Detecting trends in twitter time series

Tijl De Bie, Jefrey Lijffijt, Cedric Mesnage, Raul Santos-Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)
307 Downloads (Pure)

Abstract

Detecting underlying trends in time series is important in many settings, such as market analysis (stocks, social media coverage) and system monitoring (production facilities, networks). Although many properties of the trends are common across different domains, others are domain-specific. In particular, modelling human activities such as their behaviour on social media, often leads to sharply defined events separated by periods without events. This paper is motivated by time series representing the number of tweets per day addressed to a specific Twitter user. Such time series are characterized by the combination of (1) an underlying trend, (2) concentrated bursts of activity that can be arbitrarily large, often attributable to an event, e.g., a tweet that goes viral or a realworld event, and (3) random fluctuations/noise. We present a new probabilistic model that accurately models such time series in terms of peaks on top of a piece-wise exponential trend. Fitting this model can be done by solving an efficient convex optimization problem. As an empirical validation of the approach, we illustrate how this model performs on a set of Twitter time series, each one addressing a particular music artist, which we manually annotated with events as a reference.
Original languageEnglish
Title of host publication2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP 2016)
Subtitle of host publicationProceedings of a meeting held 13-16 September 2016, Vietri sul Mare (Salerno), Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages244-249
Number of pages6
ISBN (Electronic)9781509007462
ISBN (Print)9781509007479
DOIs
Publication statusPublished - Dec 2016
EventMachine Learning for Signal Processing - Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameProceedings of the International Workshop on Machine Learning for Signal Processing (MLSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1551-2541

Conference

ConferenceMachine Learning for Signal Processing
Abbreviated titleMLSP
CountryItaly
CitySalerno
Period13/09/1616/09/16

Keywords

  • Trend detection
  • time series
  • convexity

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