Editing Community Data Science Course (Spring 2015)/Day 6 Project
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== Download and test the Twitter project == | == Download and test the Twitter project == | ||
# Right click the following file, click "Save Target as..." or "Save link as...", and save it to your Desktop directory: http://mako.cc/teaching/2015/ | # Right click the following file, click "Save Target as..." or "Save link as...", and save it to your Desktop directory: http://mako.cc/teaching/2015/cdsw-spring/twitter-data-examples.zip | ||
# Unpack the zip file as we have in previous projects. | # Unpack the zip file as we have in previous projects. | ||
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[[File:Champagne.png|100px]][[File:Party.png|125px]] | [[File:Champagne.png|100px]][[File:Party.png|125px]] | ||
=== Potential exercises === | |||
'''Who are my followers?''' | |||
1) Use sample 2 to get your followers. | |||
2) For each of your followers, get *their* followers (investigate time.sleep to throttle your computation) | |||
3) Identify the follower you have that also follows the most of your followers. | |||
4) How many handles follow you but none of your followers? | |||
5) Repeat this for people you follow, rather than that follow you. | |||
'''Topics and Trends''' | |||
1) Use sample 3 to produce a list of 1000 tweets about a topic. | |||
2) Look at those tweets. How does twitter interpret a two word query like "data science" | |||
3) Eliminate retweets [hint: look at the tweet object!] | |||
4) For each tweet original tweet, list the number of times you see it retweeted. | |||
5) Get a list of the URLs that are associated with your topic. | |||
'''Geolocation''' | |||
1) Alter the streaming algorithm to include a "locations" filter. You need to use the order sw_lng, sw_lat, ne_lng, ne_lat for the four coordinates. | |||
2) What are people tweeting about in Times Square today? | |||
2.5) Bonus points: set up a bounding box around TS and around NYC as a whole. | |||
Can you find words that are more likely to appear in TS? | |||
3) UW is playing Arizona in football today. Set up a bounding box around the Arizona stadium and around UW. Can you identify tweets about football? Who tweets more about the game? | |||
# you can use d = api.search(geocode='37.781157,-122.398720,1mi')Β to do | |||
# static geo search. | |||
=== Congratulations!!!!=== | |||
You now know how to capture data from Twitter that you can use in your research!!! Next workshop we'll play with some fun analytical tools. In the meantime, here are [[A_Few_Words_of_Caution_About_Using_Twitter_Data_for_Science|A_Few_Words_of_Caution_About_Using_Twitter_Data_for_Science]] |