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Workshop on Modeling
Social Media
Florence, Italy, 19 May 2015
1
This Is Your Twitter on Drugs.
Any Questions?
Cody Buntain and Jennifer Golbeck
{cbuntain,golbeck}@cs.umd.edu
Human-Computer Interaction Lab
University of Maryland
What is the point of
this talk?
2
Can Twitter yield rapid insights
about trends in drug use?
3
Can Twitter yield rapid insights
about trends in drug use?
4
Can Twitter yield rapid insights
about trends in drug use?
5
Drug use
patterns are
changing in
the United
States
6
Drug use
patterns are
changing in
the United
States
Source: National Survey on Drug Use and Health: Summary of National Findings, 2012
Past Month and Past Year Heroin Use Among
Persons Aged 12 or Older: 2002-2012
The New Face of Heroin
The explosion of drugs like OxyContin has given
way to a heroin epidemic ravaging the least
likely corners of America - like bucolic Vermont,
which has just woken up to a full-blown crisis
7
Drug use
patterns are
changing in
the United
States
8
Designer
Drugs
Drug use
patterns are
changing in
the United
States
9
Designer
Drugs
10
Current methods for tracking
these trends are slow
11
12
13
Current methods have no
support for partial results or
regression
14
15
Researchers
have used
Twitter to track
a variety of
phenomena
16
Researchers
have used
Twitter to track
a variety of
phenomena
17
Earthquake Epicenter Estimation
Typhoon Track Estimation
T. Sakaki, M. Okazaki, and Y. Matsuo, “Earthquake shakes Twitter users: real-
time event detection by social sensors,” in Proceedings of the 19th
international conference on World wide web, 2010, pp. 851–860.
Researchers
have used
Twitter to track
a variety of
phenomena
18
D. Hauger and M. Schedl, “Exploring geospatial music listening patterns in
microblog data,” Proc. 10th Int. Work. Adapt. Multimed. Retr. (AMR 2012), 2012.
Artist Popularity by Region
Artist’s Song Popularity Over Time
Researchers
have used
Twitter to track
a variety of
phenomena
19
V. Lampos, T. De Bie, and N. Cristianini, “Flu detector - Tracking epidemics on Twitter,” in
Machine Learning and Knowledge Discovery in Databases, J. Balcázar, F. Bonchi, A.
Gionis, and M. Sebag, Eds. Springer Berlin Heidelberg, 2010, pp. 599–602.
V. Lampos and N. Cristianini, “Tracking the flu pandemic by monitoring the social web,”
2010 2nd Int. Work. Cogn. Inf. Process. CIP2010, pp. 411–416, 2010.
Twitter-based Flu Scores versus UK’s HPA Data
20
*M. J. Paul, M. Dredze, J. P. Michael, and D. Mark, “You are what you Tweet: Analyzing
Twitter for public health,” Icwsm, pp. 265–272, 2011.
You Are What You Tweet:
Analyzing Twitter for Public Health*
Rates of Allergy Sufferers in 2010
21
*M. J. Paul, M. Dredze, J. P. Michael, and D. Mark, “You are what you Tweet: Analyzing
Twitter for public health,” Icwsm, pp. 265–272, 2011.
You Are What You Tweet:
Analyzing Twitter for Public Health*
Rates of Allergy Sufferers in 2010
Drug use should also affect
public health
Research Questions
22
Research Questions
• RQ 1 - Can Twitter provide insight in temporal
trends in drug use?
22
Research Questions
• RQ 2 - Can Twitter illustrate state-level
geographic trends in drug use?
• RQ 1 - Can Twitter provide insight in temporal
trends in drug use?
22
Data Collection
23
Data Collection
24
Sample
Stream
Data Collection
24
Sample
Stream
Filter
Stream
Data Collection
24
Sample
Stream
Filter
Stream
Data Collection
25
Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
25
Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
26
Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
27
Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
Sampled from April-July 2014:
645,761,955 tweets (247GB)
27
Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
Sampled from April-July 2014:
645,761,955 tweets (247GB)
Filtered from Oct. 30 - Nov. 26:
176,742,962 tweets (81GB)
27
Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
28
Hadoop
+
Spark
Case Study #1
Temporal Trends
29
TweetFrequency
0
150
300
450
600
Date
4/30/145/5/145/10/145/15/145/20/145/25/145/30/146/4/146/9/146/14/146/19/146/24/146/29/147/4/147/9/147/14/147/19/147/24/147/29/14
Cocaine
Heroin
Meth
Yaba
Sample
Stream
Temporal Trends
30
TweetFrequency
0
150
300
450
600
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine
Heroin
Meth
Yaba
Sample
StreamTemporal Trends
(Smoothed)
31
TweetFrequency
0
150
300
450
600
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine
Heroin
Meth
Yaba
Sample
StreamTemporal Trends
(Smoothed)
?
31
32
33
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Heroin
Meth
Yaba
Sample
StreamTemporal Trends
(Smoothed + No RT)
34
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Heroin
Meth
Yaba
Sample
StreamTemporal Trends
(Smoothed + No RT)
Popularity by Volume:
Cocaine
Meth
Heroin
Yaba
35
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Heroin
Meth
Yaba
Sample
StreamTemporal Trends
(Smoothed + No RT)
Popularity by Volume:
Cocaine
Meth
Heroin
Yaba
Consistent
with the
Global Drug
Survey
36
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Heroin
Meth
Yaba
Sample
StreamTemporal Trends
(Smoothed + No RT)
37
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Trend 1
Heroin
Trend 2
Meth
Trend 3
Yaba
Trend 4
Sample
StreamTemporal Trends
(Smoothed + No RT + Trendlines)
38
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Trend 1
Heroin
Trend 2
Meth
Trend 3
Yaba
Trend 4
Sample
StreamTemporal Trends
(Smoothed + No RT + Trendlines)
y = 0.5269x + 149.02
R² = 0.30184
39
Case Study #2
Geographic Trends
40
Geographic Trends
41
Geographic Trends
Sample +
Filter
Stream
HDFS Store
of Drug
Tweets
Heroin
Cocaine
Meth
Oxy
Filter
for
USA
42
Geographic Trends
43
Geographic Trends
!
43
Geographic Trends
!
!
43
Geographic Trends
!
!
Only about 2% of tweets have
GPS data
44
Geographic Trends
Sample +
Filter
Stream
Drug Geocoded Tweet Count
Cocaine 281
Heroin 1,490
Meth 3,462
Oxy 223
45
Cocaine Heroin
Meth Oxy46
Geographic Trends
Sample +
Filter
Stream
47
Geographic Trends
Sample +
Filter
Stream
Cocaine
Min Max
48
Geographic Trends
Sample +
Filter
Stream
Cocaine
Min Max
Heroin
49
Geographic Trends
Sample +
Filter
Stream
Min Max
Heroin
50
Geographic Trends
Sample +
Filter
Stream
Min Max
The New Face of Heroin
The explosion of drugs like OxyContin has given
way to a heroin epidemic ravaging the least
likely corners of America - like bucolic Vermont,
which has just woken up to a full-blown crisis
Meth
51
Geographic Trends
Sample +
Filter
Stream
Min Max
Meth
52
Geographic Trends
Sample +
Filter
Stream
Min Max
Region
12 or Older
Estimate
Northeast 3.07%
Midwest 3.12%
South 3.35%
West 3.81%
Oxy
53
Geographic Trends
Sample +
Filter
Stream
Min Max
Oxy
54
Geographic Trends
Sample +
Filter
Stream
Min Max
Additional Research:
Tweet Disambiguation
55
Tweet Relevancy
56
Tweet Relevancy
!
56
Tweet Relevancy
57
Tweet Relevancy
!
57
Tweet Relevancy
!
!
57
RelevantPercentage
0%
25%
50%
75%
100%
Drug-Related Keywords
synthetic
m
arijuana
hydrocodonelortab
m
ethylonebath
salts
thizz
oxies
dope
pot
tw
eak
hash
stacks
ox
k2
subbies
fluff
pink
lady
sm
iles
pandas
oranges
dem
on
speed
m
alcolm
x
negra
Tweet Relevancy
58
RelevantPercentage
0%
25%
50%
75%
100%
Drug-Related Keywords
synthetic
m
arijuana
hydrocodonelortab
m
ethylonebath
salts
thizz
oxies
dope
pot
tw
eak
hash
stacks
ox
k2
subbies
fluff
pink
lady
sm
iles
pandas
oranges
dem
on
speed
m
alcolm
x
negra
Tweet Relevancy
59
RelevantPercentage
0%
25%
50%
75%
100%
Drug-Related Keywords
synthetic
m
arijuana
hydrocodonelortab
m
ethylonebath
salts
thizz
oxies
dope
pot
tw
eak
hash
stacks
ox
k2
subbies
fluff
pink
lady
sm
iles
pandas
oranges
dem
on
speed
m
alcolm
x
negra
Tweet Relevancy
59
RelevantPercentage
0%
25%
50%
75%
100%
Drug-Related Keywords
synthetic
m
arijuana
hydrocodonelortab
m
ethylonebath
salts
thizz
oxies
dope
pot
tw
eak
hash
stacks
ox
k2
subbies
fluff
pink
lady
sm
iles
pandas
oranges
dem
on
speed
m
alcolm
x
negra
Tweet Relevancy
59
Disambiguation with Topic
Models
Mallet Topic Modeling Package
60
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Oxy Topics
61
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Oxy Topics
adderal, amp, bars, buy,
day, drugs, ecstasy, it's,
morphine, muscle, oxy,
oxycodone, percocets,
percs, prescription,
purplethizzle, relaxers,
speed, state, xanax
61
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Oxy Topics
amino, credits, feelin,
feeling, i'm, kinda, man,
morons, oxycontin, pain,
people, pills, real, reel,
strange, supply, system,
treatment, ubos
adderal, amp, bars, buy,
day, drugs, ecstasy, it's,
morphine, muscle, oxy,
oxycodone, percocets,
percs, prescription,
purplethizzle, relaxers,
speed, state, xanax
61
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Oxy Topics
gabapertina, gotas, health,
ice, lumina, mac, make,
months, oxy, oxygen,
oxymoron, prince, redman,
sweet, tah, tonight, trailer,
video, youtube
amino, credits, feelin,
feeling, i'm, kinda, man,
morons, oxycontin, pain,
people, pills, real, reel,
strange, supply, system,
treatment, ubos
adderal, amp, bars, buy,
day, drugs, ecstasy, it's,
morphine, muscle, oxy,
oxycodone, percocets,
percs, prescription,
purplethizzle, relaxers,
speed, state, xanax
61
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Molly Topics back, chest, china, coke, crack, don't, dont,
drug, flogging, gas, gotta, green, i'm, jay,
lean, literally
62
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Molly Topics back, chest, china, coke, crack, don't, dont,
drug, flogging, gas, gotta, green, i'm, jay,
lean, literally
balloon, birthday, body,
books, bookslapped, date,
enter, happy, harpercollins,
kiss, letting, love, molly,
mollysmcadams, nov, turn
weasley, win
ally, anthony, can't, club,
emilio, estevez, finally, find,
fresh, hall, kicks, michael,
molly, music, official,
ringwald, sheedy, video,
youtube
ain't, ass, bill, champagne,
cosby, drink, een, enjoyed,
ford, girl, gramps, home,
i'm, lewinsky, lol, love,
make, molly, monica, pop,
pop, popped, pudding,
rock, slip, that's, tom,
wanna, weed, whiskey, wit,
world
62
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Molly Topics back, chest, china, coke, crack, don't, dont,
drug, flogging, gas, gotta, green, i'm, jay,
lean, literally
balloon, birthday, body,
books, bookslapped, date,
enter, happy, harpercollins,
kiss, letting, love, molly,
mollysmcadams, nov, turn
weasley, win
ally, anthony, can't, club,
emilio, estevez, finally, find,
fresh, hall, kicks, michael,
molly, music, official,
ringwald, sheedy, video,
youtube
ain't, ass, bill, champagne,
cosby, drink, een, enjoyed,
ford, girl, gramps, home,
i'm, lewinsky, lol, love,
make, molly, monica, pop,
pop, popped, pudding,
rock, slip, that's, tom,
wanna, weed, whiskey, wit,
world
62
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Molly Topics back, chest, china, coke, crack, don't, dont,
drug, flogging, gas, gotta, green, i'm, jay,
lean, literally
balloon, birthday, body,
books, bookslapped, date,
enter, happy, harpercollins,
kiss, letting, love, molly,
mollysmcadams, nov, turn
weasley, win
ally, anthony, can't, club,
emilio, estevez, finally, find,
fresh, hall, kicks, michael,
molly, music, official,
ringwald, sheedy, video,
youtube
ain't, ass, bill, champagne,
cosby, drink, een, enjoyed,
ford, girl, gramps, home,
i'm, lewinsky, lol, love,
make, molly, monica, pop,
pop, popped, pudding,
rock, slip, that's, tom,
wanna, weed, whiskey, wit,
world
62
Disambiguation with Topic
Models
Mallet Topic Modeling Package
Molly Topics back, chest, china, coke, crack, don't, dont,
drug, flogging, gas, gotta, green, i'm, jay,
lean, literally
balloon, birthday, body,
books, bookslapped, date,
enter, happy, harpercollins,
kiss, letting, love, molly,
mollysmcadams, nov, turn
weasley, win
ally, anthony, can't, club,
emilio, estevez, finally, find,
fresh, hall, kicks, michael,
molly, music, official,
ringwald, sheedy, video,
youtube
ain't, ass, bill, champagne,
cosby, drink, een, enjoyed,
ford, girl, gramps, home,
i'm, lewinsky, lol, love,
make, molly, monica, pop,
pop, popped, pudding,
rock, slip, that's, tom,
wanna, weed, whiskey, wit,
world
62
Limitations and
Future Work
63
Relevancy Classification
64
Ground Truth
Ground Truth
• Limited concurrent ground
truth for comparison
• Tweets about news stories
may introduce bias
Ground Truth
• Limited concurrent ground
truth for comparison
• Tweets about news stories
may introduce bias
• New NSDUH should be
released in the fall
Ground Truth
• Limited concurrent ground
truth for comparison
• Tweets about news stories
may introduce bias
• New NSDUH should be
released in the fall
• Comparison with Google
search trends
Twitter
Data
Limited Location Data
Twitter
Data
Limited Location Data
Drug-Related
Tweets
Twitter
Data
Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Tweets
Drug-Related
Tweets
Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets
Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets
Infer User
Locations
Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets
Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets
Conclusions
71
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Heroin
Meth
Yaba
Sample
Stream
Temporal Trends
72
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Heroin
Meth
Yaba
Sample
Stream
Temporal Trends
73
Popularity by Volume:
Cocaine
Meth
Heroin
Yaba
Consistent
with the
Global Drug
Survey
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Trend 1
Heroin
Trend 2
Meth
Trend 3
Yaba
Trend 4
Sample
Stream
Temporal Trendlines
y = 0.5269x + 149.02
R² = 0.30184
74
TweetFrequency
0
75
150
225
300
Date
5/3/145/8/145/13/145/18/145/23/145/28/146/2/146/7/146/12/146/17/146/22/146/27/147/2/147/7/147/12/147/17/147/22/147/27/14
Cocaine†
Trend 1
Heroin
Trend 2
Meth
Trend 3
Yaba
Trend 4
Sample
Stream
Temporal Trendlines
y = 0.5269x + 149.02
R² = 0.30184
75
Cocaine increased in
popularity on Twitter
Cocaine Heroin
Meth Oxy76
Geographic Trends
Sample +
Filter
Stream
Cocaine Heroin
Meth Oxy77
Geographic Trends
Sample +
Filter
Stream
Can extract geographic trends
from Twitter
Cocaine Heroin
Meth Oxy78
Geographic Trends
Sample +
Filter
Stream
Can extract geographic trends
from Twitter
(for some drugs at least)
Thanks!
Questions?
79
Contact:
@codybuntain
cbuntain@cs.umd.edu

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