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The study integrates ensemble learning into a task of classifying if a news article is on food insecurity or not. Similarity algorithms were exploited to imitate human cognition, an innovation to enhance performance. Four out of six classifiers generated performance improvement with the innovation. Articles on food insecurity identified with best classifier were generated into trends which were comparable with official trends. This paper provides information useful to stake holders in taking appropriate action depending on prevailing conditions of food insecurity. Two suggestions are put forth to promote performance: (1) using articles aggregated from several news media and (2) blending more classifiers in an ensemble.

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Food insecurity is a global challenge affecting millions of people especially those from least developed regions. Famine predictions are being carried out to estimate when shortage of food is most likely to happen. The traditional data sets such as house hold information, price trends, crop production trends and biophysical data used for predicting food insecurity are both labor intensive and expensive to acquire. Current trends are towards harnessing big data to study various phenomena such sentiment analysis and stock markets. Big data is said to be easier to obtain than traditional datasets. This study shows that phone messages archives and telephone conversations as big datasets are potential for predicting food crisis. This is timely with the current situation of massive penetration of mobile technology and the necessary data can be gathered to foster studies such as this. Computation techniques such as Naïve Bayes, Artificial Networks and Support Vector Machines are prospective candidates in this strategy. If the strategy is to work in a nation like Uganda, areas of concern have been highlighted. Future work points at exploring this approach experimentally.