twitter and alcohol - brightonseo pressentation

I'm drunk ... Karaoke Drunk

#DONTTRYSPELLINGKSRAOKDR

UN

Highwire CDT

Lancaster University

SCC

Lancaster University

Managment Science

Lancaster University

Monitoring Regional Alcohol ConsumptionThrough Social Media

Daniel Kershaw

Matthew Rowe

Patrick Stacey

People Like toDrink

UK Alcohol Consumption fromthe 1900'S

Varying Rates of Harm

Current Data CollectionMethonds

Quantity Frequency Questionnaires (QF)Time Line Method (TL)Time consumingExpensiveData is only a snapshot of the past

Data Collection ErrorsSelective reportingRecall biasAccidental under-estimation by up to 40%

People Like to Tweet

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

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)

Twitter as aSpatio-temporalSense Network

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?

Grounded TruthHealth and Social Care Infomation Center (HSCIC)Statistics on Alcohol ReportLooking for Daily Granularity

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 »

TwitterStreaming API

Bounding Box

31.6 million Tweets over 6 week period700,000 tweets/daily500 tweets/minute8 tweets/second40Gb of Data to process

MethodSimple Average Keyword Signal Analysis

KeywordsDrunk Wine BeerHangover Hungover WastedPissed

@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

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

Write drunk. Edit sober.— Shit Academics Say

(@AcademicsSay) June 18, 2014

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}

Groupings

31.6 million tweets becomes 252.8 million data points - 320 Gbto process

National → North West → LA → LA1

Lets Look at theData

I'll Drink to That

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 »

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 »

The ParablesNot to replace current methods, only too supplement themWord Sence DisambiguationTwitterology

Future WorkOpen vocabulary methodLooking at conversations around alcoholSmoothing of results using demographic modeling

To take homeThe ability to track underlying social trendsWe can detect the trend with high correlation to nationalstatisticsSimple to implement

Thank You,@danjamker

d.kershaw1@lancaster.ac.uk

smai.danjamker.co.uk/presentation