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Sanja Šćepanović

4 papers in the library · 1 citation · publishing 2022-2025

Papers

Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach

arXiv (Cornell University) July 9, 2023 Anubhab Das, Sanja Šćepanović, Luca Maria Aiello et al. 1 citation

A new data-driven method using natural language processing identifies 217 dream topics grouped into 22 larger themes from 44,213 dream reports posted on Reddit's r/Dreams subreddit. The approach overcomes limitations of traditional dream analysis, which relies on retrospective surveys and lab studies with over 130 scales. The topics were validated against the Hall and van de Castle scale. The method can detect unique patterns in nightmares or recurring dreams, assess topic importance and connections, and track changes in collective dream content over time, including shifts during the COVID-19 pandemic and the Russo-Ukrainian war.

Dream content discovery from social media using natural language processing

EPJ Data Science May 23, 2025 Anubhab Das, Sanja Šćepanović, Luca Maria Aiello et al.

Dreams remain a poorly understood aspect of human life. Traditional methods for analyzing dream content rely on over 130 rating scales and are limited by small samples and retrospective surveys. To address these limitations, researchers used natural language processing to identify topics in 44,213 dream reports from Reddit's r/Dreams subreddit. The analysis uncovered 217 topics grouped into 22 broader themes—the largest collection of dream topics to date. The topics were validated against the established Hall and van de Castle scale.

Epidemic Dreams: Dreaming about health during the COVID-19 pandemic

arXiv Preprint Archive February 2, 2022 Sanja Šćepanović, Luca Maria Aiello, Deirdre Barrett et al.

During the COVID-19 pandemic, dream content reflected waking health concerns, supporting the continuity hypothesis. A deep-learning algorithm extracted medical mentions from 2,888 dream reports and 57 million pandemic-related tweets. Common health expressions in both sets were typical COVID-19 symptoms (cough, fever, anxiety). Distinct expressions revealed different thought processes: waking life described realistic symptoms and disorders (nasal pain, SARS, H1N1), while dreaming life featured surreal or unrelated conditions (maggots, deformities, teeth falling out). The findings suggest dream reports are an underutilized source of real-world health experiences.

Dream Content Discovery from Social Media Using AI

Anubhab Das, Sanja Šćepanović, Luca Maria Aiello et al.

A new data-driven method using natural language processing identified 217 dream topics grouped into 22 larger themes from 44,213 dream reports posted on Reddit's r/dreams subreddit, the most extensive collection of dream topics to date. The topics were validated against the widely-used Hall and van de Castle scale. The method revealed unique patterns in different dream types, such as nightmares and recurring dreams, and showed that indoor location settings were more predominant in Reddit dreams than previously stipulated. It also detected changes in collective dream experiences over time and around major events like the COVID-19 pandemic and the Russo-Ukrainian war.