Where did you tweet from? Inferring the origin locations of tweets based on contextual information

TL;DR

This paper presents the True Origin Model, a machine learning framework that infers the likely origin locations of tweets using natural language understanding, achieving promising accuracy across multiple geographic levels despite limited geotagged data.

Contribution

The work introduces the True Origin Model and locBERT, novel tools for inferring tweet origins, addressing the Location A/B problem with improved accuracy and a new dataset for future research.

Findings

Achieves 80% accuracy at country level

Achieves 58% accuracy at city level

Highlights issues with current ground truth methodologies

Abstract

Public conversations on Twitter comprise many pertinent topics including disasters, protests, politics, propaganda, sports, climate change, epidemics/pandemic outbreaks, etc., that can have both regional and global aspects. Spatial discourse analysis rely on geographical data. However, today less than 1% of tweets are geotagged; in both cases--point location or bounding place information. A major issue with tweets is that Twitter users can be at location A and exchange conversations specific to location B, which we call the Location A/B problem. The problem is considered solved if location entities can be classified as either origin locations (Location As) or non-origin locations (Location Bs). In this work, we propose a simple yet effective framework--the True Origin Model--to address the problem that uses machine-level natural language understanding to identify tweets that conceivably contain their origin location information. The model achieves promising accuracy at country (80%), state (67%), city (58%), county (56%) and district (64%) levels with support from a Location Extraction Model as basic as the CoNLL-2003-based RoBERTa. We employ a tweet contexualizer (locBERT) which is one of the core components of the proposed model, to investigate multiple tweets' distributions for understanding Twitter users' tweeting behavior in terms of mentioning origin and non-origin locations. We also highlight a major concern with the currently regarded gold standard test set (ground truth) methodology, introduce a new data set, and identify further research avenues for advancing the area.