This document summarizes social network data related to the 2017 DKI Jakarta gubernatorial election between three candidates. Data was collected from over 166,000 tweets between February 8-11, 2017. Candidate 3 received the most tweets at over 82,000. For each candidate, the document analyzes the conversation universe, dominant groups, top actor interactions, and overall network metrics. It finds that Candidate 2 had the densest network with less distinct groups, while Candidates 1 and 3 had more separated, influential groups. The network analysis provides insight into discussion dynamics but requires sentiment analysis for predictions.

1. PILKADA DKI 2017
SOCIAL NETWORK MODEL
early report
8-11 Feb 2017 (60 hours)
by andry alamsyah
andry.alamsyah@gmail.com

2. DATA PROFILE
• data collection between 8-11 february 2017 (60 hours)
• at 10 february, there were last round debat between the
candidates, thus it increase number of tweets about the
election after the debat
• total tweet collected166593, where candidate 1 (35380),
candidate 2 (49028), candidate 3 (82185)
• Raw data size : 900 MB

3. • candidate 1 : #jakartauntukrakyat #ahyfordki1
#MuslimberSatupilihno1 #AgusSylviKonsisten
#SATUkanjakarta #JakartaForAll,Agus Sylvi
• candidate 2 : #perjuanganbelumselesai #coblosbadjanomor2
#FreeAhok #BadjaMelaju #SekuatBadja #Gue2 #Badjajuara
#salamduajari,Ahok Djarot
• candidate 3 : #salambersama #TerbuktiOkOce #majubersama
#CoblosPecinya #AniesSandiCintaUlama,Anies Sandi
DATA PROFILE (#HASHTAG)

4. CANDIDATE 1 :
THE
CONVERSATION
UNIVERSE

5. CANDIDATE 1: DETAILS ON DOMINANT GROUP (PURPLE)

6. CANDIDATE 1:
INTERACTION
BETWEEN TOP
ACTORS

7. CANDIDATE 1: OVERALL METRIC RANKS

8. CANDIDATE I OVERVIEW
• There are 8505 actors involved in conversations,
there are 29836 conversations.
• There 3 dominat groups (purple, green, blue) who
dominate 60 percent of overall conversations
• the biggest group contain @AgusYudhoyono
@Abaaah @SBYudhoyono (27 percent size)

9. CANDIDATE 2 :
THE
CONVERSATION
UNIVERSE

10. CANDIDATE 2:
DETAILS ON
DOMINANT
GROUP

11. CANDIDATE 2:
INTERACTION
BETWEEN TOP
ACTORS

12. CANDIDATE 2: OVERALL METRIC RANKS

13. CANDIDATE 2 OVERVIEW
• There are 15745 actors involved in conversations,
there are 44834 conversations.
• The groups are less dominant than other candidate
network, the biggest group size only 10 percent of
overall network.
• the dominant actors located on different groups, can
be seen from their different node colors

14. CANDIDATE 3 :
THE
CONVERSATION
UNIVERSE

15. CANDIDATE 3:
DETAILS ON
DOMINANT
GROUP

16. CANDIDATE 3:
INTERACTION
BETWEEN TOP
ACTORS

17. CANDIDATE 3: OVERALL METRIC RANKS

18. CANDIDATE 3 OVERVIEW
• There are 12744 actors involved in conversations, there are
22565 conversations.The biggest number of tweets comparing to
other candidates (82185 tweet = almost 50 percent of all tweet
data)
• Conversation / tweet ratio is small, mostly is individual tweet or
tweets that dont generate conversation
• There 3 dominant groups (purple (26,76%), green(23,67%),
blue(18,51%)) who dominate 69 percent of overall conversations

19. CONCLUSION (TEMPORARY)
• From this methods, we can measure the dynamics of group formation and the
tendency of actors to form group inside a candidate social network
• Network density of number 2 is higher than other, thus it means it generate more
conversation than other candidates network
• Candidate 1 and 3 have higher modularity value than candidate 2, it means the
groups are well separated (distinct separation), while in number 2, groups are less
distinct separation, where people can jump from one to other topics (groups) easily.
• Can be concluded that the conversation idea / topics in network number 2 is more
natural, because they are coming from the masses, while in number 1 and number 3,
they are mostly generated by influential actors ..

20. NOTE
• The social network in this presentation describe the
dynamcis of social conversation
• To predict / model who is the winner, we need other
measurement such as sentiment analysis
• With large scale social data, sentiment analysis process
will take times, while social network methods will give
very fast analysis.