Jessa Hayagan Gamba/

Internet News and Consumer Engagement


Internet News and Consumer Engagement

This dataset contains data on news articles published between early September to early November 2019. It's enriched by Facebook engagement data, such as the number of shares, comments, and reactions. The dataset was first created to predict the popularity of an article before it was published; however, there is a lot more you can analyze!

Not sure where to begin? Scroll to the bottom to find challenges!

import pandas as pd

pd.read_csv("news_articles.csv", index_col=0)

Data dictionary

0source_idpublisher unique identifier
1source_namehuman-readable publisher name
2authorarticle author
3titlearticle headline
4descriptionarticle short description
5urlarticle URL from publisher website
6url_to_imageurl to main image associated with the article
7published_atexact time and date of publishing the article
8contentunformatted content of the article truncated to 260 characters
9top_articlevalue indicating if article was listed as a top article on publisher website
10engagement_reaction_countusers reactions count for posts on Facebook involving article URL
11engagement_comment_countusers comments count for posts on Facebook involving article URL
12engagement_share_countusers shares count for posts on Facebook involving article URL
13engagement_comment_plugin_countUsers comments count for Facebook comment plugin on article website

Source of dataset.

Don't know where to start?

Challenges are brief tasks designed to help you practice specific skills:

  • 🗺️ Explore: What publishers and authors publish the most content based on this dataset? How about most engaging content?
  • 📊 Visualize: Create two words clouds for the title and description of the articles to find the most popular words. Make sure to remove stop words!
  • 🔎 Analyze: On days where total engagement was higher than usual, can you identify a common event or theme based on text?

Scenarios are broader questions to help you develop an end-to-end project for your portfolio:

You have a friend who works as a reporter for BBC news. He's been disappointed in his articles' low Facebook engagement and that his articles have never been listed as top articles on the BBC. You've offered your help by finding data-driven recommendations on how he should position his articles (such as guidelines on title and description) and when in the day he should publish articles. He's interested in what makes a top article at BBC and what gets the most Facebook engagement.

You will need to prepare a report that is accessible to a broad audience. It will need to outline your motivation, analysis steps, findings, and conclusions.

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-- Select the names, continents, and areas of countries
-- Note that the query may take a long time to run if you remove the LIMIT statement
SELECT AS country_name, AS continent_name,
FROM countries
	INNER JOIN regions USING(region_id)
	INNER JOIN continents USING(continent_id)
ORDER BY country_name
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