Recent developments in the field of peace science have been made possible through the availability of conflict event data. However, most event datasets rely heavily on secondary media sources, which are prone to reporting bias.
In this project we develop a novel approach to creating conflict event data, using “crowdseeding”. This technique relies on a network of paid on-the-ground participants who are trained to observe pertinent information and enter it into secure online platform immediately. Using crowdseeding has several advantages: it allows to receive important information and monitor situations (nearly) in real-time, to circumvent reporting biases from media sources, to increase the spatial precision of covered events, and to generate weights for each event (based on geographic distances and source of information), among others.
We use this technique to collect disaggregated conflict event data in a pilot study in Syria. In the pilot study, reporters living inside Syria upload real-time information on both violence and peace events. Reported events specifically include talks and agreements amongst local actors, related to both violence (e.g. ceasefires) and non-violence (e.g. access for aid workers).