This is my class project using UCI Mashable dataset to determine what constitutes popular news. In this project, I used (1) multiple regression and model building and (2) PCA and factor analysis. Data Analytics Tools: SAS and R

1. Predicting News Popularity
CSC 424 Advanced Data Analysis and Regression
Ke Feng
07/03/2019

2. Introduction
 Mashable.com is a digital website
founded in 2005. It has now become
one of today’s most popular sources
to acquire information.

3. Dataset
 This dataset summarizes articles published by Mashable in a
period of two years. The data is publically available at University
of California Irvine Machine Learning Repository
 This original dataset has a total of 39644 observations and 61
variables. 58 of the variables will be used as predictors.
 The goal of this analysis is to predict news shares on social media
networks (popularity). My response variable is number of shares
on social media networks.

4. Literature Review
 Ding, C., & He, X. (2004). K-means clustering via principal component analysis. Twenty-first International Conference
on Machine Learning - ICML 04. doi:10.1145/1015330.1015408
 Heller, B. (1986). Statistics for experimenters, an introduction to design, data analysis, and model
building. Mathematical Modelling, 7(9-12), 1657-1658. doi:10.1016/0270-0255(86)90102-8
 Khuntia, J., Sun, H., & Yim, D. (2016). Sharing News Through Social Networks. International Journal on Media
Management, 18(1), 59-74. doi:10.1080/14241277.2016.1185429
 Hate Speech, Online and Social Media. (n.d.). Encyclopedia of Social Media and Politics.
doi:10.4135/9781452244723.n252
 Barthel, M. (2017, June 01). Despite subscription surges for largest U.S. newspapers, circulation and revenue fall for
industry overall. Retrieved from http://www.pewresearch.org/fact-tank/2017/06/01/circulation-and-revenue-fall-for-
newspaper-industry/
 Advertise With Mashable. (n.d.). Retrieved from https://mashable.com/advertise/
 Al-Zwainy, F. M., Abdulmajeed, M. H., & Aljumaily, H. S. (2013). Using Multivariable Linear Regression Technique for
Modeling Productivity Construction in Iraq. Open Journal of Civil Engineering, 03(03), 127-135.
doi:10.4236/ojce.2013.33015

5. Exploratory Stage: Clean and Explore the Data
 Check if there is any missing value
 Remove repetitive columns
 Check categorical variables
 Make a descriptive statistical
summary and check the structure
again
Categorical Variables Detection
No Missing ValueDescriptive Summary of Y-variable

6. Techniques
 Multiple Regression and Model Building
 PCA & Factor Analysis

7. Multi-regression and Model Building
 Check multicollinearity
 Split the data into 80% training and 20%
testing
 Use training set to do model
construction and use testing set to
predict value
 Model 1 has a R2 of 11.9% (TOO
LOW!)
 Automatic Model Selection (Stepwise &
Backward)
 Final Model
Data Partition (80%training+20%testing)
First Model Fitting Result

8. Result & Business Insights
 Parameter Estimate shows association
between Y and X-variables. Though not
causation, it shows association between Y
and Xs. Variables like data_channel_is_tech
and abs_title_subjectivity should be
highlighted.
 Insight
 Categorize articles in the right channel is
important. More tech articles may increase
the popularity.
 More subjectivity may increase popularity.
Personal views can boost traffic.
Model Fitting Results

9. Principal Component Analysis (PCA)
 Select components based on Scree
plots and Eigenvalue.
Scree Plot

10. Naming Components
 Factor 1: Length of the Article
 Factor 2: Use of key words
 Factor 3: Number of links
 Factor 4: Published Channel
 Factor 5: Is the title polarized
 Factor 6: Publication Date
* There are factors overlapped
Loadings (After Rotation)

11. Result & Business Insight
 Appropriate length
 Which day to publish matters
 More embedded popular article links
 Tech Channel is usually more popular
 Use proper amount of key words
 Create title with unique words
 Title should be polarized

12. Data and News Ethics
 Should we focus solely on news traffic?
 Is there a better way to measure “good news”?
Hate Speech, Online and Social Media. (n.d.). Encyclopedia of Social Media and Politics.
doi:10.4135/9781452244723.n252

13. Future Work
 More work in data cleaning (Low R2)
 Try out different transformation and model selection to see if
could improve my R2
 Try out different techniques to see if there are underlying
relationships that I failed to find out from previous studies
 More diversified variables will be tested