Mining Indonesian Tweets to Understand Food Price Crises



Food prices have a direct effect on the purchasing power of a large part of the Indonesian population, and increases pose a threat to household food security, particularly when inflation affects the price of staple foods such as rice or soybeans. Particularly for poor households, food accounts for almost 75 percent of total spending. Government’s occasional efforts to reduce fuel subsidies have been known to drive up food prices. It is the government’s concern to respond to these shocks and try to mitigate their negative impact, as early as possible. The use of social media is widespread in Indonesia; the country has the fourth largest Facebook population in the world, and the third largest number of Twitter users worldwide. The 20 million user accounts in Jakarta make it the city with the largest Twitter presence in the world.


Operating on the premise that online social media conversations might represent a new source of information to monitor food security, this research analyses Twitter conversations related to food price increases amongst Indonesians during the period from March 2011 to April 2013. This research also explores the relations between such conversations, food price inflation and external events.


Taxonomies (groups of words and phrases with related meanings) relevant to food and fuel price increases were developed in the Bahasa Indonesia language in order to identify relevant content. Using Crimson Hexagon’s ForSight software, a classification algorithm was trained to categorize the extracted tweets as positive, negative, confused, or neutral in order to analyze the sentiment of these food price-related tweets. Using simple time series analysis we quantify the correlation between the volume of food-related Twitter conversations and official food inflation statistics, and between food and fuel-related tweet volumes. Spot checks using qualitative method have also been done in several cities.


We found a relationship between retrospective official food inflation statistics and the number of tweets speaking about food price increases (r=0.42). We later found, upon analyzing fuel price tweets, that there was a perceived relationship between food and fuel prices. In particular, we found a significant correlation (r=0.58) between the two topics suggesting that even potential (rather than realised) fuel price rises affect people’s perception of food security.


Our research shows that automated monitoring of public sentiment on social media, combined with contextual knowledge, has the potential to be a valuable real-time proxy for food-related economic indicators. In addition, social media analysis can be used to uncover people’s reactions to fuel discussions that affect public perception of food issues. If the analysis includes geographical mentions, it could help to differentiate the variability among cities/regions. 4 Current challenges to overcome include how to establish high-frequency models of food prices and validate them using official statistics, how to filter out noise due to non-relevant news items and how to harness the potential of inferring demographics. If social media data mining to model food prices matures to become robust in the future, statistical institutes might consider including social media monitoring into official statistics channels.

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