On the Use of URLs and Hashtags in Age Prediction of Twitter Users

Abhinay Pandya, Mourad Oussalah, P. Monachesi, Panos Kostakos, Lauri Loven

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Information Reuse and Integration (IRI) (2018)
PublisherIEEE
DOIs
Publication statusPublished - 2018

Keywords

  • Twitter
  • Tagging
  • Feature extraction
  • Metadata
  • Semantics
  • Task analysis

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