Prediction of Online News Popularity using ANN Deep Learning
Authors: 
Kawser Irom Rushee, Enan Ajmain, Md. Moshavvir Hossain Captain, Md. Talha Jubayer, Wahidul IslamAuthors Info & Claims
Kawser Irom Rushee, Enan Ajmain, Md. Moshavvir Hossain Captain, Md. Talha Jubayer, Wahidul IslamAuthors Info & ClaimsPages 415 - 419
Abstract
Online news is incredibly popular these days because of the growth of the Internet and web expansion. At the same time, it is dynamic and chaotic on many levels, thus it gives an interesting research opportunity for the prediction of online news popularity. ANN (Artificial Neural Network) was used in this paper on a online news popularity based dataset. The goal was to increase prediction accuracy using deep learning. Dataset was preprocessed to use for a multiclass classification. The model was created with appropriate features needed and it produced more than 96 percent accuracy. Moreover, the false negative value of each multiclass was very low and precision, recall, and f1 score was high in our proposed model. All the results were discussed for the prediction model. This can help the online news authors to increase their news popularity.
References
[1]
Trends and Facts on Newspapers. Retrieved from https://www.pewresearch.org/journalism/fact-sheet/newspapers/.
[2]
Kelwin Fernandes, Pedro Vinagre and Paulo Cortez, 2015. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence, September, Coimbra.
[3]
Md. Uddin, Muhammed Patwary, Tanveer Ahsan and Mohammed Alam, 2016. Predicting the popularity of online news from content metadata. 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET).
[4]
Tom Smedt, Lucas Nijs and Walter Daelemans, 2014. Creative Web Services with Pattern. In: Proceedings of the 5th International Conference on Computational Creativity (ICCC 2014). [online] Available at: <http://computationalcreativity.net/iccc2014/wp-content/uploads/2014/06//13.5_DeSmedt.pdf>[Accessed 31 December 2021].
[5]
Mohamed Ahmed, Stella Spagna, Felipe Huici and Saverio Niccolini, 2013. A peek into the future. Proceedings of the sixth ACM international conference on Web search and data mining – WSDM.
[6]
Zemin Bao, Yun Liu, Zhenjiang Zhang, Hui Liu and Jun-Jun Cheng, 2019. Predicting popularity via a generative model with adaptive peeking window. Physica A: Statistical Mechanics and its Applications, 522, pp.54-68.
[7]
Gabor Szabo, Bernardo Huberman, 2010. Predicting the popularity of online content. Communications of the ACM, 53(8), pp.80-88.
[8]
Stefan Siersdorfer, Jose Pedro and Mark Sanderson, 2009. Automatic video tagging using content redundancy. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '.
[9]
Dheeru Dua and Casey Graf, 2017. UCI Machine Learning Repository: Citation Policy.Irvine, CA: University of California, School of Information and Computer Science.Archive.ics.uci.edu. Available at: <http://archive.ics.uci.edu/ml>[Accessed 31 December 2021].
[10]
sklearn.preprocessing.LabelEncoder-scikit-learn1.0.2. Retrieved from http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
[11]
sklearn.preprocessing.StandardScaler-scikit-learn1.0.2. Retrieved from http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
[12]
About Keras .Retrieved from keras.io/about
[13]
The Sequential Model Retrieved from https://keras.io/guides/sequential_model
[14]
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems https://arxiv.org/abs/1603.04467.
Index Terms
- Prediction of Online News Popularity using ANN Deep Learning
Recommendations
Predicting online news popularity based on machine learning
Research highlights- A UCI online news popularity dataset was utilized in this study, and the quality of the dataset was improved after using data pre-processing methods ...
AbstractDue to its fast transmission and easy accessibility features, the Internet has replaced traditional newspapers and magazines as the main channel for delivering public news. Hence, predicting the popularity of Internet news has become ...
Graphical abstractOne-class SVM algorithm based on an autoencoder adopted in this study outperforms other algorithms, namely Random Forest, XGBoost, and LightGBM, in the category of the accuracy, the precision, the recall, and F1 scores.
...Modeling and predicting the popularity of online news based on temporal and content-related features
As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest ...
Stock prediction using deep learning
Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging ...
Comments
Information & Contributors
Information
Published In
March 2022
543 pages
ISBN:9781450397346
DOI:10.1145/3542954
Copyright © 2022 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]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 August 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCA 2022
ICCA 2022: 2nd International Conference on Computing Advancements
March 10 - 12, 2022
Dhaka, Bangladesh
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 65Total Downloads
- Downloads (Last 12 months)18
- Downloads (Last 6 weeks)2
Reflects downloads up to 28 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format