@inproceedings{sutton-etal-2023-read,
    title = "You Are What You Read: Inferring Personality From Consumed Textual Content",
    author = "Sutton, Adam  and
      Simchon, Almog  and
      Edwards, Matthew  and
      Lewandowsky, Stephan",
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman",
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wassa-1.4",
    doi = "10.18653/v1/2023.wassa-1.4",
    pages = "28--38",
    abstract = "In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content ($N = 913$) or news content ($N = 213$). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of $r=.06$ between the model{'}s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as $r=.34$ when predicting personality from the text being read.",
}

In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content ($N = 913$) or news content ($N = 213$). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of $r=.06$ between the model{'}s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as $r=.34$ when predicting personality from the text being read.
Sutton, Adam Simchon, Almog Edwards, Matthew Lew owsky, Stephan
You Are What You Read: Inferring Personality From Consumed Textual Content