Twitter and Alcohol - BrightonSEO Pressentation
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Transcript of Twitter and Alcohol - BrightonSEO Pressentation
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I'm drunk ... Karaoke Drunk
#DONTTRYSPELLINGKSRAOKDR
UN
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Highwire CDT
Lancaster University
SCC
Lancaster University
Managment Science
Lancaster University
Monitoring Regional Alcohol ConsumptionThrough Social Media
Daniel Kershaw
Matthew Rowe
Patrick Stacey
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People Like toDrink
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UK Alcohol Consumption fromthe 1900'S
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Varying Rates of Harm
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Current Data CollectionMethonds
Quantity Frequency Questionnaires (QF)Time Line Method (TL)Time consumingExpensiveData is only a snapshot of the past
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Data Collection ErrorsSelective reportingRecall biasAccidental under-estimation by up to 40%
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People Like to Tweet
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Why Do People Use TwitterMinimal EffortMobile and pervasivePeople-based RSS feedsBroadcast Nature of TwitterKeeping in touch with friends and familyGathering information / Seeking help / Releasing emotionalstress
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Previous WorkMonitoring Flu Spreading Though Twitter - Culotta, A. (2010)Social Media to Track Depression on a Global Scale - DeChoudhury, M., Counts, S., & Horvitz, E. (2013)Stock Market Prediction Through Sentiment Analysis - Bollen,Mao, Zeng. (2011)Detecting Earthquakes Through Peoples Tweets - Sakaki, T.,Okazaki, M., & MATSUO, Y. (2010)
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Twitter as aSpatio-temporalSense Network
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ResearchQuestion
Is it possible to characterise and model UKalcohol consumption patterns of alcohol on
social media data such as Twitter, and if so isthere a variation across geographical location in
drinking patterns and terminology usage?
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Grounded TruthHealth and Social Care Infomation Center (HSCIC)Statistics on Alcohol ReportLooking for Daily Granularity
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Sunday Monday Tuesday Wednesday Thursday Friday Saturday
5
10
15
20
25
30
35
Day of the Week
% o
f res
pond
ent
Combined16 - 2425 - 4445 - 6465 +
plotly - data and graph »
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TwitterStreaming API
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Bounding Box
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31.6 million Tweets over 6 week period700,000 tweets/daily500 tweets/minute8 tweets/second40Gb of Data to process
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MethodSimple Average Keyword Signal Analysis
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KeywordsDrunk Wine BeerHangover Hungover WastedPissed
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@JeremyClarkson are you dead? pic.twitter.com/BsT7SlYAPvAdzy @iliffe25
@iliffe25 A bit pissed. But not dead11:52 PM - 10 Jun 2014
Jeremy Clarkson @JeremyClarkson
Follow
132 RETWEETS 210 FAVORITES
10 Jun
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My cat is sad because his mate got drunk at a strip club last night & is now vomiting in a quiet corner of the house. 7:30 AM - 12 Jun 2014
WHY MY CAT IS SAD @MYSADCAT
Follow
231 RETWEETS 361 FAVORITES
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Write drunk. Edit sober.— Shit Academics Say
(@AcademicsSay) June 18, 2014
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The Math(s)SMAI(T, M) = s(t,M)*t!T
|T|
s(t, M) = c(t,m)*m!Mtokens(t)<< <<
c(t, m) = f (w, m)*w!tokens(t)
f : W × M → {0, 1}
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Groupings
31.6 million tweets becomes 252.8 million data points - 320 Gbto process
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National → North West → LA → LA1
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Lets Look at theData
I'll Drink to That
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Week 1 Week 2 Week 3 Week 4 Week 5 Week 6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
Week of Study
Corr
olat
ion
National UKNorth WestYorkshire & HumbersideGreater LondonSouth WestSouth EastNorthern IrelandWest MidlandsChannel IslandsHome CountiesScotland (North)East EnglandScotland (South & Central)Wales (South)Wales (North)East MidlandsNorth EastAvrage
plotly - data and graph »
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52 54 56 58 60 62 64 66
500μ
550μ
600μ
650μ
700μ
Drank last week (% of poppulation)
Avra
ge S
MAI
plotly - data and graph »
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The ParablesNot to replace current methods, only too supplement themWord Sence DisambiguationTwitterology
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Future WorkOpen vocabulary methodLooking at conversations around alcoholSmoothing of results using demographic modeling
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To take homeThe ability to track underlying social trendsWe can detect the trend with high correlation to nationalstatisticsSimple to implement