End-to-end Network for Twitter Geolocation Prediction and Hashing
This work addresses geolocation prediction for social media analysis, but it is incremental as it builds on existing methods with modest performance gains.
The authors tackled the problem of predicting tweet geolocation using an end-to-end neural network that processes raw Twitter metadata, achieving a 2%-6% improvement over state-of-the-art systems. They also extended the model to compress representations into binary codes, producing more compact codes than benchmark hashing algorithms.
We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.