Talk at Computational Social Science Workshop (CSS'14), a Lucca. September 2014 (ECCS satellite workshop)

1. Introduction Results. Event analysis Discussion Conclusions
Analysis of the Evolution of Events on Online
Social Networks
E. del Val, M. Rebollo and V. Botti
Grupo Tec. Inform.-Inteligencia Artificial
Universitat Politècnica de València
CSS 2014
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

2. Introduction Results. Event analysis Discussion Conclusions
Problem
Analysis of user behavior
How user interactions evolve in a social network associated to the
realization of a scheduled event in the real world.
4 types of events are considered
TV shows
socio-political
conferences
keynotes
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

3. Introduction Results. Event analysis Discussion Conclusions
The Hypothesis
There are significative differences in the behavior of the
participants in each type of event
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

4. Introduction Results. Event analysis Discussion Conclusions
Data set
twits containing the hashtag of the event are retrieved
interaction-based network: mentions, replies and retweets
focus on scheduled events
temporally annotated network (nodes and links)
empirical analysis
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

5. Introduction Results. Event analysis Discussion Conclusions
Events
TV shows
La voz
Topchef
BreakingBad
OperacionPalace
Keynotes
applekeynote
nuevosiphone
innovationreinvented (Nokia)
Socio-political
lomce
viacatalana
EU elections debate
Conferences
TEDValencia
seo4seos
Twitter awards 2013
InternetOfThings forum
CW’13 (web conference)
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

6. Introduction Results. Event analysis Discussion Conclusions
Event characterization
Event N E clust. d path comp. k m GC %sl
#lomce #24O 61,653 97,570 0.07 27 6.93 8,088 1.58 0.73 49,164 10.80
#via 41,166 76,094 0.07 23 8.28 8,705 1.85 0.56 30,745 12.19
catalana
#topchef12 26,044 27,155 0.05 25 8.74 10,689 1.04 0.66 12,794 12.29
#lavoz 45,914 39,891 0.07 9 2.11 19,398 0.87 0.81 17,941 6.95
#breakingbad 151,473 120,661 0.05 13 3.24 67,060 0.80 0.72 71,130 14.26
#operacion 107,606 195,470 0.08 23 8.26 19,504 1.82 0.49 85,768 15.06
Palace
#apple 3,367 1,729 0.04 4 1.27 1,827 0.51 0.93 700 1.53
keynote
#nuevos 9,509 10,600 0.05 9 2.09 1,227 1.12 0.62 7,799 6.47
iPhone
#innovation 110 95 0.04 3 1.34 32 0.86 0.83 26 10.00
reinvented
#IoTWF 4,150 17,545 0.22 9 3.96 330 4.23 0.42 3,680 30.83
#cw13 1,051 2,608 0.20 9 3.90 60 2.48 0.56 966 19.76
#seo4seos 367 1,474 0.35 6 2.91 16 4.02 0.30 347 31.07
#tedx 325 843 0.17 8 3.60 45 2.59 0.32 276 8.60
valencia
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

7. Introduction Results. Event analysis Discussion Conclusions
Message type
tv: linear, global, during
soc: linear, inter., always
keyn: global, before
conf: linear, inter., during
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
0 50 100 150 200 250 300 350
num tweets
snapshot
global
retweets
mentions
replyTo
(a) TV show: #topchef12.
60000
50000
40000
30000
20000
10000
0
0 50 100 150 200 250 300 350
num tweets
snapshot
global
retweets
mentions
replyTo
(b) Socio-political: #viacatalana
6000
5000
4000
3000
2000
1000
0
0 50 100 150 200 250 300 350
num tweets
snapshot
global
retweets
mentions
replyTo
(c) Keynote: #applekeynote
1000
800
600
400
200
0
0 100 200 300 400 500 600 700
num tweets
snapshot
global
retweets
mentions
replyTo
(d) Conference: #tedxValencia
Figure 2: Evolution of the number of global and individual messages (mentions, retweets, and reply to) in different type of events.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks
9

8. Introduction Results. Event analysis Discussion Conclusions
Network size (links and nodes)
tv: always, similar
soc: always, ""links
keyn: before, ""links
conf: but after, ""links
30000
25000
20000
15000
10000
5000
0
0 50 100 150 200 250 300 350
num links or nodes
snapshot
links
nodes
(a) TV show: #topchef12.
80000
70000
60000
50000
40000
30000
20000
10000
0
0 50 100 150 200 250 300 350
num links or nodes
snapshot
links
nodes
(b) Socio-political: #viacatalana.
3500
3000
2500
2000
1500
1000
500
0
0 50 100 150 200 250 300 350
num links or nodes
snapshot
links
nodes
(c) Keynote: #applekeynote.
900
800
700
600
500
400
300
200
100
0
0 100 200 300 400 500 600 700
num links or nodes
snapshot
links
nodes
(d) Conference: #tedxValencia.
Figure 3: Evolution of the number of nodes and links in different type of events.
the number of nodes and links increases moderately. People in the analyzed events prefer to talk and interact
before the event rather than during the keynote.
In the analyzed conference networks, the number of nodes grows rapidly before the event starts (see Figure
3d). Then, during the event, there is also an important increase in the number of nodes. Towards the end of the
event as well as after the event, the number of nodes remains almost constant. If the conference consists on two
or three days, during the first day is when a most significant increase in the number of new nodes occurs. The
number of links evolves similarly as the nodes. However, the increase in the number of interactions is produced
at a higher rate. This means that assistants to a conference are more social and interact with other assistants.
This behavior is similar to behavior of participants in socio-political networks.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

9. Introduction Results. Event analysis Discussion Conclusions
Symmetric links
tv: low, decreasing
soc: low, constant
keyn: lowest
conf: tipically high,
constant
40
35
30
25
20
15
10
5
0
% of symmetric links
0 50 100 150 200 250 300 350
symmetric links
snapshot
(a) TV show: #topchef12.
40
35
30
25
20
15
10
5
0
% of symmetric links
0 50 100 150 200 250 300 350
symmetric links
snapshot
(b) Socio-political: #viacatalana.
40
35
30
25
20
15
10
5
0
% of symmetric links
0 50 100 150 200 250 300 350
symmetric links
snapshot
(c) Keynote: #applekeynote.
40
35
30
25
20
15
10
5
0
% of symmetric links
0 100 200 300 400 500 600 700
symmetric links
snapshot
(d) Conference: #tedxValencia.
Figure 4: Evolution of the number of symmetric links in different type of events.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks
12

10. Introduction Results. Event analysis Discussion Conclusions
Cummulative degree distrib. and CCDF
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

11. Introduction Results. Event analysis Discussion Conclusions
Path length and diameter
all: grows before and
remains constant
non significative
differences
30
25
20
15
10
5
0
path Length
0 50 100 150 200 250 300 350
num steps
timeStep
diameter
(a) TV show: #topchef12.
30
25
20
15
10
5
0
path Length
0 50 100 150 200 250 300 350
num steps
timeStep
diameter
(b) Socio-political: #viacatalana.
4
3.5
3
2.5
2
1.5
1
path Length
0 50 100 150 200 250 300 350
num steps
timeStep
diameter
(c) Keynote: #applekeynote.
9
8
7
6
5
4
3
2
1
path Length
0 100 200 300 400 500 600 700
num steps
timeStep
diameter
(d) Conference: #tedxValencia.
Figure 6: Evolution of the average path length and diameter in different type of events.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks
15

12. Introduction Results. Event analysis Discussion Conclusions
Clustering
not significative in the
evolution
differences in the values
("conf, ## keyn)
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
clustering dynamics
0 50 100 150 200 250 300 350
Clustering Degree
timeStep
(a) TV show: #topchef12.
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
clustering dynamics
0 50 100 150 200 250 300 350
Clustering Degree
timeStep
(b) Socio-political: #viacatalana.
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
clustering dynamics
0 50 100 150 200 250 300 350
Clustering Degree
timeStep
(c) Keynote: #applekeynote.
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
clustering dynamics
0 100 200 300 400 500 600 700
Clustering Degree
timeStep
(d) Conference: #tedxValencia.
Figure 7: Evolution of the average clustering in different type of events.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks
16

13. Introduction Results. Event analysis Discussion Conclusions
Nodes in the giant component
all: created before the
event
tv and keyn: low
soc and conf: high
100
80
60
40
20
0
% of nodes
0 50 100 150 200 250 300 350
% of nodes that are in the giant component
timeStep
(a) TV show: #topchef12.
100
80
60
40
20
0
% of nodes
0 50 100 150 200 250 300 350
% of nodes that are in the giant component
timeStep
(b) Socio-political: #viacatalana.
100
80
60
40
20
0
% of nodes
0 50 100 150 200 250 300 350
% of nodes that are in the giant component
timeStep
(c) Keynote: #applekeynote.
100
80
60
40
20
0
% of nodes
0 100 200 300 400 500 600 700
% of nodes that are in the giant component
timeStep
(d) Conference: #tedxValencia.
Figure 8: Evolution of the % of nodes that are part of the giant component in different type of events.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks
17

14. Introduction Results. Event analysis Discussion Conclusions
Betweenness
tv: official + celebrities
soc: politics, journalists,
bloggers
keyn: media, tech sites,
bloggers
conf: official + speakers








       












(a) TV show: #topchef12.









       












(b) Socio-political: #viacatalana.









       











 

(c) Keynote: #applekeynote.









       












(d) Conference: #tedxValencia.
Figure 9: Evolution of the betweenness of the ten nodes with the highest betweenness value in different type of events.
The nodes with the highest betweenness value in the analyzed keynote networks are the nodes that represent
media, technological web pages, and bloggers. In general, the value of betweenness of these nodes starts
increasing before the event and after the event (see Figure 9c). However, during the event, the values of
betweenness remain constant since there are not a high number of interactions (new links) between nodes.
There is a big difference between the nodes that represent media or web pages and the rest of the nodes that
represent bloggers or users.
In the analyzed conference networks, the nodes with the highest betweenness are official accounts and
speakers. In general, the official account has the highest betweenness value with respect the betweenness of
the speakers (see Figure 9d). During the event, the betweenness of the official account increases at a higher
rate than the speakers account. Among the nodes that represent the speakers there is also a difference between
those that participate in the first sessions and the speakers that participate in later sessions. The betweenness
of the speakers that participate in the first sessions increases from the beginning. However, the betweenness of
the speakers that participate in later sessions is initially almost constant and starts to increase once the speaker
participates in the conference. After the event the betweenness of all nodes remains almost constant.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

15. Introduction Results. Event analysis Discussion Conclusions
Indegree and outdegree
tv, soc and keyn: indegree
same as betweenness;
outdegree anonymous
conf: balanced










       












(a) TV show: #topchef12. Indegree











       












(b) TV show: #topchef12. Outdegree







       












(c) Socio-political: #viacatalana. Indegree









       












(d) Socio-political: #viacatalana. Outdegree







       









 



(e) Keynote: #applekeynote. Indegree








       














 
(f) Keynote: #applekeynote. Outdegree







       












(g) Conference: #tedxValencia. Indegree








       












(h) Conference: #tedxValencia. Outdegree
21
Figure 10: Evolution of the indegree and outdegree of the ten nodes with the highest indegree and outdegree value in different type
of events.
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

16. Introduction Results. Event analysis Discussion Conclusions
Eigenvector
"" heterogeneous (even in
the same event type)
not significative






       












(a) TV show: #topchef12.






       












(b) Socio-political: #viacatalana.











 



       





(c) Keynote: #applekeynote.






       












(d) Conference: #tedxValencia.
Figure 11: Evolution of the eigenvector of the ten nodes with the highest eigenvector value in different type of events.
connection join the network and the eigenvector of the speakers decreases steadily. In contrast, the speakers
that participate in the event later on have an eigenvector that increases smoothly before their participation in
the event. Then, when the speakers participate in the event, their eigenvector increases sharply. This means
that other nodes with a high degree of connection establish a connection with the speakers. After this increase,
the eigenvector centrality of the last speakers remains almost constant or there is a small decrease. When the
event is going to finish, the eigenvector of all the nodes remains almost constant.
6. Discussion
After the analysis of the networks, we observed that the networks generated from the Twitter events can be
@mrebollo classified in two main groups based on the type of interactions between users. One group consists of the TV
UPV
Analysis of the Evolution of Events on Online Social Networks
show and keynote networks. The other group consists of the socio-political and conference networks.
In the group of TV show and keynote networks, users tend to participate in the event through global mes-sages.
The majority of interactions are unidirectional from unknown users to official accounts or celebrities.

17. Introduction Results. Event analysis Discussion Conclusions
Discussion
two clearly differenced types
TV shows and keynotes
global, undirectional messages
asymmetric: celebrities - annonymous
low clustering and long pathes
TV: after & before; keyn: during
socio-political and conferences
real communication among participants
higher symmetry, reciprocity
conf: official account more participative
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks

18. Introduction Results. Event analysis Discussion Conclusions
Conclusions
Conclusions
official event account barely influences
communities created around ’persons of interest’
giant component created before event
2 groups clearly differencied
significant differences among the 4 groups
Future work
follow user activity to complete de information
include decay in the links
include specific temporal measures; multiplex structure
create models for events and user profiles
@mrebollo UPV
Analysis of the Evolution of Events on Online Social Networks