Statistical Models to Predict Popularity of News Articles on Social Networks

Author

Ziyi Liu, Washington University in St. LouisFollow

Date of Award

Spring 5-2017

Author's School

Graduate School of Arts and Sciences

Author's Department

Mathematics

Additional Affiliations

Statistics

Degree Name

Master of Arts (AM/MA)

Degree Type

Thesis

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.

Language

English (en)

Chair and Committee

Todd Kuffner

Committee Members

Nan Lin, Jimin Ding

Comments

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

Recommended Citation

Liu, Ziyi, "Statistical Models to Predict Popularity of News Articles on Social Networks" (2017). Arts & Sciences Electronic Theses and Dissertations. 1052.
https://openscholarship.wustl.edu/art_sci_etds/1052