Ioana R. Podină

Also published as: Ioana R. Podina


Natural language processing as a tool to identify the Reddit particularities of cancer survivors around the time of diagnosis and remission: A pilot study
Ioana R. Podină | Ana-Maria Bucur | Diana Todea | Liviu Fodor | Andreea Luca | Liviu P. Dinu | Rareș Boian
Proceedings of the Fifth Workshop on Widening Natural Language Processing

In the current study, we analyzed 15297 texts from 39 cancer survivors who posted or commented on Reddit in order to detect the language particularities of cancer survivors from online discourse. We performed a computational linguistic analysis (part-of-speech analysis, emoji detection, sentiment analysis) on submissions around the time of the cancer diagnosis and around the time of remission. We found several significant differences in the texts posted around the time of remission compared to those around the time of diagnosis. Though our results need to be backed up by a higher corpus of data, they do cue to the fact that cancer survivors, around the time of remission, focus more on others, are more active on social media, and do not see the glass as half empty as suggested by the valence of the emojis.

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media
Ana-Maria Bucur | Ioana R. Podina | Liviu P. Dinu
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

In this work, we provide an extensive part-of-speech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to be self-focused, more preoccupied with themselves and ruminate more about their lives and emotions. Our work aims to make use of large-scale datasets and computational methods for a quantitative exploration of discourse. We use the publicly available depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extract part-of-speech features and several indices based on them. Our results reveal statistically significant differences between the depressed and non-depressed individuals confirming findings from the existing psychology literature. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.