Statistical Models to Predict Popularity of News Articles on Social Networks

Author

Ziyi Liu, Washington University in St. LouisFollow

Abstract

Social networks have changed the way that we obtain information. Content creators and, specifically news article authors, have in interest in predicting the popularity of content, in terms of the number of shares, likes, and comments across various social media platforms. In this thesis, I employ several statistical learning methods for prediction. Both regression-based and classification-based methods are compared according to their predictive ability, using a database from the UCI Machine Learning Repository.

Committee Chair

Todd Kuffner

Committee Members

Nan Lin, Jimin Ding

Comments

Permanent URL: https://doi.org/10.7936/K7Z036MM

Degree

Master of Arts (AM/MA)

Author's Department

Mathematics

Author's School

Graduate School of Arts and Sciences

Document Type

Thesis

Date of Award

Spring 5-2017

Language

English (en)

DOI

https://doi.org/10.7936/K7Z036MM

Recommended Citation

Liu, Ziyi, "Statistical Models to Predict Popularity of News Articles on Social Networks" (2017). Arts & Sciences Theses and Dissertations. 1052.

The definitive version is available at https://doi.org/10.7936/K7Z036MM