Designing and Evaluating Techniques to
Mitigate Misinformation Spread on
Micro-blogging Web Services"
Adi$
Gupta
Under
the
Supervision
of
Dr.
Ponnurangam
Kumaraguru
Indraprastha
Ins9tute
of
Informa9on
Technology,
Delhi
July
6,
2015

Power of Social Media"
2
300
hours
of
video
uploaded
every
minute
500
million
tweets
posted
every
day
1.44
Billion
monthly
ac$ve
users
60
million
photos
shared
everyday
*
2015
Sta9s9cs

Real World Events"
3

Misinformation on Social Media"
4

Misinformation on Social Media"
5

Misinformation on Social Media"
6

Focus: Twitter"
7
Profile
Photo
Hashtag
Followers
Retweet
BuOon
Username

Misinformation Tweets"
FAKE
RUMORS
8
$

Aim"
Designing
and
Evalua9ng
Techniques
to
Mi9gate
Misinforma9on
Spread
on
Micro-­‐blogging
Web
Services
9

Proposed Solution"
10
– Learning
to
Rank
model
for
assessing
credibility
of
Tweets
– Model
based
on
ground
truth
data
for
20
real
world
events
and
45
features
– System
evalua9on
using
year
long
real
world
experiment
– 1800+
users
requested
for
credibility
score
of
more
than
14.2
million
tweets.

TweetCred Demo"

Approach"
12
Characterizing
Misinforma$on
and
Fake
Content
Ranking
Framework
to
Assess
Credibility
Building
and
Evalua$ng
a
Real-­‐
$me
System
Detec9ng
fake
images
(Hurricane
sandy)
Analyzing
rumor
propaga9on
(Boston
blasts)
Detec9ng
user
communi9es
(three
events)
Analyzing
rumors
spread
in
India
centric
events
(Mumbai
blasts
and
Assam
riots)
14
events
data
tagging
30%
of
tweets
provide
informa9on
(17%
credible
informa9on
Linear
logis9c
regression
Present
ranking
algorithm
to
assess
credibility
in
tweets
using
pseudo
relevance
feedback
45
features
computable
for
a
single
tweet
Live
deployment:
1,800+
TwiOer
users
Credibility
score
computed
for
14+
Million
tweets
Evaluated
TweetCred
in
terms
of
response
9me,
effec9veness
and
usability

Data Collection"
– Created
a
24*7
data
collec9on
framework
- Streaming
/
REST
APIs
- JSON
Format
- MySql
Databases
– Collected
2+
Billion
tweets
from
2011-­‐14
13

Approach"
14
Characterizing
Misinforma$on
and
Fake
Content
Ranking
Framework
to
Assess
Credibility
Building
and
Evalua$ng
a
Real-­‐
$me
System
Detec9ng
fake
images
(Hurricane
sandy)
Analyzing
rumor
propaga9on
(Boston
blasts)
Detec9ng
user
communi9es
(three
events)
Analyzing
rumors
spread
in
India
centric
events
(Mumbai
blasts
and
Assam
riots)
14
events
data
tagging
30%
of
tweets
provide
informa9on
(17%
credible
informa9on
Linear
logis9c
regression
Present
ranking
algorithm
to
assess
credibility
in
tweets
using
pseudo
relevance
feedback
45
features
computable
for
a
single
tweet
Live
deployment:
1,800+
TwiOer
users
Credibility
score
computed
for
14+
Million
tweets
Evaluated
TweetCred
in
terms
of
response
9me,
effec9veness
and
usability

Background: Hurricane Sandy"
– Dates:
Oct
22-­‐
31,
2012
– Damages
worth
$75
billion
– Coast
of
NE
America
15
Faking
Sandy:
Characterizing
and
Iden9fying
Fake
Images
on
TwiOer
during
Hurricane
Sandy.
Adi9
Gupta,
Hemank
Lamba,
Ponnurangam
Kumaraguru
and
Anupam
Joshi.
Accepted
at
the
2nd
Interna9onal
Workshop
on
Privacy
and
Security
in
Online
Social
Media
(PSOSM),
in
conjunc9on
with
the
22th
Interna9onal
World
Wide
Web
Conference
(WWW),
Rio
De
Janeiro,
Brazil,
2013.
Best
Paper
Award.

Fake Image Tweets"
16

Data Description"
17
Total
tweets
1,782,526
Total
unique
users
1,174,266
Tweets
with
URLs
622,860
Tweets
with
fake
images
10,350
Users
with
fake
images
10,215
Tweets
with
real
images
5,767
Users
with
real
images
5,678

Network Analysis"
18
Tweet
–
Retweet
graph
for
the
propaga9on
of
fake
images
during
first
2
hours
Node
-­‐>
User
Id
Edge
-­‐>
Retweet

Role of Twitter Network"
– Analyzed
role
of
follower
network
in
fake
image
propaga9on
– Crawled
the
TwiOer
network
for
all
users
who
tweeted
the
fake
image
URLs
19
– Graph
1
- Nodes:
Users,
Edges:
Retweets
– Graph
2
- Nodes:
Users,
Edges:
Follow
rela9onships

Results"
20
Total
edges
in
retweet
network
10,508
Total
edges
in
follower-­‐followee
network
10,799,122
Common
edges
1,215
%age
Overlap
11%

Classification"
5
fold
cross
valida9on
21
Tweet
Features
[F2]
Length
of
Tweet
Number
of
Words
Contains
Ques9on
Mark?
Contains
Exclama9on
Mark?
Number
of
Ques9on
Marks
Number
of
Exclama9on
Marks
Contains
Happy
Emo9con
Contains
Sad
Emo9con
Contains
First
Order
Pronoun
Contains
Second
Order
Pronoun
Contains
Third
Order
Pronoun
Number
of
uppercase
characters
Number
of
nega9ve
sen9ment
words
Number
of
posi9ve
sen9ment
words
Number
of
men9ons
Number
of
hashtags
Number
of
URLs
Retweet
count
User
Features
[F1]
Number
of
Friends
Number
of
Followers
Follower-­‐Friend
Ra9o
Number
of
9mes
listed
User
has
a
URL
User
is
a
verified
user
Age
of
user
account

Classification Results"
22
F1
(user)
F2
(tweet)
F1+F2
Naïve
Bayes
56.32%
91.97%
91.52%
Decision
Tree
53.24%
97.65%
96.65%
• Best
results
were
obtained
from
Decision
Tree
classifier,
we
got
97%
accuracy
in
predic9ng
fake
images
from
real.
• Tweet
based
features
are
very
effec9ve
in
dis9nguishing
fake
images
tweets
from
real,
while
the
performance
of
user
based
features
was
very
poor.

Boston Blasts"
– Twin
blasts
occurred
during
the
Boston
Marathon
- April
15th,
2013
at
18:50
GMT
– 3
people
were
killed
and
264
were
injured
– First
Image
on
TwiOer
(within
4
mins)
23
$1.00
per
RT
#BostonMarathon
#PrayForBoston:
Analyzing
Fake
Content
on
TwiOer.
Adi9
Gupta,
Hemank
Lamba
and
Ponnurangam
Kumaraguru.
Accepted
at
IEEE
APWG
eCrime
Research
Summit
(eCRS),
San
Francisco,
USA,
2013.

Sample Fake Tweets"
24
>
50,000
RTs
>
30,000
RTs

Data Description"
Total tweets 7,888,374
Total users 3,677,531
Time of the blast Mon Apr 15 18:50 2013
Time of first tweet Mon Apr 15 18:53 2013
25

Geo-Located Tweets"
26

Identifying Rumor / True tweets"
– Tagged
most
viral
20
tweet
content
- Rumor
/
Fake
- True
- Generic
(NA)
– Six
Rumors
- 130,690
Tweets
/
Retweets
(29%)
- R.I.P.
to
the
8
year-­‐old
boy
who
died
in
Boston’s
explosions,
while
running
for
the
Sandy
Hook
kids.
#prayforboston
– Seven
True
news
- 116,454
Tweets
/
Retweets
(20%)
- Doctors:
bombs
contained
pellets,
shrapnel
and
nails
that
hit
vicGms
#BostonMarathon
@NBC6
– Seven
Generic
- 206,816
Tweets
/
Retweets
(51%)
- #PrayForBoston

Fake Content User Profiles"
Account
1
Account
2
Account
3
Account
4
No.
of
Followers
10
297
249
73,657
Profile
Crea$on
Date
Mar
24
2013
Apr
15
2013
Feb
07
2013
Dec
04
2008
Total
No.
of
Statuses
2
2
294
7,411
No.
of
Fake
Tweets
2
2
1
1
Current
Status
Suspended
Suspended
Suspended
Ac9ve
28
Username:
BostonMarathons

Temporal Patterns"
29
Fake
content
/
rumors
becomes
viral
in
first
7-­‐8
hours
just
aoer
the
event.

Tweet Source Analysis"
30
76%
16%
8%
Fake
64%
31%
5%
True
51%
41%
8%
General
Mobile
Web
Others

Spread of Fake Content"
– Using
linear
regression
– Predict
how
viral
a
rumor
would
get
- Based
on
aOributes
of
users
who
are
propaga9ng
the
rumor
– Based
on:
- Follower
- Friends
- Favorited
- Status
- Verified
31

Predicting Spread of Fake Content"
32
Results
show
it
is
possible
to
predict
how
viral
a
rumor
would
become
in
future
based
on
aOributes
of
users
currently
propaga9ng
the
rumor.

Book & Media"
33

Approach"
34
Characterizing
Misinforma$on
and
Fake
Content
Ranking
Framework
to
Assess
Credibility
Building
and
Evalua$ng
a
Real-­‐
$me
System
Detec9ng
fake
images
(Hurricane
sandy)
Analyzing
rumor
propaga9on
(Boston
blasts)
Detec9ng
user
communi9es
(three
events)
Analyzing
rumors
spread
in
India
centric
events
(Mumbai
blasts
and
Assam
riots)
14
events
data
tagging
30%
of
tweets
provide
informa9on
(17%
credible
informa9on
Linear
logis9c
regression
Present
ranking
algorithm
to
assess
credibility
in
tweets
using
pseudo
relevance
feedback
45
features
computable
for
a
single
tweet
Live
deployment:
1,800+
TwiOer
users
Credibility
score
computed
for
14+
Million
tweets
Evaluated
TweetCred
in
terms
of
response
9me,
effec9veness
and
usability
Credibility
Ranking
of
Tweets
during
High
Impact
Events.
Adi9
Gupta
and
Ponnurangam
Kumaraguru,
Workshop
on
Privacy
and
Security
on
Online
Social
Media
(PSOSM),
co-­‐located
with
the
21st
Interna9onal
World
Wide
Web
Conference
(WWW),
Lyon,
France,
2012.

Tweets about an Event"
35
Tweets
#event
Informa$on
No
informa$on
Tweets
with
informa$on
Credible
Informa$on
Non-­‐
Credible
Informa$on
Fake
news
/
Rumors
Personal
Opinions
/
Spam
No.
of
people
affected
Place
of
event
Pictures
/
videos

36

Architecture"
37

Data Statistics"
Events Tweets Trending Topics
UK Riots 542,685 #ukriots, #londonri- ots, #prayforlondon
Libya Crisis 389,506 libya, tripoli
Earthquake in Virginia 277,604 #earthquake, Earth- quake in SF
JanLokPal Bill Agitation 182,692 Anna Hazare, #jan- lokpal, #anna
Apple CEO Steve Jobs resigns 158,816 Steve Jobs, Tim Cook, Apple CEO
US Downgrading 148,047 S&P, AAA to AA
Hurricane Irene 90,237 Hurricane Irene, Tropical Storm Irene
Google acquires Motorola Mobility 68,527 Google, Motorola Mobility
News of the World Scandal 67,602 Rupert Murdoch, #murdoch
Abercrombie & Fitch stocks drop 54,763 Abercrombie & Fitch, A&F
Muppets Bert and Ernie were gay 52,401 Bert and Ernie
Indiana State Fair Tragedy 49,924 Indiana State Fair
Mumbai Blast, 2011 32,156 #mumbaiblast, Dadar, #needhelp
New Facebook Messenger 28,206 Facebook Messenger 38

Annotation"
– Step
1
- R1.
Contains
informa9on
about
the
event
- R2.
Is
related
to
the
event,
but
contains
no
informa9on
- R3.
Not
related
to
the
event
- R4.
Skip
tweet
– Step
2
- C1.
Definitely
credible
- C2.
Seems
credible
- C3.
Definitely
incredible
- C4.
Skip
tweet.
39

Annotation Results"
40
– Each
tweet
annotated
by
3
people
– Inter-­‐annotator
agreement
(Cronbach
Alpha)
=
0.748
– 30%
of
tweets
provide
informa9on
(17%
credible
informa9on)
and
14%
was
spam

Feature Sets"
41
Message based features
Length of the tweet
Number of words
Number of unique characters
Number of hashtags
Number of retweets
Number of swear language words
Number of positive sentiment words
Number of negative sentiment words
Tweet is a retweet
Number of special symbols [$, !]
Number of emoticons [:-), :-(]
Tweet is a reply
Number of @- mentions
Number of retweets
Time lapse since the query
Has URL
Number of URLs
Use of URL shortener service
Message based features
Length of the tweet
Number of words
Source based features
Registration age of the user
Number of statuses
Number of followers
Number of friends
Is a verified account
Length of description
Length of screen name
Has URL
Ratio of followers to followees
Source based features
Registration age of the user
Number of statuses
Number of followers

Evaluation Metric"
42
Evalua9on
Metric:
NDCG
(Normalized
Discounted
Cumula9ve
Gain)
NDCG
is
the
standard
metric
used
to
evaluate
“graded”
results

Ranking Results"
43
• Tweet
and
user
based
features
contribute
in
determining
the
credibility
–
it
maOers
“what
you
post
and
who
you
are”

PRF"
– PRF
(Pseudo
Relevance
Feedback)
- Extract
k
ranked
documents
and
then
re-­‐rank
those
documents
according
to
a
defined
score
- Re-­‐ranking
based
on
‘top
words’
of
an
event
- Top
n
unigrams
based
on
BM25
ranking
func9on
44

Algorithm"
45
SVM-­‐Rank
T1
.
.
.
.
Tn
T’1
.
.
T’k
.
T’n
Extract
top
unigrams
per
event
PRFRank
(similarity
metric)
T’’1
.
.
T’’k

Ranking Results"
46
PRF
ranking
greatly
enhances
the
performance
(upto
.74
NDCG)

Approach"
47
Characterizing
Misinforma$on
and
Fake
Content
Ranking
Framework
to
Assess
Credibility
Building
and
Evalua$ng
a
Real-­‐
$me
System
Detec9ng
fake
images
(Hurricane
sandy)
Analyzing
rumor
propaga9on
(Boston
blasts)
Detec9ng
user
communi9es
(three
events)
Analyzing
rumors
spread
in
India
centric
events
(Mumbai
blasts
and
Assam
riots)
14
events
data
tagging
30%
of
tweets
provide
informa9on
(17%
credible
informa9on
Linear
logis9c
regression
Present
ranking
algorithm
to
assess
credibility
in
tweets
using
pseudo
relevance
feedback
45
features
computable
for
a
single
tweet
Live
deployment:
1,800+
TwiOer
users
Credibility
score
computed
for
14+
Million
tweets
Evaluated
TweetCred
in
terms
of
response
9me,
effec9veness
and
usability
TweetCred:
Real-­‐Time
Credibility
Assessment
of
Content
on
TwiOer.
Adi9
Gupta,
Ponnurangam
Kumaraguru,
Carlos
Cas9llo
and
Patrick
Meier.
Proceedings
of
the
6th
Interna9onal
Conference
on
Social
Informa9cs
(SocInfo),
Barcelona,
Spain,
2014.
Honorable
Men$on
for
Best
Paper.

TweetCred"
– Available
as
a
Chrome
Extension
– Rest
API

Features for Real-time Analysis"
49
Feature
set
Features
(45)
Tweet
meta-­‐data
Number
of
seconds
since
the
tweet;
Source
of
tweet
(mobile
/
web/
etc);
Tweet
contains
geo-­‐coordinates
Tweet
content
(simple)
Number
of
characters;
Number
of
words;
Number
of
URLs;
Number
of
hashtags;
Number
of
unique
characters;
Presence
of
stock
symbol;
Presence
of
happy
smiley;
Presence
of
sad
smiley;
Tweet
contains
`via';
Presence
of
colon
symbol
Tweet
content
(linguis9c)
Presence
of
swear
words;
Presence
of
nega9ve
emo9on
words;
Presence
of
posi9ve
emo9on
words;
Presence
of
pronouns;
Men9on
of
self
words
in
tweet
(I;
my;
mine)
Tweet
author
Number
of
followers;
friends;
9me
since
the
user
if
on
TwiOer;
etc.
Tweet
network
Number
of
retweets;
Number
of
men9ons;
Tweet
is
a
reply;
Tweet
is
a
retweet
Tweet
links
WOT
score
for
the
URL;
Ra9o
of
likes
/
dislikes
for
a
YouTube
video

Training Data"
– 500
Tweets
per
event
– Used
CrowdFlower
service
50
Event
Tweets
Users
Boston
Marathon
Blasts
(2013)
7,888,374
3,677,531
Typhoon
Haiyan
/
Yolanda
(2013)
671,918
368,269
Cyclone
Phailin
(2013)
76,136
34,776
Washington
Navy
yard
shoo9ngs
(2013)
484,609
257,682
Polar
vortex
cold
wave
(2014)
143,959
116,141
Oklahoma
Tornadoes
(2013)
809,154
542,049
Total
10,074,150
4,996,448

Annotation"
– Step
1
- R1.
Contains
informa9on
about
the
event
- R2.
Is
related
to
the
event,
but
contains
no
informa9on
- R3.
Not
related
to
the
event
- R4.
Skip
tweet
45%
(class
R1),
40%
(class
R2),
and
15%
(class
R3)
– Step
2
- C1.
Definitely
credible
- C2.
Seems
credible
- C3.
Definitely
incredible
- C4.
Skip
tweet.
52%
(class
C1),
35%
(class
C2),
and
13%
(class
C3)
51

Ranking Model Evaluation"
52
AdaRank
Coord.
Ascent
RankBoost
SVM-­‐
rank
NDCG@25
0.6773
0.5358
0.6736
0.3951
NDCG@50
0.6861
0.5194
0.6825
0.4919
NDCG@75
0.6949
0.7521
0.689
0.6188
NDCG@100
0.6669
0.7607
0.6826
0.7219
Time
(training)
35-­‐40
secs
1
min
35-­‐40
secs
9-­‐10
secs
Time
(tes$ng)
<1
sec
<1
sec
<1
sec
<1
sec

Top Ten Features"
– No.
of
characters
in
tweet
– Unique
characters
in
tweet
– No.
of
words
in
tweet
– User
has
loca9on
in
profile
– Number
of
retweets
– Age
of
tweet
– Tweet
contains
URL
– Tweet
contains
via
– Statuses
/
Followers
– Friends
/
Followers
53

Implementation"

Feedback by Users"
55

Usage Statistics"
Date
of
launch
of
TweetCred
27
Apr,
2014
Credibility
score
requests
received
14,234,131
Unique
TwiOer
users
1,808
Feedback
was
given
for
tweets
1,654
Unique
users
who
gave
feedback
364
56
*
Data
as
on
April’15

Users of TweetCred"
Sample
users:
- Emergency
responders
- Firefighters
- Journalists
/
news
media
- General
users
- Researchers
(Requested
API
tokens)
57

System Evaluation"
– Usability
Evalua9on
- System
Usability
Scale
(SUS):
70
– Response
Time
58

v
Media"

Limitations & Future Work"
– Current
research
focuses
on
TwiOer,
we
would
like
analyze
credibility
of
content
on
different
social
media
using
similar
framework
– We
would
like
to
enhance
the
current
system
to
indicate
tweets
that
are
9mely,
factual,
well-­‐wriOen,
etc.
60

Contributions Summary"
– Analyzed
how
real
and
fake
content
is
propagated
through
the
TwiOer
network,
with
the
purpose
of
assessing
the
reliability
of
TwiOer
as
an
informa9on
source
during
real-­‐world
events.
– Proposed
a
learning-­‐to-­‐rank
framework
for
assessing
credibility
of
content
on
TwiOer
using
a
combina9on
of
content,
meta-­‐data,
network,
user
profile
and
temporal
features.
– Evaluated
and
deployed
a
novel
framework
for
providing
indica9on
of
trustworthiness
/
credibility
of
tweets
posted
during
events.
61

Real world Impact"
– The
real-­‐9me
system
TweetCred
built
to
assess
credibility
of
content
on
TwiOer
is
used
by
1,808
real
TwiOer
users
to
obtain
credibility
scores
for
more
than
14.2
million
tweets.
– A
unique
data
set
of
thousands
of
fake
images,
rumor
tweets
and
malicious
profiles
for
25+
real-­‐world
events.
62

Publications"
– Peer
Reviewed
Publica9ons
- TweetCred:
Real-­‐Time
Credibility
Assessment
of
Content
on
TwiOer.
Adi9
Gupta,
Ponnurangam
Kumaraguru,
Carlos
Cas9llo
and
Patrick
Meier.
Proceedings
of
the
6th
Interna9onal
Conference
on
Social
Informa9cs
(SocInfo),
Barcelona,
Spain,
2014.
Honorable
Men9on
for
Best
Paper.
- $1.00
per
RT
#BostonMarathon
#PrayForBoston:
Analyzing
Fake
Content
on
TwiOer.
Adi9
Gupta,
Hemank
Lamba
and
Ponnurangam
Kumaraguru.
Accepted
at
IEEE
APWG
eCrime
Research
Summit
(eCRS),
San
Francisco,
USA,
2013.
- Faking
Sandy:
Characterizing
and
Iden9fying
Fake
Images
on
TwiOer
during
Hurricane
Sandy.
Adi9
Gupta,
Hemank
Lamba,
Ponnurangam
Kumaraguru
and
Anupam
Joshi.
Accepted
at
the
2nd
Interna9onal
Workshop
on
Privacy
and
Security
in
Online
Social
Media
(PSOSM),
in
conjunc9on
with
the
22th
Interna9onal
World
Wide
Web
Conference
(WWW),
Rio
De
Janeiro,
Brazil,
2013.
Best
Paper
Award.
- Iden9fying
and
Characterizing
User
Communi9es
on
TwiOer
during
Crisis
Events.
Adi9
Gupta,
Anupam
Joshi
and
Ponnurangam
Kumaraguru.
Workshop
on
Data-­‐driven
User
Behavioral
Modeling
and
Mining
from
Social
Media
(UMSOCIAL),
Co-­‐located
with
21st
ACM
Interna9onal
Conference
on
Informa9on
and
Knowledge
Management
(CIKM),
Hawaii,
USA,
2012.
- Credibility
Ranking
of
Tweets
during
High
Impact
Events.
Adi9
Gupta
and
Ponnurangam
Kumaraguru,
Workshop
on
Privacy
and
Security
on
Online
Social
Media
(PSOSM),
co-­‐located
with
the
21st
Interna9onal
World
Wide
Web
Conference
(WWW),
Lyon,
France,
2012.
- Beware
of
What
You
Share:
Inferring
Home
Loca9on
in
Social
Networks.
Ta9ana
Pontes,
Gabriel
Magno,
Marisa
Vasconcelos,
Adi9
Gupta,
Jussara
Almeida,
Ponnurangam
Kumaraguru
and
Virgilio
Almeida,
Privacy
in
Social
Data
(PinSoda),
in
conjunc9on
with
Interna9onal
Conference
on
Data
Mining
(ICDM)
(2012).
63

Publications"
– Peer
Reviewed
Publica9ons
(Posters)
- Analyzing
and
Measuring
Spread
of
Fake
Content
on
TwiOer
during
High
Impact
Events.
Adi9
Gupta,
Hemank
Lamba,
Ponnurangam
Kumaraguru.
Security
and
Privacy
Symposium
IIT,
Kanpur,
2014.
Best
Poster
Winner.
- Twit-­‐Digest
Version
2:
An
Online
Solu9on
for
Analyzing
and
Visualizing
TwiOer
in
Real-­‐Time.
Adi9
Gupta,
Mayank
Gupta,
Ponnurangam
Kumaraguru.
Security
and
Privacy
Symposium
IIT,
Kanpur,
2014.
- Twit-­‐Digest:
Real-­‐9me
TwiOer
search
portal
for
extrac9ng,
tracking
and
visualizing
informa9on.
Adi9
Gupta,
Akshit
Chhabra
and
Ponnurangam
Kumaraguru.
IBM
ICARE
2012.
2nd
Runner’s
Up
prize
Best
Poster.
- U2P2:
Understanding
User
Privacy
Percep9ons,
Niharika
Sachdeva,
Ponnurangam
Kumaraguru
and
Adi9
Gupta,
Poster
at
IBM-­‐ICARE,
2011.
– Book
Chapter
- Misinforma9on
on
TwiOer
during
Crisis
Events.
Encyclopedia
of
Social
Network
Analysis
and
Mining
(ESNAM).
Adi9
Gupta,
Ponnurangam
Kumaraguru.
Book
Chapter.
Springer
publica9ons.
2012.
64

Thank
you!
hOp://twitdigest.iiitd.edu.in/TweetCred/
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