Twitter can be a rich source of information when one wants to monitor trends related to a given topic. We looked at how tweets can augment a public health program that studies emerging patterns of illicit drug use. We describe the architecture necessary to collect vast numbers of tweets over time based on a large number of search terms and the challenges that come with finding relevant information in the collected tweets. We then show several examples of early analysis we have done on this data, examining temporal and geospatial trends.

1. 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

2. What is the point of
this talk?
2

3. Can Twitter yield rapid insights
about trends in drug use?
3

4. Can Twitter yield rapid insights
about trends in drug use?
4

5. Can Twitter yield rapid insights
about trends in drug use?
5

6. Drug use
patterns are
changing in
the United
States
6

7. 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

8. Drug use
patterns are
changing in
the United
States
8
Designer
Drugs

9. Drug use
patterns are
changing in
the United
States
9
Designer
Drugs

10. 10

11. Current methods for tracking
these trends are slow
11

12. 12

13. 13

14. Current methods have no
support for partial results or
regression
14

15. 15

16. Researchers
have used
Twitter to track
a variety of
phenomena
16

17. 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.

18. 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

19. 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. 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. 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

22. Research Questions
22

23. Research Questions
• RQ 1 - Can Twitter provide insight in temporal
trends in drug use?
22

24. 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

25. Data Collection
23

26. Data Collection
24

27. Sample
Stream
Data Collection
24

28. Sample
Stream
Filter
Stream
Data Collection
24

29. Sample
Stream
Filter
Stream
Data Collection
25

30. Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
25

31. Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
26

32. Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
27

33. Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
Sampled from April-July 2014:
645,761,955 tweets (247GB)
27

34. 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

35. Sample
Stream
Filter
Stream
Data Collection
Distributed
Data Store
(HDFS)
Drug
Keywords
28
Hadoop
+
Spark

36. Case Study #1
Temporal Trends
29

37. 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

38. 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

39. 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

40. 32

41. 33

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. Case Study #2
Geographic Trends
40

49. Geographic Trends
41

50. Geographic Trends
Sample +
Filter
Stream
HDFS Store
of Drug
Tweets
Heroin
Cocaine
Meth
Oxy
Filter
for
USA
42

51. Geographic Trends
43

52. Geographic Trends
!
43

53. Geographic Trends
!
!
43

54. Geographic Trends
!
!
Only about 2% of tweets have
GPS data
44

55. Geographic Trends
Sample +
Filter
Stream
Drug Geocoded Tweet Count
Cocaine 281
Heroin 1,490
Meth 3,462
Oxy 223
45

56. Cocaine Heroin
Meth Oxy46
Geographic Trends
Sample +
Filter
Stream

57. 47
Geographic Trends
Sample +
Filter
Stream
Cocaine
Min Max

58. 48
Geographic Trends
Sample +
Filter
Stream
Cocaine
Min Max

59. Heroin
49
Geographic Trends
Sample +
Filter
Stream
Min Max

60. 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

61. Meth
51
Geographic Trends
Sample +
Filter
Stream
Min Max

62. 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%

63. Oxy
53
Geographic Trends
Sample +
Filter
Stream
Min Max

64. Oxy
54
Geographic Trends
Sample +
Filter
Stream
Min Max

65. Additional Research:
Tweet Disambiguation
55

66. Tweet Relevancy
56

67. Tweet Relevancy
!
56

68. Tweet Relevancy
57

69. Tweet Relevancy
!
57

70. Tweet Relevancy
!
!
57

71. 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

72. 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

73. 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

74. 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

75. Disambiguation with Topic
Models
Mallet Topic Modeling Package
60

76. Disambiguation with Topic
Models
Mallet Topic Modeling Package
Oxy Topics
61

77. 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

78. 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

79. 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

80. 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

81. 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

82. 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

83. 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

84. 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

85. Limitations and
Future Work
63

86. Relevancy Classification
64

87. Ground Truth

88. Ground Truth
• Limited concurrent ground
truth for comparison
• Tweets about news stories
may introduce bias

89. Ground Truth
• Limited concurrent ground
truth for comparison
• Tweets about news stories
may introduce bias
• New NSDUH should be
released in the fall

90. 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

91. Twitter
Data
Limited Location Data

92. Twitter
Data
Limited Location Data
Drug-Related
Tweets

93. Twitter
Data
Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Tweets
Drug-Related
Tweets

94. Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets

95. Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets
Infer User
Locations

96. Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets

97. Limited Location Data
Geocoded
Drug-Related
Tweets
Sampled
Drug-Related
Tweets

98. Conclusions
71

99. 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

100. 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

101. 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

102. 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

103. Cocaine Heroin
Meth Oxy76
Geographic Trends
Sample +
Filter
Stream

104. Cocaine Heroin
Meth Oxy77
Geographic Trends
Sample +
Filter
Stream
Can extract geographic trends
from Twitter

105. Cocaine Heroin
Meth Oxy78
Geographic Trends
Sample +
Filter
Stream
Can extract geographic trends
from Twitter
(for some drugs at least)

106. Thanks!
Questions?
79
Contact:
@codybuntain
cbuntain@cs.umd.edu