2 nanjira 3 vs of crowdsourcing csw 14 presentation
Viability, Verification, Validity:
3Vs of Election-Based Crowdsourcing
This research was funded by Canada’s International Development Research Centre.
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
Twitter for Crowdsourcing.
Collecting information from the “crowd”
• Allows for a wide reach of people in inexpensive
• 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
What is there to (Twitter) crowdsourcing?
Viability: In what situation/events is crowdsourcing a
viable venture likely to offer worthwhile results/
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
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?
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.
Why Crowdsourcing, Kenyan Elections
• #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
(Online) Passive Crowdsourcing vs. Active
• 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
Mapping Kenyan Election Events,
Thanks to crowdsourcing!
What we did (Methodology)
Cross-comparison of different media
o Traditional Media
o Data mining from Twitter
o Uchaguzi Crowdsourcing
First tweet by a government institution
about the attack
Mining Of Twitter Data without Machine
Learning is Not Feasible
90 hrs 100 270 days No No
4.5 hrs 400 27 days No In a very
12,208 Less than 1
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
Further analysis into Twitter data from the incident
requires machine learning techniques.
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
o There are indeed considerations that are useful
for assessing and running an election-based
• Testing the 3V’s Framework on other
election-related crowdsourcing opportunities
• Move to real-time analysis of tweets
• Provide tools for verifying crowdsourced
• Integrate research to media practices
• Working with local media organizations to
build a useable tool for collecting real-
time newsworthy incidents from the crowd
Download the 3Vs Report and Crowdsourcing
Get in touch!