Supporting Human Memory by Reconstructing Personal Episodic Narratives from Digital Traces


  • Varvara Kalokyri Rutgers University
  • Alexander Borgida Rutgers University
  • Amelie Marian Rutgers University



Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health


Numerous applications capture in digital form aspects of people’s lives. The resulting data, which we call Personal Digital Traces - PDTs, can be used to help reconstruct people’s episodic memories and connect to their past personal events. This may have several applications, from helping the recall of patients with neurodegenerative diseases to gathering clues from multiple sources to identify recent contacts and places visited – a critical new application for the recent health crisis. This paper takes steps towards integrating, connecting and summarizing the heterogeneous collection of data into episodic narratives using scripts – prototypical plans for everyday activities. Specifically, we propose a matching algorithm that groups PDTs from many different sources into script instances (episodes), and we provide a technique for ranking the likelihood of candidate episodes. We report on the results of a study based on the personal data of real users, which gives evidence that our episode reconstruction 1) integrates well PDTs from different sources into coherent episodes, and 2) augments users’ memory of their past actions.




How to Cite

Kalokyri, V., Borgida, A., & Marian, A. (2022). Supporting Human Memory by Reconstructing Personal Episodic Narratives from Digital Traces. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 453-464.