Viability, Verification, Validity:
3Vs of Election-Based Crowdsourcing
Nanjira Sambuli
@NiNanjira
www.research.ihub.co.ke
...
Twitter and the ‘Tweeps’
  Twitter…can be more of a news media than even a social
network (Kwak et al, 2010)
  Breaking ne...
Twitter for Crowdsourcing.
Collecting information from the “crowd”
• Allows for a wide reach of people in inexpensive
ways...
What is there to (Twitter) crowdsourcing?
Viability: In what situation/events is crowdsourcing a
viable venture likely to ...
Crowdsourcing during an Election
•  What, if any, particular conditions should be in place
for crowdsourcing of informatio...
Why Elections?
o Elections in Kenya have been noted to spark many
online conversations, especially with the continued
upta...
Why Crowdsourcing, Kenyan Elections
and #KoT
•  #KoT have participated in crowdsourcing
activities severally, under hashta...
(Online) Passive Crowdsourcing vs. Active
Crowdsourcing
• Active – Open call made for participation
(e.g. Ushahidi’s Crowd...
Mapping Kenyan Election Events,
Thanks to crowdsourcing!
What we did (Methodology)
Cross-comparison of different media
sources:
o  Traditional Media
o  Data mining from Twitter
o ...
Research Findings
Passive
Crowdsourcing
is Viable During
the Elections in
Kenya
Twitter
Breaks
News
An Example from the Nairobi Westgate Mall Terror
Attack
First tweet about the attack at 12:38PM (September 21st,
2013)
First tweet by media about the attack
First tweet by a government institution
about the attack
Mining Of Twitter Data without Machine
Learning is Not Feasible
Search
method
Time
taken
Number of
Newsworthy
Tweets
Searc...
From the Westgate Incident…
Mining tweets from the Westgate attack manually
was labour-intensive, limiting us to sufficien...
Therefore…
o Kenyan social media content is rich with real-time
updates of happenings that might not be present
in mainstr...
The 3Vs of Crowdsourcing Framework
Next Steps
• Testing the 3V’s Framework on other
election-related crowdsourcing opportunities
• Move to real-time analysis...
Download the 3Vs Report and Crowdsourcing
Framework at
http://bit.ly/3VsCrowdsourcing
Get in touch!
nanjira@ihub.co.ke
res...
2   nanjira 3 vs of crowdsourcing  csw 14 presentation
2   nanjira 3 vs of crowdsourcing  csw 14 presentation
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2 nanjira 3 vs of crowdsourcing csw 14 presentation

  1. 1. Viability, Verification, Validity: 3Vs of Election-Based Crowdsourcing Nanjira Sambuli @NiNanjira www.research.ihub.co.ke This research was funded by Canada’s International Development Research Centre.
  2. 2. Twitter and the ‘Tweeps’   Twitter…can be more of a news media than even a social network (Kwak et al, 2010)   Breaking news and coverage of real-time events are all shared under the 140-character limit   Twitter users search for up-to-the-second information and updates on unfolding events
  3. 3. Twitter for Crowdsourcing. Collecting information from the “crowd” • Allows for a wide reach of people in inexpensive ways • Large amounts of data can be obtained quickly, and often in real time • Can leverage mobile and/or online technology • Crowdsourcing fosters citizen engagement with the information—to dispute, confirm, or acknowledge its existence.
  4. 4. What is there to (Twitter) crowdsourcing? Viability: In what situation/events is crowdsourcing a viable venture likely to offer worthwhile results/ outcomes? Validity: Does crowd-sourced information offer a true reflection of the reality on the ground? Verification: Is there a way in which we can verify that the information provided through crowdsourcing is indeed valid? If so, can the verification process be automated?
  5. 5. Crowdsourcing during an Election •  What, if any, particular conditions should be in place for crowdsourcing of information to be viable during an election period? •  Can crowd-sourced information be validated during an election period? If so, what is the practical implementation of doing so? •  How do different crowdsourcing methods contribute to the quality of information collected?
  6. 6. Why Elections? o Elections in Kenya have been noted to spark many online conversations, especially with the continued uptake of social media; o Citizens have an important role to play to contribute information from the ground; o Existing election crowdsourcing initiatives (such as Uchaguzi), but none use passive crowdsourcing; o Research exists around crowdsourcing during disasters, but does not yet exist around elections.
  7. 7. Why Crowdsourcing, Kenyan Elections and #KoT •  #KoT have participated in crowdsourcing activities severally, under hashtags such as #CarPoolKE, #findfuel, #SomeoneTellCNN etc. •  Approximately 90,000 tweets generated during the first Kenyan Presidential Debates (as monitored using popular hashtags) •  Election-campaigning was also digital
  8. 8. (Online) Passive Crowdsourcing vs. Active Crowdsourcing • Active – Open call made for participation (e.g. Ushahidi’s Crowdmap). • Passive – Sifting through content already being generated (e.g. on Twitter/ Facebook) to capture relevant information.
  9. 9. Mapping Kenyan Election Events, Thanks to crowdsourcing!
  10. 10. What we did (Methodology) Cross-comparison of different media sources: o  Traditional Media o  Data mining from Twitter o  Uchaguzi Crowdsourcing o  Fieldwork
  11. 11. Research Findings
  12. 12. Passive Crowdsourcing is Viable During the Elections in Kenya
  13. 13. Twitter Breaks News
  14. 14. An Example from the Nairobi Westgate Mall Terror Attack First tweet about the attack at 12:38PM (September 21st, 2013)
  15. 15. First tweet by media about the attack
  16. 16. First tweet by a government institution about the attack
  17. 17. Mining Of Twitter Data without Machine Learning is Not Feasible Search method Time taken Number of Newsworthy Tweets Search time for whole data set Viable for real time analysis Viable for post-data analysis Linear search 90 hrs 100 270 days No No Keyword search 4.5 hrs 400 27 days No In a very limited way ML, supervised learning Less than 6 mins, 1.5 hrs labeling 12,208 Less than 1 sec Yes Yes
  18. 18. From the Westgate Incident… Mining tweets from the Westgate attack manually was labour-intensive, limiting us to sufficiently analysing the first half hour (12:38 PM – 1:18 PM GMT+ 3) Further analysis into Twitter data from the incident requires machine learning techniques.
  19. 19. Therefore… o Kenyan social media content is rich with real-time updates of happenings that might not be present in mainstream media reports. o Mining of crowd-sourced data appears to be high value when one is looking for timely, local information. o There are indeed considerations that are useful for assessing and running an election-based crowdsourcing activity.
  20. 20. The 3Vs of Crowdsourcing Framework
  21. 21. Next Steps • Testing the 3V’s Framework on other election-related crowdsourcing opportunities • Move to real-time analysis of tweets • Provide tools for verifying crowdsourced information. • Integrate research to media practices • Working with local media organizations to build a useable tool for collecting real- time newsworthy incidents from the crowd
  22. 22. Download the 3Vs Report and Crowdsourcing Framework at http://bit.ly/3VsCrowdsourcing Get in touch! nanjira@ihub.co.ke research@ihub.co.ke Thank You!

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