Forecasting of Twitter hashtag temporal dynamics using locally weighted projection regression

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dc.contributor.authorAlsaadi, Husam Ibrahiem
dc.contributor.authorAlmajmaie, Layth Kamil
dc.contributor.authorMahmood, Wisam Ali
dc.date.accessioned2021-05-15T12:50:06Z
dc.date.available2021-05-15T12:50:06Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionAkdeniz University;IARES;IEEE
dc.description2017 International Conference on Engineering and Technology, ICET 2017 -- 21 August 2017 through 23 August 2017 -- -- 135161
dc.description.abstractPopularity of social networks opens great opportunities for market such as advertisement. Using hashtags increasingly used in twits helps us to realize popular topics on the internet. Since most of new hashtags become popular and then fade away quickly, there is a limited time to predict the trend. Therefore, this paper proposes a fast incremental method to forecast the rate of the used hashtags in hour like time series. Two main parts for forecasting system are applied Preprocessing and Supervised Learning. Normalization is one of most popular preprocessing of dataset also proposed to have larger dataset. Moreover, the efficiency of the system under changing number of input (number of past hours from hashtag history) and output (number of next hours which is going to be predicted) are evaluated. Locally Weighted Projection Regression as one of the most powerful machine learning methods with no metaparameter are applied in this paper as real-Time learning method. The performance of the system is verified by implementation of 'Volume Time Series of Memetracker Phrases and Twitter Hashtags'. The results show that the errors of forecasting system are good enough to understand the trend of the hashtag. © 2017 IEEE.en_US
dc.identifier.doi10.1109/ICEngTechnol.2017.8308166
dc.identifier.endpage4en_US
dc.identifier.isbn9781538619490
dc.identifier.scopus2-s2.0-85047739928
dc.identifier.scopusqualityN/A
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/ICEngTechnol.2017.8308166
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1183
dc.identifier.volume2018-Januaryen_US
dc.indekslendigikaynakScopus
dc.institutionauthorAlsaadi, Husam Ibrahiem
dc.institutionauthorAlmajmaie, Layth Kamil
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of 2017 International Conference on Engineering and Technology, ICET 2017
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHashtagen_US
dc.subjectLocally Weighted Projection Regressionen_US
dc.subjectMachine Learningen_US
dc.subjectSupervised Learningen_US
dc.subjectTime Series Forecastingen_US
dc.subjectTwitteren_US
dc.titleForecasting of Twitter hashtag temporal dynamics using locally weighted projection regression
dc.typeConference Object

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