Aggie

Overview


Aggie 2.0 Team : 7 members

Personal Role: UX Designer and Researcher

Skills Used: Wireframing, Task Flows, Visual Mocks, User Research,

On Field Usability Testing: Conducted for State Elections at Ekiti, Nigeria

Project Stakeholders: MacArthur Foundation, Georgia Tech, Yar'Adua Foundation, Digital Bridge Institute

Publication: Social Media Fostering Social Good: A Case for Election Monitoring in Nigeria, Note Accepted to ICTD 2015

About

Aggie is a real-time Social Media Aggregation System geared towards election monitoring in African countries. Aggie is envisioned to ensure a smooth election process and to improve the level of information available to civil society and citizen stakeholders during and beyond elections. The system enables real- time response feedback and recommendations to improve the electoral process. Aggie has been used for the purpose of election monitoring in Nigeria, Ghana and Liberia.

Background

Aggie 1.0 is a real-time social media aggregator that generates a report for any mention of an election relevant keyword over social media. The admin has the ability to create ‘sources’ of keywords that pertain to the topic of interest. Trackers elevate relevant reports to incidents according to categories (e.g. violence, fraud, etc.)

Aggie 2.0 is the redesign of Aggie 1.0 for better usability and integration of formal reporting, in this case ELMO, with social media aggregation. Reports generated in Aggie 2.0 are from both, social media sources and on-field ELMO observers.

ELMO is an open-source Election Monitoring data collection and reporting system, built specifically with election monitoring in mind. Equipped with ELMO, observers can submit evaluations of an election process – via tablets, SMS, or directly online – in real-time to mission headquarters. Aggie 2.0 crowd sources reports generated from ELMO.

Aggie version 1.0


User Testing

Aggie 2.0 was tested out for the Ekiti State Elections in Nigeria. Throughout the study, contextual observations and interviews were used as instruments in collecting data. There were a total of 14 participants in our study. By the end of the pilot, the platform had gathered over 92,000 reports, with approximately 800 of those reports coming from our trained field observers. A report volume of 92,000 represents a rate of 128 incoming reports every minute. From the reports gathered, the backend team identified a total of 74 unique incidents relevant to the election. Nineteen incidents were verified as true and escalated to INEC during the course of the election. Thirty incidents were verified as false. Reports identified as true were communicated to all stakeholders.

A lot of design insights came by the study, some of which include: 1. Keeping a counter for re-tweets so that feeds do not become repetitive, 2. Need for a better collborative workspace, 3. Need for pop up screens to view longer reports, etc. 4. Higher credibility of Elmo over Social media, etc.