A. Pandya, M. Oussalah, P. Monachesi, P. Kostakos and L. Lovén, "On the Use of URLs and Hashtags in Age Prediction of Twitter Users," 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 62-69. doi: 10.1109/IRI.2018.00017
On the use of URLs and hashtags in age prediction of Twitter users
|Author:||Pandya, Abhinay1; Oussalah, Mourad1; Monachesi, Paola2;|
1Center for Ubiquitous Computing, University of Oulu, Finland
2University of Utrecht, Netherlands
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018112849416
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2018-11-28
Social media data represent an important resource for behavioral analysis of the ageing population. This paper addresses the problem of age prediction from Twitter dataset, where the prediction issue is viewed as a classification task. For this purpose, an innovative model based on Convolutional Neural Network is devised. To this end, we rely on language-related features and social media specific metadata. More specifically, we introduce two features that have not been previously considered in the literature: the content of URLs and hashtags appearing in tweets. We also employ distributed representations of words and phrases present in tweets, hashtags and URLs, pre-trained on appropriate corpora in order to exploit their semantic information in age prediction. We show that our CNN-based classifier, when compared with an SVM baseline model, yields an improvement of 12.3% and 6.6% in the micro-averaged F1 score on the Dutch and English datasets, respectively.
|Pages:||62 - 69|
2018 IEEE International Conference on Information Reuse and Integration (IRI), 7–9 July 2018, Salt Lake City, Utah, USA
IEEE International Conference on Information Reuse and Integration
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This work is partially supported by EU Marie Skodowska-Curie grant No 645706 and EU grant 770469-Cutler. This paper is based on results of a project that has received funding from the European Unions Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant agreement No 645706.
|EU Grant Number:||
(645706) GRAGE - Grey and green in Europe: elderly living in urban areas
(770469) CUTLER - Coastal Urban developmenT through the LEnses of Resiliency
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