Madisetty, Sreekanth and Desarkar, Maunendra Sankar
(2021)
Social Media Popularity Prediction of Planned Events Using Deep Learning.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12657.
pp. 320-326.
ISSN 0302-9743
Full text not available from this repository.
(
Request a copy)
Abstract
Early prediction of popularity is crucial for recommendation of planned events such as concerts, conferences, sports events, performing arts, etc. Estimation of the volume of social media discussions related to the event can be useful for this purpose. Most of the existing methods for social media popularity prediction focus on estimating tweet popularity i.e. predicting the number of retweets for a given tweet. There is less focus on predicting event popularity using social media. We focus on predicting the popularity of an event much before its start date. This type of early prediction can be helpful in event recommendation systems, assisting event organizers for better planning, dynamic ticket pricing, etc. We propose a deep learning based model to predict the social media popularity of an event. We also incorporate an extra feature indicating how many days left to the event start date to improve the performance. Experimental results show that our proposed deep learning based approach outperforms the baseline methods.
Actions (login required)
 |
View Item |
Altmetric
Altmetric