cheating service providers?
To this end, this study employed a mixed-methods approach to identify contractor characteristics,
explore customer selection criteria, and analyse their engagement approaches using data from 71
contractors and 12,701 users on the Twitter platform.
The next sections provide an overview of the literature on academic integrity, explore the issue of
contract cheating, and discuss Twitter engagement.
Background
This section is organized into two parts. First, it provides an overview of the notions of academic
integrity and contract cheating. Second, it explicates the process of engagement using the Twitter
platform.
Academic integrity
Learning requires a significant investment of effort, time, and often financial resources which, due to
the competitive nature of the academic environment and substantial work load, come with the
inherent risk of failure and inability to recoup the investment. The pressure to succeed, heavy work-
load, and personal factors limiting the students’ability to complete the work may influence students’
decision to cut corners (McCabe and Trevino 1996; Malgwi and Rakovski 2009; Lewellyn and Rodri-
guez 2015). Therefore, cheating is a path of least resistance and a means of self-preservation. A
study by McCabe, Trevino, and Butterfield (2001) suggests that self-reports of unpermitted collabor-
ation go as high as 49%.
Institutions are well aware of the risks to assessment integrity and employ a variety of strategies to
counterbalance student creative efforts to undermine assessment protocols (Amigud et al. 2018).
However, academic integrity strategies vary in their level of assurance and logistical requirements,
are therefore applied selectively, thus leaving plenty of room for misconduct opportunities that stu-
dents and contract cheating services can exploit. Academic integrity requires a process that maps
student identity to the student-produced content, where only those students who successfully com-
plete the assessment task receive a credit. This two-tiered process, often conducted though invigila-
tion, is a logistically challenging task and therefore applied selectively. The rest of the assessments is
generally conducted in a less secure manner, where either identity or authorship is verified. Unless
2A. AMIGUD
instructors employ stringent checks and balances to validate authorship of the submitted work, any
activity bears a risk of being produced by anyone other than the student claiming credit for it. Auth-
entic assessments, such as programming projects and critical essays often undergo plagiarism
screening that deems work originally as long as it is significantly different from the content in its data-
base. Bespoke essays and assignments are original works whose authors agreed to forfeit authorship
claims in exchange for a fee. They will not be in the plagiarism database; therefore, these plagiarism
detection softwares are not effective in detecting misconduct (Walker and Townley 2012; Sivasubra-
maniam, Kostelidou, and Ramachandran 2016). Contract cheating is different from other threats to
academic integrity in that it is both convenient and presently has a low risk of detection. The contract
cheating market is eager to capitalize on these opportunities. The next section describes the notion of
contract cheating in greater detail.
Contract cheating
The notion of contracting out academic assignments was examined by Clarke and Lancaster (2006)in
their seminal paper that coined the phrase ‘contract cheating’, which was defined as ‘the submission
of work by students for academic credit which the students have paid contractors to write for them’.
This is the definition that will guide this study. Walker and Townley (2012) argued that contract cheat-
ing is a more fraudulent form of misconduct than other forms of plagiarism and offered an alternative
definition. They asserted that contract cheating is a form of cyber-pseudepigraphy –the act of claim-
ing authorship of someone else’s work obtained through the Internet. They also stress that content
may not necessarily be custom-written but also produced and submitted as part of another project.
Some of the common services of the contract cheating enterprise are bespoke essays, research
assignments, and computer programming code, among others (Clarke and Lancaster 2006; Clare,
Walker, and Hobson 2017). The extent of the contract cheating market is subject to ongoing
debate. Considering the sensitive nature of transactions, there is not enough public information avail-
able to make an accurate estimate of the transaction volume and subsequently market size. The
attempts to quantify the extent of the problem are generally limited to publicly available data,
such as the volume of offers and price structure, which is very different from the volume and
price of completed transactions. For example, a study by Clarke and Lancaster (2013) estimated
the average cost of student assignment at £138.39 GBP. A study by Owings and Nelson (2014) esti-
mated the size of the bespoke essay industry at over $50 million USD per year, assuming that 2.3% of
students outsourced only one paper. The results of a survey conducted by Bretag et al. (2018) suggest
that 2.2% (N= 13,462) of students had obtained and submitted someone else’s work as their own. A
literature review by Curtis and Clare (2017) on the prevalence of contract cheating for the period from
2006 to 2016, suggests that misconduct rates range from 0.3 to 3.1% (N= 116–413). Foltynek and Kra-
likova (2018) examined the prevalence of contract cheating behaviour in Czechia by conducting a
survey of N= 1016 students. The results suggest that 7.6% (N= 77) submitted assignments com-
pleted by someone else. A survey of teaching staffconducted by Harper et al. 2018 at eight Australian
universities examined faculty experiences with contract cheating. The results revealed a set of out-
sourcing behaviours, some more common than others. For example, 67.6% (N= 619) of faculty sus-
pected students submitting assignments and claiming credit for work they did not complete,
whereas proxy test taking was less common, with only 5.0% (N= 43) of faculty reporting experiencing
this behaviour.
Some of the proposed solutions to the problem of contract cheating include education as a means
to foster integrity culture (Walker and Townley 2012; Rogerson 2017), assessment design (Lancaster
and Clarke 2014; Sivasubramaniam, Kostelidou, and Ramachandran 2016), legislation (Draper and
Newton 2017), and continuous validation of student work (Amigud et al. 2018; Harper et al. 2018).
One may argue that the efficacy of an educational approach to deter academic misconduct is
limited and that automated methods of detection are necessary (Clarke and Lancaster 2006;
Amigud et al. 2017). Some scholars have proposed shortening turnaround time as a means to
STUDIES IN HIGHER EDUCATION 3
address contract cheating (O’Malley and Roberts 2012). This notion was examined by Wallace and
Newton (2014) who argued that imposing time constraint may not yield the desired results
because of the market saturation and short turnaround times already offered by contract cheating
services.
Furthermore, the use of social media marketing by contract cheating services has been noted by
Sivasubramaniam, Kostelidou, and Ramachandran (2016). Social media is playing an important role in
sales prospecting and customer engagement. One reason for favouring social media marketing over
search engine marketing is that the latter are known to block contract cheating ads. Their study
results suggest that contractors are targeting Twitter and Facebook users, offering help with assign-
ments, coursework, and dissertations. Contractors provide assurance of plagiarism-free work that is
undetectable by the plagiarism screening software, which denotes their understanding of academic
integrity practices. The study also highlighted the pervasiveness of social media, allowing contractors
to post service offers right on a social media page of an academic institution.
Bespoke essays, theses, and programming assignments do not spontaneously emerge on students’
desks; they need to be first procured, then produced. Establishing and maintaining a contractual relation-
ship is a complex and fragile process. As with any other business enterprise, contract cheating services
require a medium to engage with customers, communicate requirements, and negotiate the terms.
Social media provides just that. The next section provides an overview of social media engagement.
Twitter engagement
Customer-facing organizations recognized the power of social media technology as a multifaceted
communication tool that combines features of the traditional media –pushing content to customers
and also interactive media –establishing a two way communication with customers, which Felix,
Rauschnabel, and Hinsch (2017) described as an explorer approach. The collection and analysis of
user-generated content have many applications from customer insights (Ahuja and Shakeel 2017)
to engagement (Venkatesan 2017), from collaboration (Junco, Heiberger, and Loken 2011)toinfluen-
cing of purchase decisions (Barcelos, Dantas, and Sénécal 2018). As social media users go about
experiencing the world, sharing their observations, concerns, and wants on social media, their mess-
ages are available to be mined and responded to with offers to satisfy their wants, with solutions to
satisfy the concerns, and with opinions about their observations.
Twitter, a key player in the social media arena, has 330 million users worldwide (Fiegerman 2017).
The company offers free and paid services to reach specific audiences, increase brand awareness, and
analyse engagement. One explanation of its wide adoption is that the communication platform facili-
tates the ‘intrinsic need to form relationships with other people …by sending tweets and direct
messages, retweeting, following people, and gaining followers’(Chen 2011).
Considering that contract cheating services are for-profit ventures, Twitter engagement serves as a
means to generate customer leads. The process of engagement can be organized into six categories
as follows: (a) users can post messages on their profile pages; (b) users may reply to messages of other
users; (c) users may send private messages to other users, and messages, both public and private,
may include URLs and audiovisual content; (d) users may mark messages as favourites, or give
them ‘likes’; (e) users may subscribe to or ‘follow’other users; and (f) users may run search queries
that return messages with search terms.
Unlike other social media platforms, Twitter delineates users by interests therefore, engagement
requires understanding how to derive behaviours from textual and visual data. Considering a large
volume of content continuously generated by millions of users, automation is often necessary to
make the engagement process effective. Twitter provides an Application Programming Interface
(API) to integrate with its system and automate many of the management tasks such as searching
and posting messages. For example, a reply bot may be deployed to identify keywords in a
stream and automatically send a message to that user.
The next section outlines the methodology used in this study.
4A. AMIGUD
Methodology
This study is predicated on the assumption that contract cheating is a business venture with the aim
to monetize students’inability or unwillingness to satisfy academic requirements on their own. There-
fore, their engagement approaches aim to maximize the profit gain. Although contractors have
access to the same interactive features on the Twitter platform, their leads generation patterns
may vary. Using a mixed-methods approach, this study analysed the engagement approaches of con-
tract cheating services in two phases. First, public profile data were analysed to summarize contractor
characteristics and identify criteria used in customer selection. Second, data from Twitter messages
that attracted offers from contractors was analysed to take a closer look at their engagement
approaches.
The data were collected from February to March of 2018. Analysis was performed using the Python
language. The snowball sampling technique was employed as follows. Data collection commenced
with a search query for ‘essay’, which returned an array of messages. These messages were examined
to identify contract cheating services which replied to or marked these messages as favourite. Results
of the Twitter’s recommender algorithm ‘who to follow’was also used to draw additional data. Figure
1depicts the sampling process, which yielded a list of N= 71 contractors. For each contractor, Twitter
profile information was collected to compare their usage (number of messages, followers, accounts
being followed, likes), alternative contact details (website, email, phone), cost of service, location, and
maturity of accounts. Contract cheating services vary in size and maturity of business. Their usage
volume (i.e. likes, follows, and tweets) was normalized and expressed as the number of daily trans-
actions for each type of activity. Location, part of the public profile, is an optional field. It may be
left blank or include an arbitrary string, for example ‘Why do you want to know?’Entries that did
not include location names were discarded; the remainder was organized by the country.
With millions of users continually posting messages, the process of identification of prospective
customers becomes a challenging task. In order to reach customers effectively, contract cheating ser-
vices need to derive a technique to efficiently delineate related content from noise. A set of 12,701
messages from unique users was analysed to identify the most frequent keywords that contract
cheating services consider as indicators or markers of the prospective customer. For each contractor
in the list, messages marked as favourite were downloaded. The inclusion criteria were: an account
active within a month and had at least 500 likes, which yielded a subset of 45 contractors. Due to the
Figure 1. Sampling method.
STUDIES IN HIGHER EDUCATION 5
competitive nature of the contract cheating business, there is an overlap in message selection criteria,
and several contractors engaged with the same user. Bigrams were computed using the NLTK library
(Bird 2006) as follows: stop words removed, word length ≥3 characters, collocation window size = 2,
frequency filter value that limits the output to ≥10 items, and likelihood ratio scoring (Manning and
Schütze 1999).
In the second part of phase two, the top keywords were used as an input to elicit offers from con-
tractors and examine their engagement approaches. The keywords were used in a sentence and the
message read: ‘I have 2 page essay paper due & I haven’t started it. I will pay someone $50 to write
research paper & do my stats homework math homework hate statistics hate calculus. Assignments
due tomorrow’. The message was posted for 24 h and interactions logged.
To increase the accuracy of data capture a new account was created. Any new contractors ident-
ified in this phase were not added to the initial list. The sample size, type, time of data collection, and
data format are the limitations of the study.
Results
The presentation of findings is divided into three parts: (a) contractor characteristics; (b) relevance
markers; and (c) the engagement approaches, each part answering its own research question.
Contractor characteristics
The first research question involved exploring characteristics of Twitter accounts that offer contract
cheating services. The oldest contractor’s account in the sample (N= 71) is 8.5 years old, and the
newest account is 5 days old. The mean maturity of contractors’accounts is 22.25 months (SD =
21.94). Account maturity is depicted in Figure 2. The majority of profiles indicated they are located
in the United States. The second largest market is the United Kingdom, followed by the Republic of
Mauritius and Kenya. Contractor-specified location is depicted in Figure 3. Contractors generate on
average 28.98 (SD = 89.83) tweets/day, issue 22.86 likes per day (SD = 55.18), and follow other users
at a rate of 5.52 (SD = 10.13) users/day. The highest level of daily engagement was as follows: 743 mess-
ages, 375 likes, and 73 users being followed. Engagement trends are depicted in Figure 4.
The service cost was made explicit in 4% (N= 3) of the profiles and ranged from $5 to $10 per
page. Twitter was used as a storefront by 44% (N= 31) of the contractors, who did not provide
any alternative contact information such as phone, email, or website link. The remainder posted a
combination of website addresses 56% (N= 40), email addresses 35% (N= 25) and/or phone
numbers 7% (N= 5). All phone numbers had US area codes. None of the contractors in the sample
had all three contact particulars posted. Two accounts provided a link to a mobile academic help
app. The contractors’websites can be divided into three categories: those that offer services directly,
those that provide a freelance marketplace, and those that review quality or make recommendations
of contract cheating services. The last type, which is 4% (N= 3), post messages warning students
about unscrupulous contractors which serves as a strategy to deter competition, while promoting
affiliated service providers.
The profile data highlights general trends; for instance, we know that contractors automate
response to messages and use some criteria to filter out the prospects from the rest. The next sections
take a closer look at how they are doing it.
Relevance markers
The second research question analysed how contract cheating services go about identifying potential
customers. Considering a vast volume of user-generated data, contract cheating services face the
challenge of being able to accurately identify students seeking to outsource their academic work
while filtering the noise. This entails having a clear idea of the terms students seeking assistance
6A. AMIGUD
would include in their messages. The balance is key, as a single keyword can cast too wide of a net,
while irrelevant terms limit the search leading to loss of valuable computational resources and time.
The more time spent engaging with unrelated content, the less are the chances of generating leads
and making a profit. This means that contractors review and optimize their engagement strategies,
and the observed variance in the use of keywords highlights just that. Tables 1 and 2depict the most
frequent terms in two categories: unigrams and bigrams; the latter were computed to narrow down
potential customers. Two contractors use two different approaches to engage with different groups
of customers, one is targeting essays, research papers, and assignments, and the other math, stat-
istics, and calculus. The term ‘hate’is related to math homework but is not present in writing
Figure 2. Account maturity.
Table 1. Top 10 unigrams.
Contractor A Contractor B Entire set
Due Hate Essay
Essay Math Pay
Paper Homework Write
Write Starts Someone
Tomorrow Odio Paper
Assignment Pay Due
Started Someone Homework
Assignments Statistics Tomorrow
Page Much Page
Someone Matematica Research
STUDIES IN HIGHER EDUCATION 7
assignments. Furthermore, as shown in Table 1, the presence of Spanish terms suggests that some
contractors are targeting non-native English-speaking students.
These terms will serve as input for the next phase that is concerned with analysing the engage-
ment approaches.
Engagement approaches
The final research question assessed how the engagement approaches vary across contract cheating
service providers. To examine contractors’engagement approaches, a message composed of
Figure 3. Contractor-specified location.
Table 2. Top 10 bigrams.
Contractor A Contractor B Entire set
Due tomorrow Hate math Pay someone
Paper due Pay someone Write essay
Assignment due Stats homework Someone write
Research paper Hate calculus Essay due
Haven’t started Fucking hate Research paper
Write essay Statistics homework Due tomorrow
Assignments due Hate stats Write paper
Essay due Hate statistics Paper due
Page paper Math hate Essay pay
Write paper Math homework Page essay
8A. AMIGUD
keywords identified in the previous section was posted for 24 h. Within the first 5 min of being
posted, the message received 9 replies, 7 likes, and 5 follows. After 3 h, the engagement rate rose
to 19 likes, 26 replies, and 11 follows. In 12 h, the engagement rate was at 25 likes, 37 replies, 17
follows, and 1 retweet. After 24 h of being posted, 36 unique contractors engaged with the
message through 44 replies, 27 likes, 16 follows and 1 retweet. Figure 5 depicts the engagement
activity.
The data denotes the use of automation tools that not only continuously monitor the stream and
engage with messages immediately after they are posted but query historical data either on-demand
or at certain time intervals. This variance suggests the existence of three different approaches to
engagement: (a) some contractors monitor the stream continuously, (b) others query historical
data periodically, and also (c) on-demand. These require different resources and levels of human
involvement. Human responses, characterized by the personalization of messages, low volume of
Twitter activity, and breadth of responses, were observed in five cases. There was one new Twitter
account opened for the purposes of responding to the offer. This account contained a single reply
message and was not part of the initial sample. This depicts the ease with which one can start a con-
tract cheating business, using social media as a virtual storefront. There is no office, website, or phone
number required. By the same token, this suggests the potential risks to students as social media
accounts can be closed with equal ease. During the 3-week period of data collection, four accounts
were removed, one account was suspended, and one account was renamed. This denotes volatility of
the contract cheating landscape, which can be used as an advantage in the design of prevention
strategies. This will be touched on in the discussion section.
Replies were the preferred approach in this sample, followed by likes, follows, and retweets.
However, this ranking may vary with changes in sample composition. The majority of the contractors
used a plain-text message, asking to contact them privately (i.e. ‘DM’). A website link was included in
Figure 4. Engagement trends.
STUDIES IN HIGHER EDUCATION 9
the reply of 1, and 2 contractors attached images promoting their services as a form of visual adver-
tising. Replies with images are more visible than their plain-text counterparts. A phone number was
included by 1, and 3 contractors provided email addresses in the replies. Quality and originality were
stressed by 4 contractors (e.g. ‘high quality paper in good time with no plagiarism’). Furthermore, the
results suggest specialization among contractors; some offer calculus, math, and stats help, while
others offer assistance with written assignments, such as essays. This means that when bots are
used, they are configured to respond to different keywords with different messages, a finding that
is consistent with the analysis of the relevance markers.
Issuing ‘likes’is another popular approach. These are often used in conjunction with replies. Fol-
lowing users and reposting (retweeting) their messages are far less common engagement
approaches. Neither approach is explicitly offering a service but trigger account notification, thus
appealing to students’curiosity to explore who engaged with their message. Following a user and
reciprocal following, although not readily apparent, is a strategy that enables the exchange of
private messages.
These findings denote differences in engagement approaches. The following section discusses
their implications for the educational practice and offers recommendations.
Discussion
This section integrates the findings and presents an overview of what institutions are up against in
terms of potential risks to academic integrity. It discusses implications for the educational practice
and offers recommendations for addressing the issue of contract cheating.
Within minutes of posting a message requesting assistance with completion of an academic
assignment, contract cheating services embarked on a competitive quest to win the contract.
Figure 5. Engagement activity.
10 A. AMIGUD
There are two key trends that emerged in this research –the use of bots and competition. Bots make
engagement more efficient, and we see a large difference in the volume of daily transactions
between contractors who employ bots and those who do not. The use of bots denotes that contrac-
tors are tech-savvy and can make use of programming resources. This is an important characteristic
because of their potential to automate the production of academic artefacts at scale. Once they
perfect the methods for computer-assisted plagiarism and thus increase efficiency, the ability to
compete on costs and fulfil orders within tight deadlines –a weak point in their current operating
model –academia will have a much more grave concern about integrity than it does now.
Furthermore, the logistical complexity of automation suggests that contractors not only need to
be merchants but also creative problem solvers. In order to utilize the power of targeted outreach,
contractors have to go through a research process that yields a list of markers that separate leads
from noise. The process is likely to commence with an intuition as to what language a student
might use to signal demand for academic help and later undergo refinement to increase efficacy.
This takes time and resources. Considering the presence of duplicate messages in the original
dataset, the markers were not refined far enough, which resulted in an overlap and competition
where contractors engage with each other. This is a weakness in their strategy.
Heightened competition, coupled with an overlap in markers, renders the use of bots counter-pro-
ductive. Although it is important to reach a prospective customer as soon as possible, and bots
provide just that, the messages are displayed in chronological order, and excessive engagement
makes it difficult to see the earlier messages positioned at the bottom. To address this issue, contrac-
tors replied more than once, which increased their visibility and at the same time created a spam
problem.
Because students do not receive just one offer but are flooded with multiple offers and engage-
ment notifications for hours that claim to provide ‘legit’and ‘plagiarism-free’services, what was sup-
posed to be a targeted campaign ended up being a nuisance. Contract cheating services are running
a continuous risk of being reported for spam, which will result in account suspension. During the
short period of this study, several accounts were closed or suspended, and new accounts were
created. This denotes market volatility, which works against contractors. There is no assurance that
any work will be done after students remitted the payment, let alone that the contractor will
remain in business to provide assistance for the rest of the semester. Therefore, contractor screening
and procurement are continuous processes.
Contract cheating is a process constrained by the environment in which it operates. This is the
point that should be stressed most often. Competition and service volatility are two limiting
factors that place constraints on contractors’abilities to conduct business. Bespoke papers are not
one click away, because there is a need to evaluate the available options. Assignments have require-
ments, and students need assurance that the work will be done right and delivered on time. The
student is facing a decision about whom to hire, which involves establishing trust, negotiating the
terms, making a payment, then following up on the delivery. It is counter-productive to spend pre-
cious time sifting through spam when the deadline is looming. Wallace and Newton’s(2014) argu-
ment that shortening turnaround time will not affect contract cheating behaviour needs to be
further examined. Much of the work is already being requested with a very small window (i.e. ‘due
at midnight’,‘due tomorrow’), rendering order fulfilment rather difficult. On the one hand, time
restrictions are expected to create a barrier to finding a suitable contractor, in spite of the market sat-
uration. However, students with a tendency to procrastinate will likely remain true to their habits,
holding up until the last minute to outsource the work. On the other hand, reduced turnaround
time puts all students under greater pressure to succeed, which, in turn may trigger cheating behav-
iour in students who otherwise would have done the work themselves.
Furthermore, the legal frameworks targeting contract cheating services (Draper and Newton 2017;
Tauginienėand Jurkevičius 2017) will not be effective, as contractors could move jurisdictions or
reframe their services as tutoring at short notice. Because ads can be disguised as news or personal
STUDIES IN HIGHER EDUCATION 11
messages, the use of social media for customer prospecting and engagement will become more
prevalent.
Implications for the educational practice
Whilst contract cheating is a serious problem that erodes institutional credibility, to date, its true
extent has not been gauged due to the lack of replicable data. The sheer volume of advertising
creates a temptation to conflate sales efforts with academic misconduct, and both the academic lit-
erature and media seem to make this logical fallacy. It is important for academic practitioners to
remain critical about the issue. Intentions to outsource academic work, as a means to succeed or mini-
mize cognitive effort, may not extend beyond curiosity, as the procurement process has inherent
limitations and risks. Assuming that cheating is a path of least resistance, plagiarizing someone’s
work or colluding with friends and family members are more accessible and therefore more prefer-
able than hiring a contract cheating service. This argument is supported by recent survey results that
suggest that students are more likely to collude with friends, family, or classmates (Bretag et al. 2018).
Some scholars even placed family and friends as a subtype of contract cheating, along with file-
sharing and proxy test taking (Ellis, Zucker, and Randall 2018). What follows is an argument that
future research should examine: contract cheating is not a more egregious form of academic miscon-
duct –that evolved from plagiarism –but merely another tool that is available to some students to
get the job done. The commercial nature of the contract cheating enterprise affects accessibility. The
price is a discriminating factor, which means that not everyone is able to afford it, particularly when
the complexity of academic work is proportional to the contract price. By the same token, specializ-
ation is a discriminating factor, because not every subject matter expert is equally accessible.
Past research suggested that prevention should focus on raising awareness; however, this study
denotes a gap in academic integrity communication strategies. Academic integrity is treated as an
issue that only exists on campus; the onus is on students to remain virtuous outside the teaching
hours. There is no evidence of any efforts to counterbalance the engagement of the contract cheating
services on social media. Out of 44 replies and 27 likes that the contract offer received, none were
from academic sources that warned students about the potential risks or repercussions of cheating.
Perhaps practitioners may learn engagement techniques from contract cheating services, in an
attempt to counter their persuasive power and extend academic integrity awareness to social media.
The crux of the matter is that academic institutions do not appear to be proactive about academic
integrity strategies, which gives cheaters an upper hand. In spite of the availability of open technol-
ogies that validate authorship and can address the issue of contract cheating, very few pilots have
been conducted, while plagiarism detection tools remain the go-to solution for the bulk of the
written assessments. Contract cheating will prevail for as long as this gap remains open. The next
section discusses opportunities for future research.
Conclusion
This study examined the engagement approaches of contract cheating services that target students
on Twitter, discussed implications for the educational practice, and offered recommendations to miti-
gate the risks of contract cheating. This study demonstrated that within minutes of being posted, a
social media message offering payment for completing academic work prompted contractors to
engage and compete with each other. Although these figures are concerning, they should not be
taken as a direct measurement of the problem. To better understand the issue of contract cheating,
future research should focus on personal and environmental factors that facilitate and limit the use of
contract cheating services. First, it should identify the reasons why students find themselves in a pos-
ition to outsource the work. Second, the research should compare the quality of work across a sample
of contractors and analyse textual data for computer-based plagiarism. For many students the choice
of a trusted partner in crime will not go outside of their circle of friends and family members, thus
12 A. AMIGUD
minimizing risks of exposure and costs. The future research should examine the role of the family in
academic cheating. Hiring a contractor suggests that students, or their families, can afford the service,
which, in turn, suggests that cheating methods are tied to economic status.
Furthermore, future research should bring academic integrity awareness outside of the classroom
and examine the effects of these efforts. It would be interesting to examine the use of ethical bots
that employ the same techniques that contract cheaters do, in order to raise awareness among stu-
dents about the potential risks and long-term effects of cheating. Lastly, the future research may
examine the impact of the legal frameworks aimed at curbing contract cheating services. These
laws will be difficult to enforce, considering that many contractors use social media as virtual store-
fronts and may change jurisdictions within hours of being shut down or reframe their business
models to imply academic support and tutoring services. Legal pressure, if applied, will force contrac-
tors to evolve and adapt. This, in turn, might give rise to a new business model and never-before-seen
permutation of the academic grey market. This study argues that many contractors are not just mer-
chants, but are innovators and disruptors who appear to be one step ahead. If academia holds any
hope of solving the problem of contract cheating, it should start examining the operational processes
of contract cheating and get involved in the development of technologies that validate authorship.
Disclosure statement
No potential conflict of interest was reported by the author.
ORCID
Alexander Amigud http://orcid.org/0000-0002-2347-3172
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