Men's DC Hi Tx Evan Chocolate Smith Mood Variations
A number of interesting trends can be observed in the data. First, overall daily variations can be seen (first graph), with the early morning and late evening having the highest level of happy tweets. Second, geographic variations can be observed (second graph), with the west coast showing happier tweets in a pattern that is consistently three hours behind the east coast.
Similar variations were discovered independently by Michael Macy and Scott Golder, and first reported in the talk "Answers in Search of a Question" at the New Directions in Text Analysis Conference in May 2010.
Weekly trends can be observed as well, with weekends happier than weekdays. The peak in the overall tweet mood score is observed on Sunday mornings, and the trough occurs on Thursday evenings.
DC Tx Evan Chocolate Men's Hi Smith About the Data and Visualization
cartogram is a map in which the mapping variable (in this case, the number of tweets) is substituted for the true land area. Thus, the geometry of the actual map is altered so that the shape of each region is maintained as much as possible, but the area is scaled in order to be proportional to the number of tweets that originate in that region. The result is a density-equalizing map. The cartograms in this work were generated using the cart software by
Mark E. J. Newman, available at
Who We Are
We are researchers from Northeastern University and Harvard University, studying the characteristics and dynamics of Twitter.
 Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K.P. Men's Tx DC Evan Hi Smith Chocolate Measuring User Influence in Twitter: The Million Follower Fallacy. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM), Washington, DC, May 2010.
 Bradley, M.M., & Lang, P.J. Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings. Technical report C-1, The Center for Research in Psychophysiology, University of Florida.
 O'Connor, B., Balasubramanyan, R., Routedge, B., & Smith, N. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM). Washington, DC, May 2010.