Forecasting of Twitter Hashtahg Temporal Dynamics Using Locally Weighted Projection Regression

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dc.authoridAlsaadi, Husam/0000-0003-0369-6720
dc.contributor.authorAlsaadi, Husam Ibrahiem
dc.contributor.authorAlmajmaie, Layth Kamil
dc.contributor.authorMahmood, Wisam Ali
dc.date.accessioned2025-02-06T17:58:27Z
dc.date.available2025-02-06T17:58:27Z
dc.date.issued2017
dc.departmentAltınbaş Üniversitesien_US
dc.descriptionInternational Conference on Engineering and Technology (ICET) -- AUG 21-23, 2017 -- Akdeniz Univ, Antalya, TURKEYen_US
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 meta-parameter 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.en_US
dc.description.sponsorshipIARES,IEEEen_US
dc.identifier.isbn978-1-5386-1949-0
dc.identifier.issn2380-9345
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5258
dc.identifier.wosWOS:000454987100028
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 International Conference on Engineering and Technology (Icet)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250206
dc.subjectHashtagen_US
dc.subjectTwitteren_US
dc.subjectMachine Learningen_US
dc.subjectSupervised Learningen_US
dc.subjectLocally Weighted Projection Regressionen_US
dc.subjectTime Series Forecastingen_US
dc.titleForecasting of Twitter Hashtahg Temporal Dynamics Using Locally Weighted Projection Regressionen_US
dc.typeConference Objecten_US

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