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Religious Politicians and Creative Photographers: Automatic User Categorization in Twitter
[Konferenzbeitrag]
Körperschaftlicher Herausgeber
IEEE Computer Society
Abstract Finding the ''right people'' is a central aspect of social media systems. Twitter has millions of users who have varied interests, professions and personalities. For those in fields such as advertising and marketing, it is important to identify certain characteristics of users to target. However, Tw... mehr
Finding the ''right people'' is a central aspect of social media systems. Twitter has millions of users who have varied interests, professions and personalities. For those in fields such as advertising and marketing, it is important to identify certain characteristics of users to target. However, Twitter users do not generally provide sufficient information about themselves on their profile which makes this task difficult. In response, this work sets out to automatically infer professions (e.g., musicians, health sector workers, technicians) and personality related attributes (e.g., creative, innovative, funny) for Twitter users based on features extracted from their content, their interaction networks, attributes of their friends and their activity patterns. We develop a comprehensive set of latent features that are then employed to perform efficient classification of users along these two dimensions (profession and personality). Our experiments on a large sample of Twitter users demonstrate both a high overall accuracy in detecting profession and personality related attributes as well as highlighting the benefits and pitfalls of various types of features for particular categories of users.... weniger
Thesaurusschlagwörter
Beruf; Klassifikation; Persönlichkeit; Benutzer; Twitter; Soziale Medien
Klassifikation
interaktive, elektronische Medien
Freie Schlagwörter
user profiling
Titel Sammelwerk, Herausgeber- oder Konferenzband
SocialCom '13: Proceedings of the 2013 International Conference on Social Computing
Konferenz
SocialCom 2013 - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013. Washington, D.C., 2013
Sprache Dokument
Englisch
Publikationsjahr
2013
Erscheinungsort
Piscataway, NJ
Seitenangabe
S. 303-310
DOI
https://doi.org/10.1109/SocialCom.2013.49
ISBN
978-0-7695-5137-1
Status
Postprint; begutachtet (peer reviewed)
Lizenz
Deposit Licence - Keine Weiterverbreitung, keine Bearbeitung