On the use of distributed semantics of tweet metadata for user age prediction

Social media data represent an important resource for behavioral analysis of the aging 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 baseline models, yields an improvement of up to 12.3% for Dutch dataset, 9.8% for English1 dataset, and 6.6% for English2 dataset in the micro-averaged F1 score.

Pandya Abhinay, Oussalah Mourad, Monachesi Paola, Kostakos Panos

Publication type:
A1 Journal article – refereed

Place of publication:

Age prediction, Convolutional neural networks, Social media mining, Twitter


Full citation:
Abhinay Pandya, Mourad Oussalah, Paola Monachesi, Panos Kostakos, On the use of distributed semantics of tweet metadata for user age prediction, Future Generation Computer Systems, Volume 102, 2020, Pages 437-452, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.08.018


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