%0 Conference Proceedings
%T Machine Learning Approach to Analyze and Predict the Popularity of Tweets with Images
%+ Department of Management Studies [Delhi]
%A Joseph, Nimish
%A Sultan, Amir
%A Kar, Arpan, Kumar
%A Vigneswara Ilavarasan, P.
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 17th Conference on e-Business, e-Services and e-Society (I3E)
%C Kuwait City, Kuwait
%Y Salah A. Al-Sharhan
%Y Antonis C. Simintiras
%Y Yogesh K. Dwivedi
%Y Marijn Janssen
%Y Matti Mäntymäki
%Y Luay Tahat
%Y Issam Moughrabi
%Y Taher M. Ali
%Y Nripendra P. Rana
%I Springer International Publishing
%3 Challenges and Opportunities in the Digital Era
%V LNCS-11195
%P 567-576
%8 2018-10-30
%D 2018
%R 10.1007/978-3-030-02131-3_49
%K Twitter image
%K Neural network
%K Random forest
%K Decision tree
%K Popularity
%K Machine learning
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Social Media platforms play a major role in spreading information. Twitter, is one such platform which is used by millions of people to share information every day. Twitter with the recent introduction of a feature that helps its users to attach images to a tweet has changed the dynamics of tweeting. Many people now prefer to tweet with images. This study tries to analyse and predict the popularity of such tweets. This study uses learning mechanisms like decision tree, neural networks and random forests to learn the tweets posted by people with a higher number of followers. Image parameters, network variables, transactional, and historical variables of a tweet are identified and are trained for predicting the test data. This study can help businesses to build better social media tools, which allows customers to tweet data at the right time. This study also identifies the contribution of various parameters that may help a tweet to go viral.
%G English
%Z TC 6
%Z WG 6.11
%2 https://inria.hal.science/hal-02274178/document
%2 https://inria.hal.science/hal-02274178/file/474698_1_En_49_Chapter.pdf
%L hal-02274178
%U https://inria.hal.science/hal-02274178
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-11
%~ IFIP-I3E
%~ IFIP-LNCS-11195