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

Kristoffer L. Nielbo*, Peter B. Vahlstrup, Anja Bechmann, Jianbo Gao

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisKonferenceartikelForskningpeer review

2 Citationer (Scopus)
294 Downloads (Pure)

Abstract

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.

OriginalsprogEngelsk
TidsskriftCEUR Workshop Proceedings
Vol/bind2723
Sider (fra-til)290-297
Antal sider8
ISSN1613-0073
StatusUdgivet - 2020
Begivenhed1st Workshop on Computational Humanities Research, CHR 2020 - Virtual, Amsterdam, Holland
Varighed: 18 nov. 202020 nov. 2020

Konference

Konference1st Workshop on Computational Humanities Research, CHR 2020
Land/OmrådeHolland
ByVirtual, Amsterdam
Periode18/11/202020/11/2020

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