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Predicting the Popularity of Trending Arabic News on Twitter

Authors:

Nourh Abdulaziz Rsheed,

Muhammad Badruddin KhanAuthors Info & Claims

MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

Pages 15 - 19

https://doi.org/10.1145/2668260.2668285

Published: 15 September 2014 Publication History

Abstract

Twitter is a popular form of social media that helps to spread breaking news. Mining textual documents and the number of posts ("tweets") can assist in predicting the popularity of news articles based on the content of news articles. The purpose of this study is to build a model that can predict the popularity of news articles on Twitter by classifying their features, providing comparisons using three algorithms for data mining: decision tree, NB, and rule based W-JRIP. Four approaches were used to compare the extracted features: light stemming, N-grams and light stemming, N-grams, and bag of words. The results of the experiment on the external features of articles indicate that the decision tree performed better (93.30%) than the rule-based and NB algorithms. The experiment on the internal features yielded no significant predictors of the popularity of news articles on Twitter. The results of the experiments on internal and external features of articles indicate that the decision tree algorithm with light stemming approaches performs best, achieving 85.60%. This study expands the limited literature on Arabic text classification and provides a predictive model that may help news organizations improve their online contents.

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Cited By

Alharbi AAljaedi A(2019)Predicting Rogue Content and Arabic Spammers on TwitterFuture Internet10.3390/fi1111022911:11(229)Online publication date: 30-Oct-2019
https://doi.org/10.3390/fi11110229
Meghawat MYadav SMahata DYin YShah RZimmermann R(2018)A Multimodal Approach to Predict Social Media Popularity2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00042(190-195)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00042
Li LSitu RGao JYang ZLiu WLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)A Hybrid Model Combining Convolutional Neural Network with XGBoost for Predicting Social Media PopularityProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3127902(1912-1917)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3127902
Show More Cited By

Index Terms

Predicting the Popularity of Trending Arabic News on Twitter
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Other conferences

MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

September 2014

225 pages

ISBN:9781450327671

DOI:10.1145/2668260

Conference Chairs:
Obaid Al Motairy
Qassim University, KSA
,
Richard Chbeir
University of Pau and Adour Countries, France
,
Mohammed Alodib
Qassim University, KSA
,
Lamri Laouamer
Qassim University, KSA
,
Youakim Badr
INSA De Lyon, France
,
Program Chairs:
Morad Benyoucef
University of Ottawa, Canada
,
Saad Harous
United Arab Emirates University
,
Ismail Biskri

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 15 September 2014

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MEDES '14

MEDES '14: The 6th International Conference on Management of Emergent Digital EcoSystems

September 15 - 17, 2014

Al Qassim, Buraidah, Saudi Arabia

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Overall Acceptance Rate 267 of 682 submissions, 39%

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Citations

Cited By

Alharbi AAljaedi A(2019)Predicting Rogue Content and Arabic Spammers on TwitterFuture Internet10.3390/fi1111022911:11(229)Online publication date: 30-Oct-2019
https://doi.org/10.3390/fi11110229
Meghawat MYadav SMahata DYin YShah RZimmermann R(2018)A Multimodal Approach to Predict Social Media Popularity2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00042(190-195)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00042
Li LSitu RGao JYang ZLiu WLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)A Hybrid Model Combining Convolutional Neural Network with XGBoost for Predicting Social Media PopularityProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3127902(1912-1917)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3127902
Abozinadah EJones J(2017)A Statistical Learning Approach to Detect Abusive Twitter AccountsProceedings of the International Conference on Compute and Data Analysis10.1145/3093241.3093281(6-13)Online publication date: 19-May-2017
https://dl.acm.org/doi/10.1145/3093241.3093281
Isabella KSampebatu LAlbarda I(2017)Analysis of earthquake magnitude level based on data Twitter with decision tree algorithm2017 International Conference on Information Technology Systems and Innovation (ICITSI)10.1109/ICITSI.2017.8267921(73-76)Online publication date: Oct-2017
https://doi.org/10.1109/ICITSI.2017.8267921
Abozinadah EMbaziira AJones J(2015)Detection of Abusive Accounts with Arabic TweetsInternational Journal of Knowledge Engineering-IACSIT10.7763/IJKE.2015.V1.191:2(113-119)Online publication date: 2015
https://doi.org/10.7763/IJKE.2015.V1.19

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