Inferring demographic data of marginalized users in Twitter with computer vision APIs |
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Author: | Kostakos, Panos1; Pandya, Abhinay1; Kyriakouli, Olga2; |
Organizations: |
1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland 2Dpt. of Informatics and Telematic,s Harokopio University Athens, Greece |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.5 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2019082024775 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2019
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Publish Date: | 2019-08-20 |
Description: |
AbstractInferring demographic intelligence from unlabeled social media data is an actively growing area of research, challenged by low availability of ground truth annotated training corpora. High-accuracy approaches for labeling demographic traits of social media users employ various heuristics that do not scale up and often discount non-English texts and marginalized users. First, we present a framework for inferring the demographic attributes of Twitter users from their profile pictures (avatars) using the Microsoft Azure Face API. Second, we measure the inter-rater agreement between annotations made using our framework against two pre-labeled samples of Twitter users (N1=1163; N2=659) whose age labels were manually annotated. Our results indicate that the strength of the inter-rater agreement (Gwet’s AC1=0.89; 0.90) between the gold standard and our approach is ‘very good’ for labelling the age group of users. The paper provides a use case of Computer Vision for enabling the development of large cross-sectional labeled datasets, and further advances novel solutions in the field of demographic inference from short social media texts. see all
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ISBN: | 978-1-5386-9400-8 |
ISBN Print: | 978-1-5386-9401-5 |
Pages: | 81 - 84 |
DOI: | 10.1109/EISIC.2018.00022 |
OADOI: | https://oadoi.org/10.1109/EISIC.2018.00022 |
Host publication: |
Proceedings of the European Intelligence and Security Informatics Conference (EISIC) 2018 October 24-25, 2018 Blekinge Institute of Technology, Karlskrona, Sweden |
Host publication editor: |
Brynielsson, Joel |
Conference: |
European Intelligence and Security Informatics Conference (EISIC) |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Funding: |
This work is (partially) funded by the European Commission grant 770469-CUTLER and 645706-GRAGE. |
EU Grant Number: |
(770469) CUTLER - Coastal Urban developmenT through the LEnses of Resiliency (645706) GRAGE - Grey and green in Europe: elderly living in urban areas |
Copyright information: |
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