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Running Head: Student athletes and the influence of social media
Persuasion by Centrality: A Social Network Analysis of 2016 UVA Football Recruits
Billy Skrobacz 05/06/13
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Intro: In a world of e-‐bills, e-‐mail, and dying newspapers, the increasing prevalence
of Internet profiles in the public eye is unavoidable. For example, not only are
people associating themselves with an online identity known as a “twitter handle,”
but they are also making these virtual identities public, sharing some of their most
personal, and in some cases extreme opinions and beliefs with their friends,
acquaintances, and followers. The share of such personal content through online
identities is elevated to a whole new level when the people behind the “handles” are
future NCAA student athletes. Not only are these athletes already under the strict
microscope of the public eye for their work on the field or court, but also their use of
social media creates full-‐time accessibility from people who are not in their
immediate circle of friends and family. This easily accessible and opinionated
content, created by the use of social media, plays a pivotal role in the persuasive
influence that student athletes have over their peers.
While intercollegiate student athletes have a governing body in the NCAA
that monitors current team and player accounts for punishable content, this
authority has no control over prospective high school student athletes. So, though it
is illegal for a current NCAA athlete to directly contact a potential recruit via social
media, the NCAA has no control over recruits contacting other potential prospects
throughout the college recruitment process. Therefore, thanks to social media, high
school athletes have all of the accessibility tools that they need in order to begin
recruiting and persuading their peers into making a college decision. While the
mode of communication used by today’s prospective athletes is unique, this process
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of peer-‐recruiting is not, and frequently happens in revenue sports where signing
recruits is a high-‐stakes business for coaches and athletics departments. Therefore,
when coaches begin recruiting potential prospects, they will often look at that
prospect’s immediate circle of friends. This search, depending on how sought-‐after
the prospect is, occasionally results in a scholarship offer for the less reputable
friend with hopes that this can persuade the more coveted athlete to attend their
institution as well. The best example of this can be found in the recruiting class of
the 2012 University of Georgia football team, where Quayvon Hicks was offered a
scholarship in order to lock down a commit from a high school teammate and friend,
Isaiah Crowell (Towers, 2011). Though this worked out for UGA as Hicks became a
highly touted player, coaches more frequently find themselves wasting a scholarship
with the hopes of persuasion.
This study, however, seeks to save coaches from wasting these valuable
scholarships, by finding the most persuasive connections through a social network
analysis of prospective athletes’ Twitter accounts. If coaches can use this analysis to
find out which recruits are the most popular amongst the rest of the recruiting class,
then they should be able to focus more energy on getting that popular athlete to
commit, and ultimately reap the benefits of this athlete’s influence over his or her
athletic peers. While this study will not be a content analysis of prospect’s Tweets, it
will analyze athletes’ followers in order to find and exemplify the strength of the
central nodes of communication within a particular recruiting class. This
identification will paint a picture for coaches and athletic administration as to which
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prospects are worthy of more time on the recruiting trail, and which prospects can
be more easily obtained through peer interaction via social media activity.
Review of Literature:
The growing prevalence of social media use comes with many unexpected
full-‐time implications for the student athletes who are logged in to these various
web based sites. For the purposes of this study, social media is defined as “a group
of Internet-‐based applications […] that allow the creation and exchange of User
Generated Content (UGC)” (Kaplan & Haenlein, pg. 61, 2010). This content is
continually modified by the user, and is presented within a framework where all
users have equality in sharing information (Clavio, pg. 310, 2011). Equality here
refers to the universal ability for members of a particular social media network to,
both, produce and access to information.
UGC can be analyzed through two frames. While a content-‐based frame
pertains to actual messages generated by users, an audience-‐based frame applies to
who receives these virtual stimuli (Clavio, 2008). Though recruiting prospective
athletes relies partly on the content and information that a coach or peer recruiter
provides, actual content, or individual Tweets produced by the sample population,
does not fall within the realm of research for this particular study. Instead, audience
based inquiries will be examined in order to provide data on which fellow recruits
are receiving particular athletes’ UGC, and what type of uses, gratifications, and
general reactions are experienced from consumers of this content (Clavio, 2011).
Uses and Gratifications Theory
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According to the uses-‐and-‐gratifications theory (Katz, Blumler, & Gurevitch,
1974), media use revolves around achievement of specific goals. Thus, media
consumers have a motive and preference which drives the consumption of a
particular source, and that motive typically revolves around the benefits received
upon utilizing that particular media outlet over another. While media producers use
highly trafficked avenues as a means of getting messages across to interested
parties, media consumers “frame what they stand to gain from consuming a media
channel, or what they will lose by avoiding […] that media source” (Browning &
Sanderson, 2012). Essentially, producers cast a wide net in order to gain an
audience, and consumers sift through these various sources in order to find the
outlets which are most personally beneficial.
Gratifications Rooted in Accessibility
Though the Uses and Gratifications theory originated from traditional media
use (i.e. news papers, radio shows, and television networks), Ruggiero applied this
theory to the realm of social media, claiming that the Internet extends full time
interactivity, demassification, and asynchroniety (Ruggiero, 2000). Therefore, social
media users have complete discretion over what is addressed, no time constraints
as to when UGC is presented, and even have a direct method of contacting these
content producers. These freedoms of content, time, and interactivity play a majorly
persuasive role for prospective student athletes. For example, a prospective athlete
in California can explain why he committed to a particular school via Twitter, in one
hundred and forty characters or less, and athletes all along the east coast can
instantly receive and respond to that opinionated information.
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This increased access has both benefits and implications for student-‐athletes
on many levels. Not only can consumed information persuade these athletes to
attend a particular college, which could be seen as beneficial depending on the
institution, the accessibility factor subjects athletes to critical tweets and direct
messages from angry fans or nosey reporters. These critical tweets are also
persuasive for a media consumer’s behavior due to the invoked emotional effects
and responses. Critical stimuli often warrant a self-‐defense rebuttal, which places
the athlete under strict scrutiny from coaches, school administrators, and the NCAA
(Browning & Sanderson, 2012). Along with the negative effects resulting from
player-‐observer interaction is the negative implications of player interaction across
teams. On December 10, 2011 the University of Cincinnati and Xavier University
basketball teams ended a regular season matchup in fisticuffs, stemming from
negative twitter interactions between players on each team (DeCourcy, 2011).
Players were suspended and teams fined, but this event could have been avoided
altogether if it were not for the accessibility created by combined the use of social
media.
With Access Comes Power
While these aforementioned events prove that the access and interaction
capabilities of social media users can influence behavior, a deeper look into media
and social influence in general will prove that exposure to content also plays a
pivotal role in adolescent behavior. A 2010 study by an online edition of Pediatrics
shows an increase in prevalence of product placement in 200 of the nation’s top
grossing movies since 1995 (Sutherland, Mackenzie, Purvis, & Dalton, 2010). These
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products, which advertising agencies subtly placed throughout various scenes of the
studied movies, were largely from “energy dense, nutrient poor foods or product
lines” (Sutherland, et. Al, 2010). This trend of product placement is also directly
correlated with an increasing child obesity rate in America, and though there cannot
be a specific scientific link of these obesity increases to an increase in media
advertising, many connections can be made. For example, increased advertising
means increased knowledge of a particular product; and sometimes, knowledge of
existence is all one needs when it comes to making a choice between a known and
potentially unhealthy product, versus an unknown and potentially healthier food
choice.
Further, a Loyola University study of high school social networks further
exemplifies the power of social influence. This study “was designed to determine the
reason why obesity and related behaviors cluster in social networks” (Ritter, 2012).
Essentially, researches wanted to determine the power of social influence, or the
ability of peers to influence each other. This one-‐year study ultimately found that
students, who originally had Body Mass Indexes (BMI) higher than those of their
friends, had a 40 percent chance of lowering their BMI’s over the course of the year
(Ritter, 2012). Further, students who were socially connected with peers of a higher
BMI than themselves had a 56 percent chance of increasing their own BMI closer to
the levels of their friends over that same year (Ritter, 2012). Therefore, social
connection and peer behavior is also extremely influential in adolescent behavior
and choices.
Narcissistic Productions
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A combination of these previously examined influences are what my study
credits with persuasive power amongst student athletes. Though this study revolves
around a network analysis rather than content analysis, there are general
assumptions that can accurately be made when considering the UGC that is
consumed by recruits. Previous survey analysis of a large population of college
students done by McKinney, Kelly, and Duran (2010) showed that college aged
students frequently displayed higher levels of narcissism through self-‐centered UGC
postings. Further, social identities of prospective student-‐athletes are often
“grounded in attachments to teams,” thus meaning that both online and personal
identities of these athletes most commonly revolve around being self-‐proclaimed
members of a specific team and having an allegiance to that particular team
(Browning & Sanderson, pg. 506, 2012). Therefore, one can assume that the UGC
being posted by many recruits involves some sort of self and team marketing or
promotion. Furthermore, this type of athletic propaganda lays the foundation for the
creation of social norms and widespread acceptance within media networks; thus
making consumption of this information an influential factor in media consumers’
behavior. These assumptions of content, paired with the previously exemplified
influential power of peers and media within a social network, allow a strong
theoretical backing for the results of my network analysis which will show the
persuasive power of central communication nodes within a recruiting class.
Application to University Athletics
This study will be critical not only for coaches and recruiters, but for the
social media using student-‐athlete as well. While coaches stand to benefit from the
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network analysis knowledge, the student athlete benefits from knowing the
influence that he or she has via UGC on social media sites. For example, the poor use
of social media plays a major role in life beyond college athletics as well. While many
businesses differ from major league athletic clubs in the necessary qualifications of
who they hire to be the face of their organizations, there is little difference in the
type of expected character that each representative should have. The 2011 example
of Zach Houchins proves this dynamic. While he was drafted in his junior year of
college in 2011 to the Washington Nationals, he ultimately returned to school after
the resurfacing of racist tweets from an earlier time (Kilgore, 2011). Thus, student-‐
athletes are unknowingly, and more commonly than not, harming their future selves
by social media content and interaction. However, while accessibility and content
are more commonly associated with negative implications, not all social media
interaction generates negative responses.
Recruiting high-‐quality prospective intercollegiate athletes is a multi-‐million
dollar business that plays a major role in home game attendance, coaches’ job
security, and overall win-‐loss record. While this business of recruiting is regulated
with intentions of creating a level playing field amongst all participating schools,
coaches and programs will do whatever it takes to get a highly sought recruit to
commit to their institution. While the infiltration of a prospective athletes’ friend
base and home life is already a commonly used method by coaches across the
country, the infiltration of these athletes’ social media accounts is becoming a more
widely used practice. However, this method is not executed through direct
interaction, but instead by indirect persuasion. As one big-‐time recruit commits to
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an institution, coaches often ask for his help in bringing in other prospects from his
school, area, or skill set. While a form of this technique was most notably used by
coach Jimmy Johnson of the University of Miami in the late 1980’s, the nature of this
method has shifted (Corben, 2010). Now, instead of players calling each other in
order to connect, they can simply send out a recruiting pitch through a short Tweet
or message. Further, in accordance with the uses-‐and-‐gratifications theory,
committed recruits feel that they have taken part in the class building process, thus
developing ownership, power, and pride in their future schools (Hermans, Kempen,
& Van Loon, 2004). Hermans (2004) “observed that the expansion of digital media
escalates dialogical possibilities. That is, individuals become multi-‐voiced […]
offering one exposure to a wide variety of people, whose voices become part of ones
own private culture” (Browning & Sanderson, 2012). Therefore, this social media
interaction is a process, which not only allows for accessibility, but also eases
communication through an adaptation of cultures.
Summary
While the uses and gratifications theory provides justification for recruits to
use social media in order persuade and inform their prospective peers, this tool of
social media also provides many additional benefits for coaches and recruiting
coordinators alike. For example, if a Division 1 football team offers verbal
scholarships to seventy prospective student athletes, coaches will need to take time
out of their busy in-‐season schedules to call, visit, and send letters to all seventy of
these offered recruits. However, if this large pool of scholarship candidates has a
few people who are leaders or friends with the remaining members of the desired
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class, then the coaches would be able to focus more of their efforts on these few
prospects, and thus devoting more time to their current athletic season, with hopes
that this recruit will do his or her own part in building the rest of the class.
This aforementioned process would identify “which student-‐athletes are at
the ‘hub’ of a team’s Twitter activity and how their Twitter content shapes that of
their teammates” (Browning & Sanderson, pg. 518, 2012). Gathering this
information will provide a clear degree of importance that intercollegiate athletic
departments and teams will need to place on the marketing and communicating
processes of social media (Clavio, pg. 322, 2011). Thus, through a conducted
network analysis, athletic departments would be able to see which recruits are
social “lynchpins,” ultimately enabling the devotion of less administrative
manpower towards the building of a recruiting class, but rather allowing the
prospects to recruit themselves.
The concept of self-‐recruitment, or peers recruiting peers, can be attributed
to the Theory of Planned Behavior (Ajzen, 1991). This theory basically claims that
behavior can be predicted based on a person’s ability and intention. These
intentions, however, are subject to outside influence. Further, “elements of behavior
are influenced by subjective norms within a particular culture. If behavior is
considered normal within a group, it is generally more likely that an individual will
engage in it” (Clavio, pg. 313, 2011). Therefore, a heavily followed recruit can create
a social norm through his or her UGC which is then consumed by other athletes of
the same group, and thus potentially playing a persuasive role in the behavior of the
media consumers.
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Ultimately, this is a study of player interaction and connectivity. As this
review previously mentioned the negative results stemming from increased
accessibility, there are, in fact, positive benefits to the new accessibility that social
media use has created. Because this new style of recruiting has yet to be explored, I
wish to fill this void, using the sample population of University of Virginia’s 2014
Football recruiting class, by seeking an answer to the following question:
RQ1: Who are the central nodes of communication within the University of Virginia’s 2014 recruiting class? Methods: About The Study
This study uses easily compiled, accessible, and recordable data found on
subjects’ online twitter accounts. This study’s descriptive analysis design allows for
the empirical findings of a social network analysis (SNA) to be compared with the
strengths of the relationships within these findings. The SNA conducted here will
examine the degree centrality of peers within the same high school recruiting class.
The information found will provide insight as to which nodes, or recruits, are the
central nodes of communication within this aforementioned recruiting class.
Data Source
Using a relatively large sample size of eighty-‐seven verbally offered
University of Virginia football recruits from the high school class of 2015, we will be
able to asses the network of communication amongst the common peers. Using
information found on public but personal social media web pages, a comprehensive
database of “followers” will be compiled for each individual subject or node.
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Because most of the subjects have a twitter page which is open to all Internet users,
it is both legal and ethical to assess their account information without consent.
However, some of the subjects have private twitter accounts; this means that the
researcher will need to personally request to follow these subjects from another
account in order to gather the needed information. This request process, though, is
very routine, and can be done so through the simple click of a button and without
any direct contact between researchers and the individuals themselves.
The selection of this sample revolves around the athletes’ standings in their
recruitment processes, specifically with UVA. This study is most effective when the
recruiting class is at its largest size, meaning the coaching staff has given out the
highest amounts of verbal offers that it is going to give for the remainder of, in this
case, the 2015 class. Further, because the optimal time to conduct this analysis falls
between November and January, a large portion of the offered prospects will have
had opportunities to attend recruitment events, such as a UVA home football game,
which increases the probability that nodes will have had in-‐person contact with
each other, thus leading to a virtual connection as well. Essentially, it is very
common for recruits to exchange social media handles with their peers at these
recruiting events, and the timing of this study falls within the realm of possibility for
these exchanges to have occurred.
Instrumentation
The original pool of eighty-‐seven candidates was narrowed to 57 accessible
candidates after research was conducted on all of the original sample size. This
decrease in sample size was expected, considering that not everyone uses social
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media, and not everyone who does use social media has public profiles that have
their actual legal names attached to their accounts. Essentially, smart users
sometimes make it tough to find their Twitter handles, and others are not using
Twitter at all. Once the names of followers are collected and logged into an Excel
spreadsheet, it will be uploaded into MeerKat, a social network analysis software
(Borgatti, 2002). MeerKat makes the inserted spreadsheets symmetric, and preps
the data for its input into the NetDraw program. NetDraw then creates a visual of
the imputed data. This data will be measured by degree centrality, which indicates
the strength of individual connections as well as the strength of the overall network
(Wasserman & Fost, 1994).
In addition, network strength will also be determined by the measure of
network density; a comparison of actual connects versus all possible connections
within a network (Wasserman & Fost, 1994). Density can be calculated by the
formula: Density=[T/n(n-‐1)]/2; where “T” pertains to the number of ties within a
social network and “n” refers to the number of nodes (Wasserman & Fost, 1994).
Further, the resulting images and data from MeerKat and NetDraw will be analyzed
to find emerging patterns within the results, which can be used to illustrate the
utility of the yielded information.
Network Visualization
The visual mappings yielded by NetDraw are results of the information
which has been processed through Excel and Meerkat. The maps produced by
NetDraw will appear as a web of circles and bowed lines. Each circle represents a
UVA class of 2016 recruit and each line represents a Twitter connection, or follow.
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Two lines between two nodes will create a oval-‐like image, and this image indicates
two mutual follows between respective nodes. MeerKat and NetDraw allow for the
user to edit the color of nodes, thickness of lines, and overall visualization of the
network in order to make the image most effective for recipients. However, line
thickness will not be necessary for this visualization because instead of basing our
connections off of varying values of in and out degree connections, we value any
connection as 1, and any non-‐connection as 0. Therefore, the nodes with more oval-‐
like connections still have a higher value in the computed metrics, but do not show a
thicker line. This study will find the nodes with the highest degree of centrality, and
then asses whether or not this measurement is the most effective in determining
persuasion power, and critical relationship with the rest of the class.
While viewing the entire network will be important for the coach or athletic
department receiving this information, the ability to break down and isolate certain
people or relationships within the whole is crucially important to understanding
network cohesion. The individual or smaller group of individuals being studied, in
this case, is referred to as clusters. Cluster networks are compiled of an individual or
a group of individuals along with all of their connections. The groups or people with
the highest quality and number of connections are the individuals we are searching
to label as the main propeller of information and connectivity within the recruiting
class. The most connected, or followed node within the entire network will be
considered a high-‐value target (HVT), thus giving coaches and recruiting
coordinators players to focus on during the recruitment process. These clusters,
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however, will have separate representation and value throughout the “Results”
section of this discourse.
Definitions
A crucial part in being able to understand the visuals which are
manufactured by this study, is understanding the terminology which is used to
define relationships amongst clusters, nodes, or entire networks. The Indegree
measurement refers specifically to the amount of connections that a node has within
a cluster or network, and for all intensive purposes of this study will be simply
referred to as “centrality.” While Indegree measures connection, closeness measures
path distance, and thus, overall effectiveness of passing information. A node with the
highest closeness percentage means that the path distance between that node and
any other node in the network or cluster is shorter or more direct than another
other node has with the remainder of the group. The other measurement used in
this study, and that has to do with path distance, is betweeness. This measurement
is used to describe which node has the shortest paths and most direct relationships
with the rest of his direct connections. The final measurement used is based on a
compilation of path distance and indirect connections, and is referred to as hub. The
hub percentages show how likely and effectively this individual can reach the rest of
the cluster or network.
Limitations To the Study
This study suffers from a limited sample size, which may not be indicative of
the larger process of college recruiting. This selection bias, however, was
unavoidable due to a lack of public information on other schools’ recruiting classes
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and recruit contact information. Having a direct link to the University of Virginia
Football program, I was easily able to obtain my sample population, whereas I
would have needed to disassociate with UVA’s program, and jump through various
other hoops of consent to obtain this information from other NCAA institutions.
Further, as previously mentioned, this study is not a content analysis. While
subsequent studies have created a strong theoretical basis leading a researcher to
accurately predict, both, the type of UGC stimuli posted by student athletes, and the
power that this stimuli has over there peers, there are always outliers to the mold.
This historical threat to the internal validity of the study is essentially unavoidable
as I am working alone on this analysis, and there is not enough time to do both a
content and network analysis of the sample population. However, outliers to these
previously mentioned theories which assume content and persuasion would be an
abnormality that is another study in and of itself.
Results While the main goal of this study was to find the central nodes within the
2016 recruiting class, the Social Network Analysis conducted yielded much more
useful information about the sample size than just centrality. The information found
on the entire 2016 class yielded relatively low correlations, producing a network
with low network density, a high average path of separation, and an information
Hub with low access to the rest of the group. However, this information was
restricted due to the large number of generally unconnected nodes within the
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recruiting class. As you can see in FIGURE 1, there are 19 nodes that are completely
disconnected from the group.
Figure 1:
Further, there are four nodes having only one other connection within the class.
This separation from the main group largely decreases the statistical significance of
the findings. However, this data is not rendered useless. It is important to know
which nodes cannot be reached by this means of social media, and which nodes can.
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Therefore, UVA coaches know that the way to convince, node 18, for example, to
come to UVA will not be by means of Twitter.
When taking a further look at FIGURE 1 you can see that the recruiting class
is characterized by two main clusters which are connected only through a few
nodes. Therefore, these findings are further broken down into main body results
(which is the findings of the two main connected clusters), Cluster A findings, and
Cluster B findings. When limiting the research to eliminate the nodes that cannot be
reached, the statistical data increases significantly. Just by looking at FIGURE 2 you
can see that there are three nodes showing “flower-‐like” relationships, or oval-‐like
connections, to surrounding nodes. Figure 2:
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These flowers indicate mutual follows, and thus, a high degree of centrality.
While node 39, 12, and 53 all have the highest degree of centrality with numbers
ranging above 25% of the total network (See Chart 1), the connections between
nodes 15 and 12, and 53 and 29 represent the only connections which link cluster A
and B together. Therefore, node 53, or @Brandonw_7, would be the most crucial
node keeping the entire accessible network alive and effective. So, while 53 may be
limited as to who it can directly contact and influence, it has the most indirect
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relationships, and serves as the information hub connecting the two main body
clusters.
Further, separating the two clusters into their own individual entities will
help us to take a better look at the network’s components. Figure 3 represents
Cluster A’s social network visual.
Figure 3:
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Cluster A’s node 53 has a very high degree of centrality, but while node 53 is
more centralized, it is virtually equal with node 15 in its effectiveness to reach the
rest of cluster A (Closeness). In fact, node 15 actually has the more concise routes to
its direct connections (Betweeness).
Additionally, it is also important to note the connections between individual
nodes. For example, lets say a coach was only trying to recruit Node 3. This coach
could then use the SNA in order to find this node’s most direct connection (which in
this case is node 50). Deeper analysis of the two main groupings would also show
that Cluster A will have the most efficient passage of information throughout. This is
proven by its shortest average path distance of 1.6, while Cluster B and the Main
Body both have average path distances above 2.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
1 3 8 9 15 27 28 41 50 53
Percentage
Node
Cluster A
Hub
Betweenness
Closeness
InDegree
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Similarly, Cluster B analysis shows higher degrees of connections amongst
the recruits, with node 39 being the most crucial actor. Figure 4 represents Cluster
B’s analysis
Figure 4:
Not only is this node the most centralized, but it also holds leading percentages in all
other categories besides closeness, in which it marginally loses out to node 54. It is
also important to note that Cluster B contains node 39, which has the highest degree
centrality of the entire network of recruits. Therefore, while Cluster A may be more
efficient in reaching its components through the shortest average path distance,
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Cluster B contains a crucial node that has much farther reach than any node in
Cluster A. So while the numbers in Cluster B’s chart may be lower than those in its
predecessor’s, Cluster B still plays a very crucial role in the overall social network
analysis
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
2 4 7 12 13 14 24 25 26 29 31 33 36 37 39 40 42 43 46 47 54
Percentage
Node
Cluster B
Hub
Betweeness
Closeness
Indegree
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Conclusion Cluster B’s node 39, more commonly known to his followers as Tim Settle of
Manassas, Virginia, is statistically the most crucial node in the connected body of the
network. Not only does Tim have the highest degree centrality, but also he has the
greatest ability to pass information. But why? As a four-‐star recruit according to
Rivals.com, Tim is heavily recruited by many of the top schools in the nation. While
he may not have the most Twitter followers when compared with the rest of his
recruiting class, he has been on a considerably high number of official and unofficial
visits throughout his junior year of high school.
These visits put Tim in direct contact with other high school recruits, where
clearly they make a personal connection, which apparently later leads to a virtual
connection. Because Tim is such a high caliber recruit (as proven by his four-‐star
rating rather than the more plentiful two or three-‐star recruits within UVA’s 2016
class), he also carries an “awe” factor, which could also play a role in his high degree
of centrality. I believe that the most interesting part of Tim’s role as the most crucial
actor within the network, is that UVA has changed their recruiting tactics to focus
more largely on the Virginia Beach area rather than the Northern Virginia area
where Tim is from. Typically, it is obvious when people know each other through
localities and hometown identification. However, because Tim’s connections range
much farther than Northern Virginia, this means that Tim is, in fact, a bit of a star
within his own class, and is not obtaining these crucial connections simply due to
hometown linkages.
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All of this information and analysis is aimed that the efficient targeting of
specific athletes within a class. Therefore, if UVA wanted to obtain a specific recruit,
they could effectively offer a centralized node, such as Tim Settle, and put large
amounts of recruiting efforts into getting him to commit first, rather than the
multiple athletes they are actually trying to reach. Then, the coaches could rely on
Tim to be effectively pumping propaganda to other potential recruits through his
Twitter account. The result would be streamlined information to a wide audience,
and coaches spending less time actually executing traditional boots on the ground
recruiting tactics. So, instead of making phone calls and writing letters to make sure
a desired prospect is still thinking about a particular institution, the coaches and
recruiting coordinators can rely on that prospect’s peer to execute that job. Further,
this passage of information from a peer, as stated in the review of literature, will
also serve as a persuasive factor due to the position from which the information is
flowing.
While taking advantage of someone’s social network is a stealthy way to
incept prospective athletes to think about a particular institution, it may not always
yield positive results. Just as easily as this tactic may yield the passage of positive
information along social network wavelengths, the potential for negative UGC is
possible as well. If a coach targets a particular student athlete, and somewhere along
the way foils the relationship between the university and the prospect, then an
inverse effect will undoubtedly take place. The University of Virginia is a great
example of a school with high potential for these negative consequences. Because
UVA has managed to string together a fairly large amount of losing seasons, there
27
has been a high turnover of assistant coaches in the past 5 years. If an assistant
coach has forged a relationship with a particular central node, and is later relieved
of his duties due to lacking performance, then it could lead to the central node’s
public disapproval of administrative action. This disapproval would then, in fact,
warrant more work on behalf of the replacement staff in order to mend not only the
relationship with the central node, but also the relationships of the ones with whom
that node is connected.
That being said, proper analysis of the social connections may adequately
prevent the spread of negative information. If coaches know which recruit is the
central node, then they will know whose offer not to pull when National Signing Day
comes around and schools need to trim down the amount of verbal offers that have
already been extended. For example, if coaches know that the central node of a
recruiting class is a two-‐star athlete, then coaches may keep their scholarship offer
extended in order to limit the negative press, regardless of the fact that the recruit is
essentially interchangeable in the larger scheme. Without this knowledge, coaches
would more than likely pull that recruit’s offer, and potentially suffer the
consequences in terms of a social media berating.
While my sample size ended up to be relatively small and only applicable to
one school’s recruits, there is nothing stopping this from being a national practice.
This larger analysis would yield information about the entire nation’s recruits and
their connections, potentially leveling the recruiting playing field for mid-‐major and
lesser conference teams. Instead of sending letters to top recruits which would
undoubtedly never be opened, they could access top recruits in 140 characters or
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less via a trusted friend’s own medium. Not only could this ease access, but this
could also save some athletic departments across the country a lot of money in the
long run. Coaches will no longer need to make frequent trips to high schools around
the nation just to talk to a recruit for twenty minutes. Instead, the pitch will be done,
and coaches can make less frequent and therefore more meaningful trips to see
these prospects when it is closer to signing day and recruits need some last minute
persuasion.
This technique would not only be cost cutting for athletic departments, but
for general higher education as well. Administrators are constantly and actively
searching for ways to increase the national prestige of their particular university.
While schools often gain prestige as a result athletic achievements placing them in a
national media spotlight, not all sports teams can be successful, and some schools do
not reap the benefit from NCAA competition. With a social network analysis of a
potential admits to a college or university, administrators can better diversify their
student body over a period of time by quickly, cheaply, and effectively getting their
institution’s name out across the world. For example, Sweet Briar College is a small
school with very little national and international recognition. However, if Sweet
Briar decided to use a social network analysis to determine the reach and
relationships that potential students have via UGC, then Sweet Briar would
essentially be able to tailor their incoming class without having to settle on
admitting an under qualified student simply because of the region he or she lives.
Instead, Sweet Briar would be able to find a better, more qualified student with
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social ties to the area they are trying to reach, and see an influx of applicants from
that area in the coming years.
Though there are many great implications for recruiting both for athletics
and general higher education, there are some unwritten implications for
prospective students and student athletes across the nation. These prospects will be
under a much stronger microscope from higher education administrators, and the
implications could be great. Taking a closer look at high school kids’ Twitter
postings could raise a school or athletic department’s awareness of potential
character issues, which would not only make administrators cautious, but could
eventually cost the adolescent a chance at post-‐secondary education. Also, if the
prospects figure out what the universities are doing, then they are more likely to
either block their accounts from the public, or even go against their normal social
media tendencies.
Just as schools are limited in who can know about their recruitment
processes, this research is limited as well. My sample size came from only one
school, and the research took place only months after the University of Virginia
football team went two and ten in regular season matchups. Furthermore, and as
previously stated in multiple accounts, this study was by no means a content
analysis. There is a chance that Tim Settle was not saying anything good about UVA
regardless of his commitment, and regardless of the studies on narcissism which
would indicate otherwise. If this is the case, then the theories have failed and the
practice may be rendered useless. I believe, however, that these limitations leave
room for further study. Does college commitment yield propaganda-‐ridden tweets?
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Does social media centrality actually play a factor in persuasion? All of these
questions fall outside of the constraints of this study and should be further
investigated.
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