[DISC2013] Mood and Weather: Feeling the Heat?

Mood and Weather: Feeling the Heat?

Kunwoo Park1, Seonggyu Lee1, Eunae Kim1, Minjee Park2, Juyong Park3, Meeyoung Cha3

Division of Web Science and Technology1, Department of Computer Science2, Graduate School of Culture Technology3

Korea Advanced Institute of Science and Technology (KAIST)

2013. 12. 12. DISC 2013 1

Who are we?

2013. 12. 12. Mood and Weather: Feeling the Heat? 2

• Korea Advanced Institute of Science and Technology• Located in Daejeon (not Daegu)• Natural Science and Engineering oriented

• MIT in Korea

We are here

Who are we?


• Division of Web Science and Technology• Understanding and Developing the Web

• human-centered web exploration

• Graduate School of Culture Technology• Social Computing Lab

• Understanding online social networks and social media from diverse perspectives

Background


THIS… is American Idol!!!!

A mixed of raining and sunshine.

Just replaced the wheels on my

2nd car :-)

Back to work!Holla at ya’ll

later!

Research Potentials

• We can observe people’s actions and words on a significant scale [Lazer et al. Science 2009]

• It can function as one giant laboratory

• Potentials of social media• reading people’s mind and thinking

ex> predicting election [Tumasjan et al. ICWSM 2010]

• understanding how social mechanism works

ex> how social convention emerges [Kooti et al. ICWSM 2013]


2013. 12. 12. 6Mood and Weather: Feeling the Heat?

Theory on Social Science

Data-DrivenComputational Social Science

Big data generated by human

Understanding human

behaviors and social networks

How to get data??

• Most of services provide ways to get data• Can use enormous size of data by writing a programming code

• Help computer scientist start to work on computational social science


Previous works: weather and mood

• Correlation of weather and mood [Howarth and Hoffman 1984; Preti 1998]

• There are individual differences in how weather affects mood [Klimsta et al. 2011]

• Some of us cannot stand rain

• When we feel happy with sunny weather, others are not happy


source: http://iconpng.comhttp://iconarchive.com

Limitations on existing works

• Using small number of samples

• Analysis limited to specific regions

• Relying on questionnaire surveys or interviews


Involving sampling biases andhard to capture differences by region

Previous works using Twitter

• How weather affects changes in tweeting rates [Kiciman, ICWSM 2012]

• Mood is indeed affected by weather [Hannak et al. ICWSM 2012]

• Limited to twenty metropolitan areas


source: http://goo.gl/ZksQ4d source: http://goo.gl/gn6ENM

Twitter dataset

• Period: April, 2009

• Entire tweets of U.S.

• Location of users

• Size• 38.1 million tweets

• 3 million users


Twitter crawling

• Crawling (or called “spidering”) using Twitter API

• Twitter provides API (Application Programming Interface)• anyone in Twitter can use

• it indicates all of you can gather Twitter data


Make a program gathering data


from twitter import *

user = ”disc2013"

twitter = Twitter()

results = twitter.statuses.user_timeline(screen_name = user)

for status in results:print "(%s) %s" % (status["created_at"], status["text"])

With a very few line of codes,you can get Twitter data for your research!

Sentiment Analysis

• Sentiment analysis (or opinion mining) aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document(source: Wikipedia)

• Categories• measuring polarity

• ex> I am very happy to attend DISC 2013 !!!! positive

• measuring polarity scale of sentiment across different sentiment dimensions

• ex> I am very happy to attend DISC 2013 !!!! positive: 4, negative: 0

• in this task, we utilized this way


Sentiment analysis using LIWC

• LIWC (Linguistic Inquiry and Word Count)• http://www.liwc.net

• 32 behavioral and psychological dimensions

• We can conduct sentiment analysis using 2 of them (positive / negative)

• Method• Input: aggregated tweet written in a state of a day

• Output: value of positive and negative affect


Positive score: 4.59Negative score: 2.23

http://www.liwc.net

Example: sentiment analysis of 4/1 in NY


NY_1.txt

Filename … humans affect posemo negemo ..

NY_1.txt … 0.7 6.77 4.59 2.23 …

NY_1.xls

Weather dataset

• Period: April, 2009

• Source: http://www.wunderground.com

• Using web crawling

• Gathering weather information of each day across states


http://www.wunderground.com/

Web crawling

• Read HTML codes and pick information that we want


See the relationship

• Correlations between sentiment and weather variables


Twitter sentiments Weather variables

Correlation of temperature and positive affects

• Generally, there is correlation between positive affect and temperature across states (average of r =0.14)

• Some states are showing negative correlations


Correlation of temperature and positive affects

• Generally, there is correlation between positive affect and temperature across states (average of r =0.14)

• Some states are showing negative correlations


There is positive correlation between temperature and positive affectsBut, it depends on where they live

Conclusion

• Used social media data to compare w/ environmental data - Wunderground.com vs. Twitter.com

• Identified notable relationship between the mood and weather• how weather affects mood varies across region

• The notion of ‘good’ weather can be different against each state

• Arizonans preferred humid days and Hawaiians preferred low atmospheric pressures

• Future works• Understanding cultural and economic factors by examining wide geographical regions



Kunwoo ParkM.S. candidate

Social Computing Lab

E-mail: kw.park at kaist.ac.krTwitter: @words_life

This work was presented as a poster in the International AAAI Conference on Weblogs and Social Media (ICWSM), 2013 in Boston, US.

[DISC2013] Mood and Weather: Feeling the Heat?

Science

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