You are what you tweet

I ate, and I'm eating. The gathered tweets are analyzed to determine whether they contain food types, and if they do, they are processed further. For each relevant tweet, the FoodMood system consults the geolocation component to determine the location of the Twitter user. A combination of the tweet, the recognized food, the Twitter user location, the Twitter user identity, and the sentiment orientation is stored in the database. In addition to live data from Twitter, FoodMood uses static data from the CIA World Factbook and the WHO for a country's GDP per capita and obesity levels, respectively. A total of 138 countries are present in the database, with the annotated GDP per capita and obesity levels. On request, database information from Twitter and the additional country information are combined, processed, and provided to the front end for visualization. Visualization In the visualization, color indicates range of emotion (yellow is most happy; blue is least happy), while sizes of the blocks indicate quantity of tweets (Figure 2). Icons, stick men and money bags, represent country obesity and GDP, respectively (Figure 3). These were adjusted for size to depict a range of quantities. mood.in) is a data-visualization project that captures the content and sentiment of global English-language tweets about food. Acting like a global food-sentiment barometer, FoodMood aims to better understand food-consumption patterns and their impact on the daily emotional well-being of people. This is contextualized against the backdrop of country data such as gross domestic product (GDP) and obesity levels. The FoodMood user experience is meant to be a playful and explor-ative one. Users can search for foods, discover new ones, or browse their own country's food tweets, sorting by emotion or by tweet quantity, and zoom down into the content of individual tweets. By engaging citizens with their own data about food, FoodMood comes at a highly relevant time for reflection and self-awareness. It provides an interface to engage and explore social data to lend important insights into food practices, not only for interested foodies but also for advocacy and campaign groups like Oxfam Grow, the Slow Food movement, and FairFood. Measuring Global Food Sentiment, One Tweet at a Time FoodMood's architecture is depicted in Figure 1. The system continuously gathers live data about food from Twitter by querying the standard Twitter API with terms such Every day is a living torment. Nobody understands my suffering. The only thing …