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Age Prediction of Social Media Users: Case Study on Robots in Hospitality

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Robot Intelligence Technology and Applications 7 (RiTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 642))

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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Notes

  1. 1.

    https://pytorch.org/hub/huggingface_pytorch-transformers/.

  2. 2.

    https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html.

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Author information

Authors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Brisbane, Australia

    Jinyuan Chen & Bela Stantic

  2. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Jinyan Chen

Authors
  1. Jinyuan Chen
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  2. Bela Stantic
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  3. Jinyan Chen
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Corresponding author

Correspondence to Bela Stantic .

Editor information

Editors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Southport, Australia

    Jun Jo

  2. Department of Aerospace Engineering, KAIST, Daejeon, Korea (Republic of)

    Han-Lim Choi

  3. School of Information and Communication Technology, Griffith University, Southport, Australia

    Marde Helbig

  4. Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea (Republic of)

    Hyondong Oh

  5. Department of Mechanical Engineering, KAIST, Daejeon, Korea (Republic of)

    Jemin Hwangbo

  6. Department of Mechanical Engineering, KAIST, Daejeon, Korea (Republic of)

    Chang-Hun Lee

  7. School of Information and Communication Technology, Griffith University, Southport, Australia

    Bela Stantic

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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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