Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention

Veronica Lynn, Niranjan Balasubramanian, H. Andrew Schwartz

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Computational Social Science and Social Media Long Paper

Session 9B: Jul 7 (18:00-19:00 GMT)
Session 10A: Jul 7 (20:00-21:00 GMT)
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.
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