Density Estimation for Geolocation via Convolutional Mixture Density Network
This provides a method for fine-grained geolocation applications on social media, offering richer information including location ambiguity, but it is incremental as it builds on existing mixture density networks.
The paper tackles the problem of low geographic information on Twitter by estimating tweet locations as probability distributions rather than discrete classifications, achieving the highest prediction performance for exact coordinates.
Nowadays, geographic information related to Twitter is crucially important for fine-grained applications. However, the amount of geographic information avail- able on Twitter is low, which makes the pursuit of many applications challenging. Under such circumstances, estimating the location of a tweet is an important goal of the study. Unlike most previous studies that estimate the pre-defined district as the classification task, this study employs a probability distribution to represent richer information of the tweet, not only the location but also its ambiguity. To realize this modeling, we propose the convolutional mixture density network (CMDN), which uses text data to estimate the mixture model parameters. Experimentally obtained results reveal that CMDN achieved the highest prediction performance among the method for predicting the exact coordinates. It also provides a quantitative representation of the location ambiguity for each tweet that properly works for extracting the reliable location estimations.