@inproceedings{lynn-etal-2020-hierarchical,
    title = "Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention",
    author = "Lynn, Veronica  and
      Balasubramanian, Niranjan  and
      Schwartz, H. Andrew",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-main.472/",
    doi = "10.18653/v1/2020.acl-main.472",
    pages = "5306--5316",
    abstract = "Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users' social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5{\%}). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work."
}