# Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration

arXiv: https://arxiv.org/abs/2106.13411 · HTML: https://mlsanity.com/paper/2106.13411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Sentence-State LSTM for Text Representation](https://mlsanity.com/paper/1805.02474/) (2018)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [Density Estimation for Geolocation via Convolutional Mixture Density Network](https://mlsanity.com/paper/1705.02750/) (2017)
- [City-level Geolocation of Tweets for Real-time Visual Analytics](https://mlsanity.com/paper/1910.02213/) (2019)
- [Multiview Deep Learning for Predicting Twitter Users' Location](https://mlsanity.com/paper/1712.08091/) (2017)

## Built on by (most cited first, 1 of 1)

- [Multi-modal Representation Learning for Social Post Location Inference](https://mlsanity.com/paper/2306.07935/) (2023)

## Abstract (arXiv)

Twitter is a useful resource to analyze peoples' opinions on various topics. Often these topics are correlated or associated with locations from where these Tweet posts are made. For example, restaurant owners may need to know where their target customers eat with respect to the sentiment of the posts made related to food, policy planners may need to analyze citizens' opinion on relevant issues such as crime, safety, congestion, etc. with respect to specific parts of the city, or county or state. As promising as this is, less than 1% of the crawled Tweet posts come with geolocation tags. That makes accurate prediction of Tweet posts for the non geo-tagged tweets very critical to analyze data in various domains. In this research, we utilized millions of Twitter posts and end-users domain expertise to build a set of deep neural network models using natural language processing (NLP) techniques, that predicts the geolocation of non geo-tagged Tweet posts at various level of granularities such as neighborhood, zipcode, and longitude with latitudes. With multiple neural architecture experiments, and a collaborative human-machine workflow design, our ongoing work on geolocation detection shows promising results that empower end-users to correlate relationship between variables of choice with the location information.