Nowcasting commodity prices using social media - Jaewoo Kim (PeerJ Computer Science, 2017)
Gathering up-to-date information on food prices is critical in developing regions, as it allows policymakers and development practitioners to rely on accurate data on food security. This study explores the feasibility of utilizing social media as a new data source for predicting food security landscape in developing countries. Through a case study of Indonesia, we developed a nowcast model that monitors mentions of food prices on Twitter and forecasts daily price fluctuations of four major food commodities: beef, chicken, onion, and chilli. A longitudinal test over 15 months of data demonstrates that not only that the proposed model accurately predicts food prices, but it is also resilient to data scarcity. The high accuracy of the nowcast model is attributed to the observed trend that the volume of tweets mentioning food prices tends to increase on days when food prices change sharply. We discuss factors that affect the veracity of price quotations such as social network-wide sensitivity and user influence.