2013/11/05 by Dániel Kondor, István Csabai, Kondor, Dániel +15
Computer Science · Social Sciences · #China's Ethnic Minorities and Relations #Computation and Language (cs.CL) #FOS: Computer and information sciences #Linguistic Variation and Morphology #cs.CL
paper · pdf · doi:10.48550/arxiv.1311.1169
arxiv created 2013/11/05 · openalex publication_date 2013/11/05 · arxiv updated 2013/11/06 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
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.