Abstract
Social media has gained popularity and we witness a vast volume of publicly available social media posts where people are commenting on different topics. This discussion contains a lot of valuable information deeply hidden inside the data and its metadata, which can be valuable for different stockholders. To extract this information different methods have been proposed in the literature and methods relied on different aspects of data and were based on diverse techniques such as text mining, machine and deep learning, predictive analytics, and natural language processing. This work proposes a method that relies on transformer-based architectures and it is based on supervised machine learning that predicts the age indirectly hidden in the description users provided in their profiles. To test the accuracy of the proposed method the case study of robots acceptance in hospitality has been considered. Relevant posts from social media Twitter have been collected and the proposed model tested. Results from extensive experimental evaluation demonstrate the suitability of the proposed method achieving high accuracy of age prediction, to the extent of 82% on test data. To demonstrate the usability and value of predicting the age of social media users we calculate the emotions as well as sentiment in posts and investigate the acceptance of robots in hospitality for different age groups.
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Chen, J., Stantic, B., Chen, J. (2023). Age Prediction of Social Media Users: Case Study on Robots in Hospitality. In: Jo, J., et al. Robot Intelligence Technology and Applications 7. RiTA 2022. Lecture Notes in Networks and Systems, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-031-26889-2_39
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DOI: https://doi.org/10.1007/978-3-031-26889-2_39
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