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Analyzing the language of food on social media
2014 IEEE International Conference on Big Data (Big Data)
We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complexdoi:10.1109/bigdata.2014.7004305 dblp:conf/bigdataconf/FriedSKHB14 fatcat:bxuolty3v5e4hfequtxemtwycq