Tracking food insecurity from tweets using data mining techniques - Lukyamuzi, Andrew; Ngubiri, John; Okori, Washington (2018 IEEE ACM Symposium on Software Engineering in Africa (SEiA), 2018)
Data mining algorithms can be applied to extract useful patterns from social media conversations to monitor disasters such as tsunami, earth quakes and nuclear power accidents. While food insecurity has persistently remained a world concern, its monitoring with this strategy has received limited attention. In attempt to address this concern, UN Global Pulse demonstrated that tweets reporting food prices from Indonesians can aid in predicting actual food price increase. For regions like Kenya and Uganda where use of tweets is considered low, this option can be problematic. Using Uganda as a case study, this study takes an alternative of using tweets from all over the world with mentions of; (1) uganda +food, (2) uganda + hunger, and (3) uganda + famine for years 2014, 2015 and 2016. The study however utilized tweets on food insecurity instead of tweets on food prices. In the first step, five data mining algorithms (D-tree, SVM, KNN, Neural Networks and N-Bayes) were trained to identify tweets conversations on food insecurity. Algorithmic performance were found comparable with human labeled tweet on the same subject. In step two, tweets reporting food insecurity were generated into trends. Comparing with trends from Uganda Bureau of Statistics, promising findings have been obtained with correlation coefficients of 0.56 and 0.37 for years 2015 and 2016 respectively. The study provides a strategy to generate information about food insecurity for stakeholders such as World Food Program in Uganda for mitigation action or further investigation depending on the situation. To improve performance, future work can; (1) aggregate tweets with other datasets, (2) ensemble algorithms, and (3) apply unexplored algorithms.