# End-to-end Network for Twitter Geolocation Prediction and Hashing

arXiv: https://arxiv.org/abs/1710.04802 · HTML: https://mlsanity.com/paper/1710.04802/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Where did you tweet from? Inferring the origin locations of tweets based on contextual information](https://mlsanity.com/paper/2211.16506/) (2022)
- [Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data](https://mlsanity.com/paper/2106.07114/) (2021)
- [Multi-modal Representation Learning for Social Post Location Inference](https://mlsanity.com/paper/2306.07935/) (2023)

## Abstract (arXiv)

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.