# Using Robust PCA to estimate regional characteristics of language use from geo-tagged Twitter messages

arXiv: https://arxiv.org/abs/1311.1169 · HTML: https://mlsanity.com/paper/1311.1169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2013.

## Built on by (most cited first, 2 of 2)

- [Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology](https://mlsanity.com/paper/2104.01306/) (2021)
- [Representations of Language Varieties Are Reliable Given Corpus Similarity Measures](https://mlsanity.com/paper/2104.01294/) (2021)

## Abstract (arXiv)

Principal component analysis (PCA) and related techniques have been successfully employed in natural language processing. Text mining applications in the age of the online social media (OSM) face new challenges due to properties specific to these use cases (e.g. spelling issues specific to texts posted by users, the presence of spammers and bots, service announcements, etc.). In this paper, we employ a Robust PCA technique to separate typical outliers and highly localized topics from the low-dimensional structure present in language use in online social networks. Our focus is on identifying geospatial features among the messages posted by the users of the Twitter microblogging service. Using a dataset which consists of over 200 million geolocated tweets collected over the course of a year, we investigate whether the information present in word usage frequencies can be used to identify regional features of language use and topics of interest. Using the PCA pursuit method, we are able to identify important low-dimensional features, which constitute smoothly varying functions of the geographic location.