Competition - Internet News and Consumer Engagement

(Invalid URL)

Internet News and Consumer Engagement

Ready to put your coding skills to the test? Join us for our Workspace Competition.
For more information, visit datacamp.com/workspacecompetition

Context

This dataset (source) consists of data about news articles collected from Sept. 3, 2019 until Nov. 4, 2019. Afterwards, it is enriched by Facebook engagement data, such as number of shares, comments and reactions. It was first created to predict the popularity of an article before it was published. However, there is a lot more you can analyze; take a look at some suggestions at the end of this template.

Load packages

library(skimr)
library(tidyverse)

Load your Data

articles <- readr::read_csv('data/news_articles.csv.gz')
articles$source_id <- as.factor(articles$source_id)
articles$source_name <- as.factor(articles$source_name)
skim(articles) %>% 
  select(-(numeric.p0:numeric.p100)) %>%
  select(-(complete_rate))

Understand your data

Variable.	Description
source_id	publisher unique identifier
source_name	human-readable publisher name
author	article author
title	article headline
description	article short description
url	article URL from publisher website
url_to_image	URL to main image associated with the article
published_at	exact time and date of publishing the article
content	unformatted content of the article truncated to 260 characters
top_article	value indicating if article was listed as a top article on publisher website
engagement_reaction_count	users reactions count for posts on Facebook involving article URL
engagement_comment_count	users comments count for posts on Facebook involving article URL
engagement_share_count	users shares count for posts on Facebook involving article URL
engagement_comment_plugin_count	Users comments count for Facebook comment plugin on article website

Now you can start to explore this dataset with the chance to win incredible prices! Can't think of where to start? Try your hand at these suggestions:

Extract useful insights and visualize them in the most interesting way possible.
Categorize the articles into different categories based on, for example, sentiment.
Cluster the news articles, authors or publishers based on, for example, topic.
Make a title generator based on data such as content, description, etc.

Judging Criteria

CATEGORY	WEIGHTAGE	DETAILS
Analysis	30%	Documentation on the goal and what was included in the analysis How the question was approached Visualisation tools and techniques utilized
Results	30%	How the results derived related to the problem chosen The ability to trigger potential further analysis
Creativity	40%	How "out of the box" the analysis conducted is Whether the publication is properly motivated and adds value