1. Examining Hurricane Irma with Twitter Data
and Machine Learning
Gbemisola Oladosu, Dylan Johnson,
(Mentors: Dr. Chien-fei Chen, Dr. Xiaojing Xu, Zach McMichael, Jullian Ball)

2. Outline Introduction Purpose Research Questions Methods
Data Collection
Text Cleaning
Descriptives
Bag-of-words
Doc2Vec
Doc2Vec Models
Results
Conclusion
Future Work

3. Introduction
Proportion of Category 4 and 5 hurricanes in the US has increased in the past two decades[1]
Cost of hurricane damage is increasing[2]
Larger responses are necessary
Higher risk for social issues
Measuring response effectiveness and social issues is important

4. Introduction (Cont.)
How did tweet content and purpose change over time?
What tweet content categories had the most number of complaints, messages of appreciation, or requests for help?
How did people feel about the government?

5. Methods 10,784 Irma-related tweets from September 2017
Tweets were labeled for content (Code 1) and purpose (Code2)
Removed:
Duplicates
Labeling mistakes

6. Methods (Cont.) Text was cleaned: Non-English Retweets Punctuation
Articles
URLs
Set to lowercase
Original Text:
Senior community finally gets power restored 18 days after Irma on.wtsp.com/2xOJoDN pic.twitter.com/Og1dyXLMEa
Cleaned Text:
senior community finally gets power restored days irma

7. Methods (Cont.) Oversampled to 10,000
Composition of Tweet Purpose (Code2)
Composition of Tweet Content (Code1)

8. Methods (Cont.) Neural networks require numeric inputs
Bag of Words feature vector
Represent relationships btw. words
Word similarity
Word order
(‘Large’ as far from
‘fat’ as ‘cat’)
Vocabulary
Vector
The 2
Fat 1
Cat
Sat On
Mat
Rat
Blue
Ran
Large
The fat cat sat on the mat.

9. Doc2Vec Uses word embeddings (many dimensional vectors) to represent:
Word similarity
Word order
Relationships between words [3]

10. Methods (Cont.)
Eight Doc2Vec models were created to predict Code 1 and Code 2
Dependent Variable
Text Type
Sampling
Model Accuracy
Code1
Original
Normal
17.5%
Cleaned
18.6%
Oversampled
38.1%
43.3%
Code2
11.1%
15.3%
20.6%
31.6%

11. Results (Cont.) Only complaints at govt.
Mostly regarding infrastructure and animals
Tweets of appreciation were related to health, infrastructure, and social issues/crimes
Requests for help related to animals
Code1 vs Code2

12. Results (Cont.) Tweets dramatically increased after landfall
Tweet composition remained constant
Code1 vs Time
Code1 vs Time

13. Results (Cont.)
Not very accurate
‘Relief efforts’ was predicted a lot before the hurricane made landfall
‘Recovery info’ and ‘relief efforts’ were predicted disproportionately

14. Conclusion
Predictions were inaccurate due to a small training set of fewer than 1400 tweets
Code 1 model’s accuracy of 43% and Code 2 model’s accuracy of 31% suggest more data could produce better results

15. Conclusion (Cont.)
Analyzing the results of a more accurate model can allow future researchers to determine the most impactful hurricane problems
This can inform hurricane response groups on how to address these problems

16. References Holland, G., & Bruyère, C. L. (2013). Recent
intense hurricane response to global climate change. Climate Dynamics, 42(3–4), 617–627.
2. Hurricane Costs. (2017). Retrieved July 23, 2019, from Noaa.gov website:
3. Le, Q., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents.
Retrieved from

17. Acknowledgements Special thanks to our mentors:
Dr. Chien-fei Chen, Dr. Xiaojing Xu, Zach McMichael, and Julian Ball

18. Acknowledgements
This work was supported primarily by the ERC Program of the National Science Foundation and DOE under NSF Award Number EEC and the CURENT Industry Partnership Program.
Other US government and industrial sponsors of CURENT research are also gratefully acknowledged.