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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/	
  
cerc.iiitd.ac.in	
  

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Designing and Evaluating Techniques to
 Mitigate Misinformation Spread on 
Micro-blogging Web Services

  • 1. 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  
  • 2. Power of Social Media" 2   300  hours  of   video  uploaded   every  minute   500  million   tweets  posted   every  day   1.44  Billion   monthly  ac$ve   users   60  million   photos  shared   everyday   *  2015  Sta9s9cs    
  • 7. Focus: Twitter" 7   Profile   Photo   Hashtag   Followers   Retweet  BuOon   Username  
  • 9. Aim"      Designing  and  Evalua9ng  Techniques  to     Mi9gate  Misinforma9on  Spread  on     Micro-­‐blogging  Web  Services   9  
  • 10. Proposed Solution" 10   –  Learning  to  Rank  model  for  assessing  credibility  of  Tweets   –  Model  based  on  ground  truth  data  for  20  real  world  events   and  45  features     –  System  evalua9on  using  year  long  real  world  experiment   –  1800+  users  requested  for  credibility  score  of  more  than        14.2  million  tweets.    
  • 12. Approach" 12   Characterizing   Misinforma$on   and  Fake  Content     Ranking   Framework  to   Assess  Credibility   Building  and   Evalua$ng  a  Real-­‐ $me  System     Detec9ng  fake  images   (Hurricane  sandy)     Analyzing  rumor   propaga9on  (Boston   blasts)     Detec9ng  user   communi9es  (three   events)     Analyzing  rumors  spread   in  India  centric  events   (Mumbai  blasts  and   Assam  riots)   14  events  data  tagging     30%  of  tweets  provide   informa9on  (17%   credible  informa9on     Linear  logis9c  regression     Present  ranking   algorithm  to  assess   credibility  in  tweets   using  pseudo  relevance   feedback   45  features  computable   for  a  single  tweet     Live  deployment:  1,800+   TwiOer  users       Credibility  score   computed  for  14+   Million  tweets     Evaluated  TweetCred  in   terms  of  response  9me,   effec9veness  and   usability  
  • 13. Data Collection" – Created  a  24*7  data  collec9on  framework   - Streaming  /  REST  APIs   - JSON  Format   - MySql  Databases     – Collected  2+  Billion  tweets  from  2011-­‐14   13  
  • 14. Approach" 14   Characterizing   Misinforma$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  
  • 15. 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.  
  • 17. 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  
  • 18. Network Analysis" 18     Tweet  –  Retweet  graph  for  the  propaga9on  of  fake  images  during  first  2  hours   Node  -­‐>  User  Id   Edge  -­‐>  Retweet      
  • 19. 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  
  • 20. 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%  
  • 21. 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  
  • 22. 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.      
  • 23. 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   $1.00  per  RT  #BostonMarathon  #PrayForBoston:  Analyzing  Fake  Content  on  TwiOer.  Adi9  Gupta,  Hemank  Lamba  and   Ponnurangam  Kumaraguru.  Accepted  at  IEEE  APWG  eCrime  Research  Summit  (eCRS),  San  Francisco,  USA,  2013.  
  • 24. Sample Fake Tweets" 24   >  50,000  RTs   >  30,000  RTs  
  • 25. Data Description" Total tweets 7,888,374 Total users 3,677,531 Time of the blast Mon Apr 15 18:50 2013 Time of first tweet Mon Apr 15 18:53 2013 25  
  • 27. Identifying Rumor / True tweets" –  Tagged  most  viral  20  tweet  content   -  Rumor  /  Fake   -  True   -  Generic  (NA)     –  Six  Rumors   -  130,690  Tweets  /  Retweets  (29%)   -  R.I.P.  to  the  8  year-­‐old  boy  who  died  in  Boston’s  explosions,  while   running  for  the  Sandy  Hook  kids.  #prayforboston     –  Seven  True  news   -  116,454  Tweets  /  Retweets  (20%)   -  Doctors:  bombs  contained  pellets,  shrapnel  and  nails  that  hit  vicGms   #BostonMarathon  @NBC6     –  Seven  Generic   -  206,816  Tweets  /  Retweets  (51%)   -  #PrayForBoston    
  • 28. Fake Content User Profiles" Account  1   Account  2   Account  3   Account  4   No.  of  Followers   10   297   249   73,657   Profile  Crea$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  
  • 29. Temporal Patterns" 29   Fake  content  /  rumors  becomes  viral  in  first  7-­‐8  hours  just  aoer  the  event.      
  • 30. Tweet Source Analysis" 30   76%   16%   8%   Fake   64%   31%   5%   True   51%  41%   8%   General   Mobile   Web   Others  
  • 31. 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  
  • 32. 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.  
  • 34. Approach" 34   Characterizing   Misinforma$on   and  Fake  Content     Ranking   Framework  to   Assess  Credibility   Building  and   Evalua$ng  a  Real-­‐ $me  System     Detec9ng  fake  images   (Hurricane  sandy)     Analyzing  rumor   propaga9on  (Boston   blasts)     Detec9ng  user   communi9es  (three   events)     Analyzing  rumors  spread   in  India  centric  events   (Mumbai  blasts  and   Assam  riots)   14  events  data  tagging     30%  of  tweets  provide   informa9on  (17%   credible  informa9on     Linear  logis9c  regression     Present  ranking   algorithm  to  assess   credibility  in  tweets   using  pseudo  relevance   feedback   45  features  computable   for  a  single  tweet     Live  deployment:  1,800+   TwiOer  users       Credibility  score   computed  for  14+   Million  tweets     Evaluated  TweetCred  in   terms  of  response  9me,   effec9veness  and   usability   Credibility  Ranking  of  Tweets  during  High  Impact  Events.  Adi9  Gupta  and  Ponnurangam  Kumaraguru,  Workshop  on  Privacy   and  Security  on  Online  Social  Media  (PSOSM),  co-­‐located  with  the  21st  Interna9onal  World  Wide  Web  Conference  (WWW),   Lyon,  France,  2012.  
  • 35. Tweets about an Event" 35   Tweets   #event   Informa$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. 36  
  • 38. 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  
  • 39. 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  
  • 40. 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  
  • 41. 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 [$, !] Number of emoticons [:-), :-(] Tweet is a reply Number of @- mentions Number of retweets Time lapse since the query Has URL Number of URLs Use of URL shortener service Message based features Length of the tweet Number of words Source based features Registration age of the user Number of statuses Number of followers Number of friends Is a verified account Length of description Length of screen name Has URL Ratio of followers to followees Source based features Registration age of the user Number of statuses Number of followers
  • 42. Evaluation Metric" 42   Evalua9on  Metric:  NDCG  (Normalized  Discounted  Cumula9ve   Gain)           NDCG  is  the  standard  metric  used  to  evaluate  “graded”  results  
  • 43. Ranking Results" 43   •  Tweet  and  user  based  features  contribute  in  determining  the  credibility  –  it   maOers  “what  you  post  and  who  you  are”    
  • 44. PRF" – PRF  (Pseudo  Relevance  Feedback)     - Extract  k  ranked  documents  and  then  re-­‐rank   those  documents  according  to  a  defined  score     - Re-­‐ranking  based  on  ‘top  words’  of  an  event       - Top  n  unigrams  based  on  BM25  ranking  func9on   44  
  • 45. Algorithm" 45   SVM-­‐Rank   T1   .   .   .   .   Tn   T’1   .   .   T’k   .   T’n   Extract  top   unigrams  per   event   PRFRank  (similarity  metric)   T’’1   .   .   T’’k  
  • 46. Ranking Results" 46   PRF  ranking  greatly  enhances  the  performance  (upto  .74  NDCG)  
  • 47. Approach" 47   Characterizing   Misinforma$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.  
  • 48. TweetCred" – Available  as  a  Chrome  Extension   – Rest  API  
  • 49. 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  
  • 50. 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  
  • 51. 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  
  • 52. 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  
  • 53. 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  
  • 56. 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  
  • 57. Users of TweetCred" Sample  users:   - Emergency  responders   - Firefighters   - Journalists  /  news  media   - General  users   - Researchers  (Requested  API  tokens)   57  
  • 58. System Evaluation" – Usability  Evalua9on   - System  Usability  Scale  (SUS):  70   – Response  Time   58  
  • 60. 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  
  • 61. 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  
  • 62. 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  
  • 63. 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  
  • 64. 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  
  • 65. Thank  you!       hOp://twitdigest.iiitd.edu.in/TweetCred/   cerc.iiitd.ac.in