1
Sptio-Temporal Dynamics of
Geo-tagged Tweets in
West Lafayette, IN
Yue Li
li1050@purdue.edu
2
Introduction
• Twitter
− The most popular micro-blogging
site
− Tweets with longitude and latitude
− A gold mine for sch...
3
Methodology
• Collect 4160 geo-tagged tweets using the Twitter
Streaming API from April 11, 2013 to April 18
• Compare t...
4
Results
• Geo-tagged Tweet Clusters on Weekdays
5
Results
• Geo-tagged Tweet Clusters on Weekend
6
Results
7
Results
8
Results
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
NUMBEROFTWEETS
TIME
COUN...
9
Results
• Geo-tagged Tweet Clusters from 11AM to 12PM
10
Results
• Geo-tagged Tweet Clusters from 20PM to 21PM
11
Conclusion
• Analyze the sptio-temporal pattern of geo-tagged tweets to
discover the human mobility pattern hidden behi...
12
References
• Ghosh, D., & Guha, R. (2013). What are we ‘tweeting’about
obesity? Mapping tweets with topic modeling and
...
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Spatio-Temporal Dynamics of Geo-tagged Tweets

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Spatio-Temporal Dynamics of Geo-tagged Tweets

  1. 1. 1 Sptio-Temporal Dynamics of Geo-tagged Tweets in West Lafayette, IN Yue Li li1050@purdue.edu
  2. 2. 2 Introduction • Twitter − The most popular micro-blogging site − Tweets with longitude and latitude − A gold mine for scholars in linguistics, sociology, economics, health, and psychology (Ghosh & Guha, 2013) • West Lafayette, IN • Most densely populated city in IN • Home of Purdue University
  3. 3. 3 Methodology • Collect 4160 geo-tagged tweets using the Twitter Streaming API from April 11, 2013 to April 18 • Compare the spatial distribution of geo-tagged tweets on weekdays with those at the weekend − Point Density tool in ArcGIS 10.1 − Clustering in Esri Maps for Excel • Analyze the tweets on an hourly basis
  4. 4. 4 Results • Geo-tagged Tweet Clusters on Weekdays
  5. 5. 5 Results • Geo-tagged Tweet Clusters on Weekend
  6. 6. 6 Results
  7. 7. 7 Results
  8. 8. 8 Results 0 50 100 150 200 250 300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 NUMBEROFTWEETS TIME COUNT OF GEO-TAGGED TWEETS BY HOUR weekday weekend
  9. 9. 9 Results • Geo-tagged Tweet Clusters from 11AM to 12PM
  10. 10. 10 Results • Geo-tagged Tweet Clusters from 20PM to 21PM
  11. 11. 11 Conclusion • Analyze the sptio-temporal pattern of geo-tagged tweets to discover the human mobility pattern hidden behind • Proves the feasibility of using geo-tagged tweets, in local market research, market promotions, human mobility analysis, and even education regulation in a “college town” such as West Lafayette • Future work − Semantic analysis, topic modeling, and content analysis, aiming to track the spread of ideas and thoughts in local area − Framework of extracting spatio-temporal social patterns from geo- tagged tweets in a city scale to help social researchers, demographic surveyors, market researchers, advertisers, and policy makers
  12. 12. 12 References • Ghosh, D., & Guha, R. (2013). What are we ‘tweeting’about obesity? Mapping tweets with topic modeling and Geographic Information System. Cartography and Geographic Information Science, 40(2), 90-102. • Google Earth

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