Trend reservoir detection: Minimal persistence and resonant behavior of trends in social media

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Sociocultural trends from social media platforms such as Twitter or Instagram have become an important part of knowledge discovery. The 'trend' construct is however ambiguous and its estimation from unstructured sociocultural data complicated by several methodological issues. This paper presents an approach to trend estimation that combines domain knowledge of social media with advances in information theory and dynamical systems. In particular, we show how trend reservoirs (i.e., signals that display trend potential) can be identified by their relationship between novel and resonant behavior, and their minimal persistence.This approach contrasts with trend estimation that relies on linear or polynomial techniques to study point-like novelty behavior in social media, and it completes approaches that rely on smooth functions of time.

Original languageEnglish
JournalCEUR Workshop Proceedings
Pages (from-to)290-297
Number of pages8
Publication statusPublished - 2020
Event1st Workshop on Computational Humanities Research, CHR 2020 - Virtual, Amsterdam, Netherlands
Duration: 18 Nov 202020 Nov 2020


Conference1st Workshop on Computational Humanities Research, CHR 2020
CityVirtual, Amsterdam

Bibliographical note

Publisher Copyright:
© 2020 Copyright for this paper by its authors.

Copyright 2020 Elsevier B.V., All rights reserved.

    Research areas

  • Fractal analysis, Information dynamics, Social media, Trend detection

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