Citizen-based, crowdsourced election observation initiatives are on the rise. Leading election monitoring organizations are also looking to leverage citizen-based reporting to complement their own professional election monitoring efforts. Meanwhile, the information revolution continues apace, with the number of new mobile phone subscriptions up by over 1 billion in just the past 36 months alone. The volume of election-related reports generated by “the crowd” is thus expected to grow significantly in the coming years. But international, national and local election monitoring organizations are completely unprepared to deal with the rise of Big (Election) Data.
The purpose of this collaborative research project, AIME, is to develop a free and open source platform to automatically filter relevant election reports from the crowd. The platform will include pre-defined classifiers (e.g., security incidents, intimidation, vote-buying, ballot stuffing etc.) for specific countries and will also allow end-users to create their own classifiers on the fly. The project, launched by QCRI and several key partners, will specifically focus on unstructured user-generated content from SMS and Twitter. AIME partners include a major international election monitoring organization and several academic research centers. The AIME platform will use the technology being developed for QCRI’s AIDR project: Artificial Intelligence for Disaster Response.
- Acknowledgements Fredrik Sjoberg kindly provided the Uchaguzi data which he scraped from the public website at the time.
- Qualification: Professor Michael Best has rightly noted that these preliminary results are overstated given that the machine learning analysis was carried out on corpus of pre-structured reports.