# A Transformer-based Framework for POI-level Social Post Geolocation

arXiv: https://arxiv.org/abs/2211.01336 · HTML: https://mlsanity.com/paper/2211.01336/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 26 Oct 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)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Weakly-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/1812.11270/) (2018)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- [CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding](https://mlsanity.com/paper/2005.06627/) (2020)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [A Hierarchical Location Prediction Neural Network for Twitter User Geolocation](https://mlsanity.com/paper/1910.12941/) (2019)
- [Spatial Language Representation with Multi-Level Geocoding](https://mlsanity.com/paper/2008.09236/) (2020)
- [Density Estimation for Geolocation via Convolutional Mixture Density Network](https://mlsanity.com/paper/1705.02750/) (2017)
- [On Predicting Geolocation of Tweets using Convolutional Neural Networks](https://mlsanity.com/paper/1704.05146/) (2017)
- [A Probabilistic Framework for Location Inference from Social Media](https://mlsanity.com/paper/1702.07281/) (2017)

## Built on by (most cited first, 13 of 13)

- [Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact](https://mlsanity.com/paper/2410.17532/) (2024)
- [BTRec: BERT-Based Trajectory Recommendation for Personalized Tours](https://mlsanity.com/paper/2310.19886/) (2023)
- [Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction](https://mlsanity.com/paper/2403.13872/) (2024)
- [Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media](https://mlsanity.com/paper/2405.05760/) (2024)
- [SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights](https://mlsanity.com/paper/2302.09938/) (2023)
- [Predicting the Geolocation of Tweets Using transformer models on Customized Data](https://mlsanity.com/paper/2303.07865/) (2023)
- [Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach](https://mlsanity.com/paper/2304.08495/) (2023)
- [Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech](https://mlsanity.com/paper/2405.06665/) (2024)
- [Georeferencing complex relative locality descriptions with large language models](https://mlsanity.com/paper/2512.14228/) (2025)
- [SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis](https://mlsanity.com/paper/2311.11071/) (2023)
- [FewUser: Few-Shot Social User Geolocation via Contrastive Learning](https://mlsanity.com/paper/2404.08662/) (2024)
- [Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencing](https://mlsanity.com/paper/2507.08575/) (2025)
- [A Social Data-Driven System for Identifying Estate-related Events and Topics](https://mlsanity.com/paper/2508.03711/) (2025)

## Abstract (arXiv)

POI-level geo-information of social posts is critical to many location-based applications and services. However, the multi-modality, complexity and diverse nature of social media data and their platforms limit the performance of inferring such fine-grained locations and their subsequent applications. To address this issue, we present a transformer-based general framework, which builds upon pre-trained language models and considers non-textual data, for social post geolocation at the POI level. To this end, inputs are categorized to handle different social data, and an optimal combination strategy is provided for feature representations. Moreover, a uniform representation of hierarchy is proposed to learn temporal information, and a concatenated version of encodings is employed to capture feature-wise positions better. Experimental results on various social datasets demonstrate that three variants of our proposed framework outperform multiple state-of-art baselines by a large margin in terms of accuracy and distance error metrics.