EventsThe 6th International Electronic Conference on Foods
Published
This submission belongs to the session B. Nutritional and Functional Foods of the event The 6th International Electronic Conference on Foods
Published date
27 Oct, 2025
Academic Editor
author-avatarManuel Viuda-Martos
Citation
Christos Gatsios, Malamatenia Panagiotou, Efstathios Kaloudis, Konstantinos Gkatzionis, Sofia Vasileiadou, Unveiling Popularity Shifts in Natural Nootropics: A Time Series Clustering of Reddit Discussions, in Proceedings of The 6th International Electronic Conference on Foods, 28 October–30 October 2025, MDPI: Basel, Switzerland
Share
Email
Facebook
Twitter
LinkedIn

Unveiling Popularity Shifts in Natural Nootropics: A Time Series Clustering of Reddit Discussions

Sofia Vasileiadou 2
Malamatenia Panagiotou 2
image
1. Computer Simulation, Genomics and Data Analysis Laboratory, Department of Food Science and Nutrition, University of the Aegean, Metropolite Ioakeim 2, 81400 Myrina, Lemnos, Greece, Greece
2. Laboratory of Consumer and Sensory Perception of Food & Drinks, Department of Food Science and Nutrition, University of the Aegean, Metropolite Ioakeim 2, 81400 Myrina, Lemnos, Greece, Greece
Abstract

The increasing popularity of natural nootropics (substances believed to enhance cognitive function) is often reflected in online discussions. Reddit, particularly its r/Nutrition community, offers a rich, time-resolved dataset for exploring these evolving interests. In this study, we analyzed approximately 10 years of posts from r/Nutrition (sourced via academictorrents.com), extracting the 15,000 most frequent words. Using the MiniLM-L6-v2 language model for semantic filtering, we isolated a curated set of 150 food-related nootropic terms. From these, we selected the top 40 most frequent, which collectively account for over 99% of total nootropic term frequency. For each term, we constructed a monthly time series normalized by the number of active users per month, ensuring that trends reflect genuine relative interest. Each time series was then standardized using mean-variance scaling to allow shape-based comparisons. To identify groups of terms with similar temporal dynamics, we applied the TimeSeriesKMeans algorithm (from the tslearn library), using Dynamic Time Warping (DTW) as the distance metric to accommodate non-linear shifts and misalignments. The results identified four clusters containing 5, 8, 10, and 17 terms, respectively, each with distinct temporal patterns. Cluster 1 showed a broad peak of interest between 2016 and 2018; Cluster 3 peaked around 2017 before declining; Cluster 0 remained stable with minor fluctuations; and Cluster 2 showed a rising trend after 2021, indicating emerging topics. Semantic analysis showed that all clusters were (to some extent) relevant to cognitive performance and food-related enhancers (functional foods, supplements, etc.). Semantic coherence varied. Each cluster had a different temporal usage pattern, but all four in fact depicted what seems to be a general pattern: scientific research focuses on certain foods/substances, which causes an increase in consumers’ interest and an explosion in the market (marketing, availability, and sales), leading to skepticism and critical reassessment and, thus, a drop in interest over time.

Keywords
natural nootropics
emerging food trends
time series analysis
reddit
social media
semantic filtering
Poster
FOODS 2025 - Poster - Gatsios.pdf
From Waste to Wealth: Valorizing Pomegranate Seeds into Bioactive Oil by Microwave and Soxhlet Extraction
Convergence or divergence: a comparative analysis of scientific and traditional cassava processing practices and their implications for the uptake of agro-processing technologies