Exploring Time Series Spectral Features in Viral Hashtags Prediction

dc.contributor.author Doong, Shing
dc.contributor.author Chung, Daniel
dc.date.accessioned 2016-12-29T00:46:16Z
dc.date.available 2016-12-29T00:46:16Z
dc.date.issued 2017-01-04
dc.description.abstract Viral hashtags spread across a large population of Internet users very quickly. Previous studies use features mostly in an aggregate sense to predict the popularity of hashtags, for example, the total number of hyperlinks in early tweets adopting a tag. Since each tweet is time stamped, many aggregate features can be decomposed into fine-grained time series such as a series of numbers of hyperlinks in early adopting tweets. This research utilizes frequency domain tools to analyze these time series. In particular, we apply scalogram analysis to study the series of adoption time lapses and the series of mentions and hyperlinks in early adopting tweets. Besides continuous wavelet transforms (CWTs), we also use fast wavelet transforms (FWTs) to analyze the time series. Through experiments with two sets of tweets collected in different seasons, out-of-sample cross validations show that wavelet spectral features can generally improve the prediction performance, and discrete FWT yields results as good as the more complicated CWT-based methods with scalogram analysis.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.227
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41381
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Hashtag popularity
dc.subject spectral feature
dc.subject time series
dc.subject Twitter
dc.subject wavelet transform
dc.title Exploring Time Series Spectral Features in Viral Hashtags Prediction
dc.type Conference Paper
dc.type.dcmi Text
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