towards detecting influenza epidemics by analyzing twitter massages
DESCRIPTION
Towards Detecting Influenza Epidemics by Analyzing Twitter Massages. Aron Culotta. Jedsada Chartree. Introduction. Growing interest in monitoring disease outbreaks. Growing of twitter users - February, 201050 million tweets/day - June, 201065 million tweets/day (750 tweets/ s - PowerPoint PPT PresentationTRANSCRIPT
Towards Detecting Influenza Epidemics by Analyzing Twitter Massages
Aron Culotta
Jedsada Chartree
Introduction• Growing interest in monitoring disease outbreaks.• Growing of twitter users
- February, 2010 50 million tweets/day- June, 2010 65 million tweets/day (750 tweets/s
- 190 million users
Source: http://en.wikipedia.org/wiki/Twitter
Introduction
• Twitter is a website, which offers a social networking and micro-blogging service.- Users send and read messages called “tweets”
(140 characters)
Introduction• Advantages of Twitter for this research
- Full messages provide more information than query.- Twitter profiles contain more detail to analyze.
(city, state, gender, age)- Diversity of twitter users.
Methodology• Data
- Collect 574,643 messages for 10 weeks (February 12, 2010 to April 24, 2010) - The US Centers for Disease Control and Prevention (CDC)
publishes the US Outpatient Influenza-like Illness Surveillance Network (ILINet)
Methodology
The Ground truth ILI rates obtained from the CDC statistics
Methodology• Regression Models 1. Simple linear regression
P = the proportion of the population exhibiting ILI symptoms = the coefficients = Error = the fraction of document in D that match W = D = a document collection Dw = a document frequency for word W logit(x) =
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log it(P) = β1 log it(Q(W ,D))+ β 2 +ε
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β1
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β2€
ε
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Q(W ,D)
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DwD
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ln( x1− x
)
Methodology• Regression Models 2. Multiple linear regression
P = the proportion of the population exhibiting ILI symptoms = the coefficients = Error = the fraction of document in D that match Wi = D = a document collection Dwi = a document frequency for word Wi
logit(x) =
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log it(P) = β1 log it(Q({W1},D))+ ...+ log it(Q({Wk},D))+ β k+1 +ε
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β1
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β2€
ε
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Q({Wi },D)
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DwiD
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ln( x1− x
)
Methodology• Keyword Selection1. Correlation Coefficient
- Simple linear regression model evaluation
2. Residual Sum of Squares (RSS)
- It measures a discrepancy between the data and an estimation model
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RSS(P,^P) = ( pi − p
^)2
i∑
Methodology• Keyword Generation1. Hand-chosen keywords
(flu, cough, sore throat, headache)
2. Most frequent keywords - Search all documents containing any of hand-chosen
keywords. - Find the top 5,000 most frequently occurring words.
Methodology• Document Filtering - Applying logistic regression to predict whether a Twitter
message is reporting an ILI symptom.
yi = a binary random variable (1 if document Di is positive, 0 otherwise) xi = {xij} = number of times word j appears in document i
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p(y i = 1 | x i ;θ ) =1
1+ e(−xi •θ )
Methodology
Methodology• Classification evaluation
- Accuracy - Precision - Recall - F-measure
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F = 2• Pr ecision • RecallPr ecision +Recall
Results
• Document Filtering
Evaluation of messages classification with standard error in parentheses
Results
• Regression
The 10 different systems evaluated
Results
• Regression
The regression coefficient (r), residual sum of square (RSS), and standard error of each system
Results
Results for multi-hand-rss(2) Results for classification-hand
Results
Results for multi-freq-rss(3) Results for simple-hand-rss(1)
Results
Correlation results for simple –hand-rss and multi-hand-rss
Correlation results for simple –hand-corr and multi-hand-corr
Results
Correlation results for simple –freq-rss and multi-freq-rss
Correlation results for simple –freq-corr and multi-freq-corr
Conclusion• Several methods to identify influenza-related messages.• Compare a number of regression models to correlate the
messages with CDC statistics.• The best model achieves correlation of .78 .