Presentation at WWW, held in Lyon, April 2012. Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users’ activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors. This work was done in collaboration with Bruno Gonçalves, Jose J. Ramasco, and Ciro Cattuto.

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2. Outline
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6. Data and Methodology
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12. Activity Timelines & Peaks
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13. Classes of Peaks
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14. Classes of Peaks
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15. Classes of Peaks
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16. Classes of Peaks
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17. Classes of Peaks
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18. Classes of Peaks
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19. Classes of Peaks
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coalition
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watchmen
job
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superbowl
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blackout
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snowmageddon
nsotu
zombies
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brand
skittles
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socialmedia
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peace
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amazonfail
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20. Classes of Peaks
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superbowl
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blackout
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nsotu
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21. Classes of Peaks
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cricket
google
hadopi
inaug09
drupalcon
coalition
geekw2e humor
davos
watchmen
job
house
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superbowl
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blackout
oscar
snowmageddon
nsotu
zombies
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skittles
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socialmedia
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22. Classes of Peaks
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job
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23. Classes of Peaks
! 5(%678',$B3)I/0'&7$B)
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cricket
google
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inaug09
drupalcon
coalition
geekw2e humor
davos
watchmen
job
house
mikeyy
superbowl
gfail
blackout
oscar
snowmageddon
nsotu
zombies
rp09
brand
skittles
phish
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socialmedia
winnenden
peace
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earthday
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24. Social Semantics
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b_c<P)K(217<0)<,$8)F$B-<)
?,/..7(2P)1/..'%Q'%$()
[$B-`/,)<3(</,)-/,/6%$()
c'(2D'2/)-/,/6%$()
EF//,)6$(6/8,<)
!$"#$%

25. Social Semantics
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
!"#$%"&'"()
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!"#$%&'("#$)(*+%&,-#.%&/#))0%1112
!3-4('(5%&6))7%&)+/8)4%&/)+/-(%1112
!0+489%&,-+#'8'-(%&0-#',)%&/,:--#%1112
!"'(+#%&3))8%&3-;')%&,-(8)/8%&3$/',+#%1112
!<-=%&'(+$5$4+8'-(%&;-8)%&6+8,:'(5%1112
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!6'(%&,:+(5)%&,4+/:%&"'4)%1112
!8)+%&/8$""%&0+5)%&/'#;)4%1112
!()6/%&'("-%&#'/8%&$0.+8)%1112
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26. Social Semantics
/=$B/)8/'9)
! !"#$%&'()*)+,"-.(!/0)(1)2%#3-(
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27. Social Semantics
/=$B/)8/'9)
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28. Social Semantics
/=$B/)8/'9)
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29. Social Semantics
/=$B/)8/'9)
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30. Information Spreading
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
! 5('13Q7(2)7(=$B.'%$()<8B/'-7(2)$=),0/)8/'9)61'<</<)
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#."#$%

31. Information Spreading
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
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32. Summary
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
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33. Summary
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
! A-/(%R6'%$()$=):)-7<6B/,/)8/'9)61'<</<)F7,0)
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34. Thank you
• g'(/G/)c/0.'(( ) )N'(/G/"1/0.'(V!h/<,D-7'(,"D8="/-D)
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35. References
! M%N)<C(%<C,%O%G+)C(66(8%M+PI76%,QC<H*E%EB<77(7%)(R(<B(,%PQ%H(<7I)*C=%6:(%
)(7K+C7(%AICE6*+C%+A%<%7+E*<B%7Q76(H8%'STGU%-.&V-&@$2U%!../8%
! W8%X<C=%<C,%W8%Y(7D+R(E8%'<66()C7%+A%6(HK+)<B%R<)*<6*+C%*C%+CB*C(%H(,*<8%JC%')+E8%+A%
6:(%A+I)6:%TN9%*C6()C<6*+C<B%E+CA()(CE(%+C%Z(P%7(<)E:%<C,%,<6<%H*C*C=U%K<=(7%
-110-/@U%!.--8%
! 98%[B<E:+7U%N8%9((DU%8%[<=(C<U%<C,%O8%]IC+KIB+78%J,(C6*AQ*C=%7*H*B<)*6*(7U%
K()*+,*E*6*(7%<C,%PI)767%A+)%+CB*C(%7(<)E:%^I()*(78%JC%')+E8%!..$%TN9%GJ]9_O%
*C6()C<6*+C<B%E+CA()(CE(%+C%9<C<=(H(C6%+A%,<6<U%K<=(7%-#-0-$!U%!..$8%
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8& #&"#$%

36. All Hashtags
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8& #@"#$%

37. Semantic Grounding
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
!"#$%&'()&"%
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V!"H1%-)*0I")7%")(/$"5@A(C+JCKBCDLEM8
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Y!">%&%)%"9$%2"2%0%2%-6%$"5N9$%28"(-1")*-I"92&$"57))O:..)*-I92&!6,'.AP"QREM8
V!">%&%)%"$O%6*(&"67(2(6)%2$"5AP(C+JCKBCDE8
Z!";S)2(6)")%2'$"5$%O(2()%1"TI"T&(-U$8
6)"71-"8318$9":(;
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38. Hashtag Statistics
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
!"#$%&'()'"*%&*
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39. Hashtag Similarities
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!"#
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40. WordNet Tree
!"#$%$&'$()"##&&&&&&&&&&&&&&&&&&&&&&&&&&&*+#"),-".&/."00$0&12&/1..$-34$&5%$#31#&,#&67,%$8&
!"#$#%
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6!/*+!-:@=C@>
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