# Location Inference from Tweets using Grid-based Classification

## TL;DR

This paper introduces a novel grid-based classification method using supervised Naive Bayes and user metadata to infer user locations from tweets, achieving over 57% accuracy at city-level granularity.

## Contribution

It presents the first geo-enriched grid-based classification approach for location inference from tweets, improving accuracy over existing methods.

## Key findings

- Achieves over 57% accuracy at city-level
- First to use geo-enriched grid-based classification for this task
- Provides a framework for content-based location estimation

## Abstract

The impact of social media and its growing association with the sharing of ideas and propagation of messages remains vital in everyday communication. Twitter is one effective platform for the dissemination of news and stories about recent events happening around the world. It has a continually growing database currently adopted by over 300 million users. In this paper we propose a novel grid-based approach employing supervised Multinomial Naive Bayes while extracting geographic entities from relevant user descriptions metadata which gives a spatial indication of the user location. To the best of our knowledge our approach is the first to make location inference from tweets using geo-enriched grid-based classification. Our approach performs better than existing baselines achieving more than 57% accuracy at city-level granularity. In addition we present a novel framework for content-based estimation of user locations by specifying levels of granularity required in pre-defined location grids.