In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11%) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97% accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter.

1. Faking Sandy: Characterizing and
Identifying Fake Images on Twitter
during Hurricane Sandy
Adi$
Gupta
Hemank
Lamba
Ponnurangam
Kumaraguru
Anupam
Joshi

2. Agenda
 Background
 Mo$va$on
 Main
contribu$ons
 Data
descrip$on
 Methodology
 Results
 Future
work
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3. Background: Hurricane Sandy
 Dates:
Oct
22-­‐
31,
2012
 Category
3
storm
 Damages
worth
$75
billion
 Coast
of
NE
America
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4. Motivation
FAKE
IMAGES
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5. Motivation
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6. Our Contributions
 Performed
in-­‐depth
characteriza$on
of
tweets
sharing
fake
images
on
TwiVer
during
Hurricane
Sandy
- Tweets
containing
the
fake
images
URLs
were
mostly
retweets
(86%)
- Just
11%
overlap
between
the
retweet
and
follower
graphs
of
tweets
containing
fake
images.
 Applied
classifica$on
algorithms
to
dis$nguish
between
tweets
containing
fake
and
real
images.
- Best
accuracy
of
97%
was
achieved
using
decision
tree
classifier,
using
tweet
based
features.
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7. Methodology
Feature
Genera$on
Data
Collec$on
and
Filtering
Evalua$ng
Results
Data
Characteriza$on
Obtaining
Ground
Truth
Classifica$on
Module
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8. Data Description
Total
tweets
Total
unique
users
1,174,266
Tweets
with
URLs
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1,782,526
622,860
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9. Data Filtering
 Reputable
online
resource
to
filter
fake
and
real
images
- Guardian
collected
and
publically
distributed
a
list
of
fake
and
true
images
shared
during
Hurricane
Sandy
Tweets
with
fake
images
10,350
Users
with
fake
images
10,215
Tweets
with
real
images
5,767
Users
with
real
images
5,678
 One
of
the
biggest
fake
content
propaga$on
datasets
that
have
been
studied
by
researchers
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10. Characterization – Fake Image
Propagation
 86%
of
tweets
spreading
the
fake
images
were
retweets
 Top
30
users
out
of
10,215
users
(0.3%)
resulted
in
90%
of
the
retweets
of
fake
images
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11. Network Analysis
Tweet
–
Retweet
graph
for
the
spread
of
fake
images
at
‘nth’
and
‘n+1th‘
hour
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12. Role of Explicit Twitter Network
 Analyzing
role
of
follower
network
in
fake
image
propaga$on
 We
crawled
the
TwiVer
network
for
all
users
who
tweeted
the
fake
image
URLs
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13. Algorithm
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14. Results
Total
edges
in
the
retweet
network
Total
edges
in
the
follower-­‐followee
network
Total
edges
that
exist
in
both
retweet
network
and
the
follower-­‐
followee
network
%age
Overlap
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10,508
10,799,122
1,215
11%
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15. Classification
 5
fold
cross
valida$on
User
Features
[F1]
Number
of
Friends
Number
of
Followers
Follower-­‐Friend
Ra$o
Number
of
$mes
listed
User
has
a
URL
User
is
a
verified
user
Age
of
user
account
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Tweet
Features
[F2]
Length
of
Tweet
Number
of
Words
Contains
Ques$on
Mark?
Contains
Exclama$on
Mark?
Number
of
Ques$on
Marks
Number
of
Exclama$on
Marks
Contains
Happy
Emo$con
Contains
Sad
Emo$con
Contains
First
Order
Pronoun
Contains
Second
Order
Pronoun
Contains
Third
Order
Pronoun
Number
of
uppercase
characters
Number
of
nega$ve
sen$ment
words
Number
of
posi$ve
sen$ment
words
Number
of
men$ons
Number
of
hashtags
Number
of
URLs
Retweet
count
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16. Classification Results
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
predic$ng
fake
images
from
real.
• Tweet
based
features
are
very
effec$ve
in
dis$nguishing
fake
images
tweets
from
real,
while
the
performance
of
user
based
features
was
very
poor.
• Our
results
provided
a
proof
of
concept
that,
automated
techniques
can
be
used
in
iden$fying
real
images
from
fake
images
posted
on
TwiVer.
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17. Future Work
Fake
Charity
Rumor
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18. Some Attractions
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19. Some Attractions
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20. Q&A
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21. Thank You!!!
pk@iiitd.ac.in
adi$g@iiitd.ac.in

22. For any further information, please write to
pk@iiitd.ac.in
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