=Paper=
{{Paper
|id=Vol-1183/gedm_paper07
|storemode=property
|title=Snag'em: Graph Data Mining for a Social Networking Game
|pdfUrl=https://ceur-ws.org/Vol-1183/gedm_paper07.pdf
|volume=Vol-1183
|dblpUrl=https://dblp.org/rec/conf/edm/CateteHBL14
}}
==Snag'em: Graph Data Mining for a Social Networking Game==
https://ceur-ws.org/Vol-1183/gedm_paper07.pdf
Snag’em: Graph Data Mining for a Social Networking Game
Veronica Cateté Drew Hicks
North Carolina State North Carolina State
University University
911 Oval Drive 911 Oval Drive
Raleigh, NC 27606 Raleigh, NC 27606
vmcatete@ncsu.edu aghicks3@ncsu.edu
Collin Lynch Tiffany Barnes
North Carolina State North Carolina State
University University
911 Oval Drive 911 Oval Drive
Raleigh, NC 27606 Raleigh, NC 27606
cflynch@ncsu.edu tmbarnes@ncsu.edu
ABSTRACT foundational networks and thus face difficulties making con-
New conference attendees often lack existing social networks nections. Based on Tinto’s Theory of University Departure,
and thus face difficulties in identifying relevant collaborators increased interaction with other students, faculty, staff and
or in making appropriate connections. As a consequence community supporters can increase the retention rate of mi-
they often feel disconnected from the research community nority populations and sense of community within secondary
and do not derive the desired benefits from the conferences and post-secondary academic communities [7].
that they attend. In this paper we discuss Snag’em, a social
network game designed to support new conference attendees In academia, sense of community has a strong positive cor-
in forming social connections and in developing an appropri- relation with retention [7]. Research indicates that students
ate research network. Snag’em has been used at seven pro- who do not feel as if they are part of a larger academic com-
fessional conferences and in four student settings and is the munity are less likely to participate in extracurricular activ-
subject of active research and development. The developers ities and organizations. This leads to lower retention rates,
have sought to make the system engaging and competitive especially amongst minority students who suffer without a
while preventing players from ‘gaming’ it and thus accru- strong student support group [7]. A feeling of community
ing points while neglecting to form real-world connections. can be nurtured with small group activities that augment
We briefly describe the system itself, discuss its impact on the individual’s role within a setting and helps students to
users, and describe our ongoing work on the identification foster connections [8].
of critical hub players and important social networks.
Snag’em was designed as a pervasive game to encourage
valuable professional networking and promote sense of com-
Keywords munity. The system’s pervasive features are designed to
Social Networks, Gamification, Conferences, Underrepresented help players translate their in-game networks directly into
Populations real world peer groups. The system was originally created
for the 2009 Students and Technology Academia Research &
1. INTRODUCTION Service (STARS) conference. This conference is unusual in
Social networking is an essential task at any academic con- that it is an academic conference designed specifically to en-
ference or professional venue. One of the primary goals of gage with minority and female undergraduates majoring in
attendees is to seek out relevant work, identify potential computing fields. Students who attend the conference par-
collaborators, and to maintain existing connections. Many ticipate in competitions and attend training sessions to sup-
of these contacts are made by building upon existing re- port engagement and research. Studies conducted at prior
lationships and by expanding the attendees existing social conferences has shown that while students were engaged in
network. New conference goers however, particularly stu- the training sessions and vigorously involved in learning they
dents and historically underrepresented groups, lack these did not develop the lasting social connections that can arise
out of conferences. Snag’em was designed to engage stu-
dents in social networking through gamification of the pro-
cess. Prior research has shown that social games can help
people to engage in otherwise challenging or uncomfortable
situations [6, 4, 2, 3].
Snag’em functions as a large human scavenger hunt. Play-
ers are assigned a set of relevant tags (e.g. “I’m a games
researcher”, or “I’m interested in data-mining”). They are
Figure 2: Here is an example of a message sent in
Figure 1: The browser interface for mission assign- game after a conversation between players.
ments. Snag Snapshots highlight missions recently
completed.
To date, Snag’em has been used at seven academic confer-
ences. It has also been deployed to help incoming freshman
then assigned a set of missions (e.g. “Find someone who spe- and transfer students connect at four academic institutions.
cializes in HCI”) which they must complete by identifying In 2009, for example, Snag’em was used by new students in
and engaging with an appropriate individual. The system the College of Computing and Informatics at the University
was developed in PHP with a MySQL backed and provides of North Carolina at Charlotte. Students were able to play
a web-based front end for players to edit their profile and to the game during the freshman orientation week with kiosks
record interactions. We have also developed a mobile ver- available for students to sign up located in the College of
sion of Snag’em which allows players to access the game via Computing and Informatics. SNAG’EM was used alongside
tablets and smartphones. The game itself is designed for other social activities to get students acquainted with each
easy deployment to new conferences and we are presently other, the faculty, and the CCI campus.
adding features that will allow us to automatically populate
the database with initial tags.
2. PRIOR ANALYSIS
Figure 1 shows a snapshot of the mission browser screen from We have studied the impact of Snag’em on users and found
the web version of Snag’em. Contact is registered when the that playing the game improved conference attendees’ sense
players enter a 4-digit ID from the other person. In addi- of community [6, 1]. We have also analyzed the existing
tion to missions the systems also allows players to record dataset both to test the implementation of the Snag’em fea-
notes about one-another for future reference (e.g. “I should tures, and to identify hubs or critical players whose activity
e-mail my proposal to him after the conference”) and to send predicts the behavior of others.
one-another messages. A sample message from the mobile
interface is shown in Figure 2. Snag’em can also be con- In analyzing the game mechanisms we have focused primar-
figured to suggest specific individuals that students should ily on the STARS 2009 dataset. As mentioned above STARS
make contact with based upon their mutual interests or so- is primarily targeted at undergraduate students specifically
cial connections. females and underrepresented minorities. We deployed the
system via the conference infrastructure and set up a table
The system logs all player interactions including tag up- near the registration booth. The game was active during
dates, missions completed, notes made, messages, sent, con- the first two full days of the conference. The conference
nections added, and so on. This provides a rich dataset of had 280 attendees 60.0% of whom were female (N=168) and
information that we can use to analyze social patterns at 70% of which (N=196) were students. Roughly 28% of the
conferences and to improve the impact of the intervention. conference-goers played the game (N=80) of whom 50% were
In addition to the raw logs the game contains a number of female. In previous analysis 35.0% of the players were clas-
features to support easy analysis. The developers have cre- sified as active. It is important to note that this data was
ated a set of badges that allowed administrators to easily collected on an earlier version of SNAG’EM where players
track the number of people playing via the mobile or web could snag each other only once, and only a single mission
interfaces as well as the number of missions completed. The was available at a time. Because completing missions was
badge system also provides a simple visual record of the significantly more difficult in this version of the game, play-
types of features (i.e. notes, tags, avatars) each player is us- ers were classified as active if they completed at least two
ing. The badge systems also allows administrators to note missions. An additional 50% of the players were classified
the frequency of use, time of day that players are online and as Interested, meaning they did more than just register for
so on. the game or that they completed one mission.
Figure 4: Correlation between active player hubs
Figure 3: Visualization of community center 4142, and number of interactions.
with one of that user’s maximal cliques highlighted.
likely to engage in the deep and meaningful conversations
Our analysis of this data was focused primarily on the mis- required or to form lasting connections.
sion and scoring systems. In 2009 the mission system was
relatively simple and focused solely on guiding students to In response to these results we have overhauled the scor-
locate a single individual with a desired tag. Players were ing system. This included changing the connectivity bonus
then guided to record the match via the ID system discussed to reward players based upon the size of the largest clique
above. Both the missions generated and points received were that they participate in. Players are now rewarded more
determined by the state of the current network. When gen- for expanding this clique, thus deepening their social net-
erating missions we attempted to ensure that they were of works, than they are for adding an unrelated individual to
varying difficulty, and were relevant to the current user. In their friends of friends. We have also allowed players to re-
this iteration of the system the missions could only be sat- snag the same individual for multiple missions with a low
isfied by identifying someone whom the user had not previ- penalty for re-snags, and have begun to reward players with
ously snagged. The target tags were selected from the full set points for allowing themselves to be snagged to help others
listed in the system. Easy missions were assigned high fre- complete a mission. We have not yet analyzed the effects of
quency tags (more than 12 of the non-adjacent users), while these changes on a the dataset.
medium missions were assigned tags that are present in 14 of
non-adjacent users and hard missions required tags present We have used two measures of importance when identifying
in less than 14 of the non-adjacent community. critical players. The first is the simple interaction frequency
as measured by the number of outgoing arcs from a player in
The difficulty of the mission determined the base score which the network. The second is membership in maximal cliques,
was then modified by a connectedness factor. This factor that is, cliques which are not part of a larger clique. Play-
was greater than 1 if adding this connection expanded your ers that participate in a large number of maximal cliques
“Friends of friends,” that is, the number of vertices less than are hubs. We were able to identify three distinct user com-
2 edges distant from the user. The connectedness factor was munities in the STARS 2009 dataset that centered on these
less than 1 if you completed the mission using the ID of a hubs. A sample community graph is shown in Figure 3. We
person you were already adjacent to, In this way we hoped also found that the activity of these hub players was highly
to encourage players to branch out. correlated with the activity of the other players in the com-
munity (r=0.827). A graph of these spikes is shown in Figure
When developing the system we had hoped that players 4. More specifically, on any day where one or more of the
would develop social networks that exhibited breadth (i.e. hub players were active, we observed spikes in the number
meeting lots of people), depth (i.e. getting to know some in- of interactions taking place across users. We were able to
dividuals well), and mutuality (i.e. snags in both directions). observe a similar effect (r = 0.659) on days when the devel-
We therefore hoped that users’ immediate neighborhoods opers had a booth/kiosk available.
would be large and relatively dense with multiple snags be-
tween some people and bidirectional connections. When an- We also performed an analysis of hub players using the
alyzing the STARS 2009 dataset, however, we found that UNCC Student Orientation dataset described above. In this
this was not the case. Rather the game mechanics encour- dataset 91 of the 1290 potential students registered to play
aged players to make a relatively large number of unrelated Snag’em of which 22% (N=20) were female [5]. This data
connections which, in turn, produced relatively broad and was collected on a version of Snag’em permitting multiple
shallow social neighborhoods with very few inbound arcs. In missions and allowing players to connect with the same user
fact some players actually opted to hide their IDs so that no multiple times.We classified players as active if they com-
other player could gain points by using them to complete a pleted 5 or more missions. In total, 9 users were active
mission. As a consequence the attendees were actually less users during this study. However, all of these players were
moderators or members of the development team. In this 4. ACKNOWLEDGMENTS
deployment almost all of the game interaction took place This research was supported by the NSF GRFP Fellowships
at the registration table thus making the administrators re- No. 0900860 & No. 1252376 and BPC Grant No. 0739216
sponsible for most of the activity. We had hypothesized and No. 1042468 Thanks to all developers who have worked
that the moderators would only need to initiate the game on the SNAG’EM project. The authors also wish to thank
and then it would be self-sustaining. As our analysis shows Shaghayegh Sahebi for her expert advice.
however, this was not the case. In general the players did
not think about the game outside of the advertised area. 5. REFERENCES
[1] S. L. Finkelstein, E. Powell, A. Hicks, K. Doran, S. R.
3. OPEN QUESTIONS & FUTURE WORK Charugulla, and T. Barnes. Snag: using social
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using graph methods. We plan to continue examining these computer science. In Proceedings of the fifteenth annual
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We also plan to expand our in-game evaluation of Snag’em
itself. We are presently adapting the system to poll play-
ers for their opinions as the system is used. This will bet-
ter help us to identify the immediate impact of the system
on users’ social connections. We will be deploying some of
these new features of the system during the 2014 Educa-
tional Datamining Conference in London as well as subse-
quent conferences in 2014 and 2014.