Almog Simchon


2023

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You Are What You Read: Inferring Personality From Consumed Textual Content
Adam Sutton | Almog Simchon | Matthew Edwards | Stephan Lewandowsky
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

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.

2021

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Using Psychologically-Informed Priors for Suicide Prediction in the CLPsych 2021 Shared Task
Avi Gamoran | Yonatan Kaplan | Almog Simchon | Michael Gilead
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

This paper describes our approach to the CLPsych 2021 Shared Task, in which we aimed to predict suicide attempts based on Twitter feed data. We addressed this challenge by emphasizing reliance on prior domain knowledge. We engineered novel theory-driven features, and integrated prior knowledge with empirical evidence in a principled manner using Bayesian modeling. While this theory-guided approach increases bias and lowers accuracy on the training set, it was successful in preventing over-fitting. The models provided reasonable classification accuracy on unseen test data (0.68<=AUC<= 0.84). Our approach may be particularly useful in prediction tasks trained on a relatively small data set.

2018

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A Psychologically Informed Approach to CLPsych Shared Task 2018
Almog Simchon | Michael Gilead
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

This paper describes our approach to the CLPsych 2018 Shared Task, in which we attempted to predict cross-sectional psychological health at age 11 and future psychological distress based on childhood essays. We attempted several modeling approaches and observed best cross-validated prediction accuracy with relatively simple models based on psychological theory. The models provided reasonable predictions in most outcomes. Notably, our model was especially successful in predicting out-of-sample psychological distress (across people and across time) at age 50.