Twitter is a prominent online social media which is used to share information and opinions. Previous research has shown that current real world news topics and events dominate the discussions on Twitter. In this paper, we present a preliminary study to identify and characterize communities from a set of users who post messages on Twitter during crisis events. We present our work in progress by analyzing three major crisis events of 2011 as case studies (Hurricane Irene, Riots in England, and Earthquake in Virginia). Hurricane Irene alone, caused a damage of about 7-10 billion USD and claimed 56 lives. The aim of this paper is to identify the different user communities, and characterize them by the top central users. First, we defined a similarity metric between users based on their links, content posted and meta-data. Second, we applied spectral clustering to obtain communities of users formed during three different cri- sis events. Third, we evaluated the mechanism to identify top central users using degree centrality; we showed that the top users represent the topics and opinions of all the users in the community with 81% accuracy on an average. The top central people identified represent what the entire community shares. Therefore to understand a community, we need to monitor and analyze only these top users rather than all the users in a community.

1. Identifying and Characterizing
User Communities on Twitter
during Crisis Events
Adi$
Gupta,
Anupam
Joshi,
Ponnurangam
Kumaraguru
Oct.
29,
2012
UMSOCIAL
@
CIKM’12

2. Outline
 Problem
Statement
 Introduc$on
 Data
 Methodology
 Results
 Future
Work
 References
2

3. Problem Statement
 To
iden$fy
and
characterize
user
communi$es
on
TwiRer
during
crisis
events,
using
semi-­‐automated
techniques.
3

4. Data: Three 2011 Crisis Events
Event:
Hurricane
Irene
Tweets
collected:
90,237
Users:
55,718
Event:
Earthquake
in
VA
Tweets
collected:
277,604
Users:
219,621
Event:
England
Riots
Tweets
collected:
1,165,628
Users:
546,966
4

5. Our Contributions
 Defined
a
novel
similarity
metric
to
compute
similari$es
between
users
based
on
their
links,
content
posted
and
meta
data.
 Applied
spectral
clustering
to
obtain
communi$es
of
users
formed
during
three
different
crisis
events.
 Showed
most
central
people
(by
degree
centrality)
represent
what
users
in
the
cluster
are
talking
about
with
81%
accuracy
on
average.
5

6. Methodology
 STEP
1:
Computer
User*User
Similarity
Matrix
 STEP
2:
Use
spectral
clustering
to
obtain
communi$es
of
users
 STEP
3:
Use
degree
centrality
to
compute
top
users
in
each
cluster
6

7. Methodology
 user*user
similarity
matrix,
U[i,j]
-
Content
Similarity,
C[i,j]
 Computed
based
on
common
words,
hashtags
and
URLs
in
tweets
- Link
Similarity,
L[i,j]
 Based
on
users
retweet,
men$on
or
reply
to
each
other
tweets
or
a
common
third
person’s
tweet
- Meta-­‐data
Similarity,
M[i,j]
 Similarity
between
two
users
based
on
their
loca$on
(country,
state
or
city)
 U[i,j]
=
C[i,j]
+
L[i,j]
+
M[i,j],
for
each
user
i
and
j
7

8. Top Users Identification
 Use
degree
centrality
- Social
Network
Analysis
metric
- Number
of
links
incident
upon
a
node
(i.e.,
the
number
of
$es
that
a
node
has)
 In
each
cluster
obtained
- We
compute
the
top
30
users
using
degree
centrality
8

9. Evaluation Metrics
 Modularity
- How
good
is
the
division
of
the
network
in
communi$es
 Mean
average
precision
- With
how
much
precision
can
we
say
that
the
influencers
in
the
cluster
represent
the
en$re
cluster
9

10. Results: Modularity score
 The
modularity
score
greater
than
0.5
for
all
events
- Hurricane
Irene
=
0.67
- England
Riots
=
0.51
- Virginia
Earthquake
=
0.53
 We
conclude
that
the
division
of
graph
into
communi$es
is
of
good
quality
10

11. Results: MAP Performance
• Top
users
based
on
degree
centrality
represent
the
opinions
and
topic
of
the
en$re
cluster
during
crisis
events.
• In
case,
of
a
random
TwiRer
sample,
the
above
is
not
true.
11

12. Results: Characterization
Events
Community 1
Community 2
Community 3
Hurricane
Irene
hurricane, irene, http, jada, song,
brenda, butistillloveu, photos,
icanhonestlysay, check,
neverapologizefor, video, scandal,
angelina, jolie, storm, nick, college,
bahamas, puerto
irene, hurricane, http, coast, category, bahamas, east, puerto,
storm, rico, winds, advisory,
issued, fore- cast, heads, track,
path, update, moving, number
hurricane, irene, http, puerto, coast,
storm, rico, news, track, winds, bahamas, category, east, forecast, advisory, florida, update, issued, tropical, latest
England
Riots
londonriots, http, ukriots, police,
london, riots, people, hackney, riot
ers, news, fire, dont, riot, cameron,
riotcleanup, birmingham, looting,
croydon, night, stop
londonriots, http, ukriots, police,
london, riots, people, rioters,
cameron, hackney, news, fire,
dont, riot, looting, prayforlondon,
birm- ingham, manchesterriots,
stop, riot- cleanup
londonriots, http, ukriots, police, riots, london, people, rioters, hackney, cameron, riot, birmingham,
news, fire, riotcleanup, dont, looting,
make, manchesterriots, stop, rioting
Virginia
Earthquake
earthquake, http, today, quake,
east, coast, earth, richmond,
washington, august, virginia, news,
campbell, glen, monument, heard,
norwegian, alaska, new york, video
earthquake, http, east, coast, virginia, washington, felt, magnitude,
news, monument, feel, damage,
today, nuclear, quake, epicenter,
people, school, video
earthquake, http, coast, east,
washington, felt, virginia, news,
damage, today, magnitude,
monument, feel, quake, people,
video, area, breaking, shit, nuclear
Hurricane
Irene
• Community
1:
Contains
spam
words
–
spammers
dominate
this
cluster
• Community
2
and
3
contain
similar
words
• Analyze
the
corresponding
top
central
users
(next
slide)
12

13. Results: Characterization
C2:Username
C2:Description
C3:Username
C3:Description
top_tw_science
none
YaekoAvilez3837
none
top_trend_world
none
MarxInbody5668
none
Neednewsnow
none
KerrieRamagano3
none
iBreakings
none
AureliaHickman1
none
metofficestorm
Official Met Office page for latest information on tropical storms,
hurricanes, typhoons and cyclones
LenitaBross5906
none
RES911CU
RES911CU Bringing you the latest news/weather from around the world &
country using over 1000 news sources
BeckieSteakley4
none
qianafgtt
none
KirbyPietig6221
none
Georgeannla47
none
GailSterner5774
none
moneyworlds
none
LeroyRasheed
none
LinJosh2011
none
ClorindaKan1926
none
top_trend_uk
none
StaceeRosebure2
none
ShareWith911
Emergency Information Sharing Network connecting 911,First Responders
and Citizens before, during and after emergencies of all shapes and sizes.
VaniaAsmus
none
Hurricane
Irene
• Community
2:
formed
of
official
news
media,
emergency
and
alert
profiles
(ShareWith911,
metofficestorm,
RES911CU,
Neednewsnow)
• Community
3:
formed
with
common
or
general
users
of
TwiRer
(KerrieRamagano3,
YaekoAvilez3837,
AureliaHickman1)
13

14. Conclusions
 The
results
obtained
with
the
case
study
of
three
events
show
the
poten$al
of
applying
spectral
clustering
and
centrality
measures,
to
iden$fy
community
of
users
and
the
prominent
top
users
in
each
community,
during
crisis
events.
14

15. Future Work
 Construct
weighted
similarity
metrics
to
compute
similarity
between
users
 Include
behavioral
features
of
users
in
similarity
metric
construc$on
 Conduct
a
more
generic
experiment
with
more
crisis
events
15

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16

17. Thank you
Contact:
adi$g@iiitd.ac.in
joshi@cs.umbc.edu
[ebiquity.umbc.edu]
pk@iiitd.ac.in
[precog.iiitd.edu.in]
17

18. For any further information, please write to
pk@iiitd.ac.in
precog.iiitd.edu.in
18