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

arXiv: https://arxiv.org/abs/2211.16506 · HTML: https://mlsanity.com/paper/2211.16506/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [Towards Real-Time, Country-Level Location Classification of Worldwide Tweets](https://mlsanity.com/paper/1604.07236/) (2016)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [Inferring the Origin Locations of Tweets with Quantitative Confidence](https://mlsanity.com/paper/1305.3932/) (2013)
- [SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies](https://mlsanity.com/paper/1801.07757/) (2018)
- [End-to-end Network for Twitter Geolocation Prediction and Hashing](https://mlsanity.com/paper/1710.04802/) (2017)
- [Twitter conversations predict the daily confirmed COVID-19 cases](https://mlsanity.com/paper/2206.10471/) (2022)

## Built on by (most cited first, 6 of 6)

- [Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter](https://mlsanity.com/paper/2406.15362/) (2024)
- [CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://mlsanity.com/paper/2309.05494/) (2023)
- [CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media](https://mlsanity.com/paper/2405.11897/) (2024)
- [Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://mlsanity.com/paper/2403.16614/) (2024)
- [Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets](https://mlsanity.com/paper/2503.22856/) (2025)
- ["Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts](https://mlsanity.com/paper/2502.16839/) (2025)

## Abstract (arXiv)

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.