Online News Popularity
About
This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks (popularity).
* The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls.
* Acquisition date: January 8, 2015
* The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method. See their article for more details on how the relative performance values were set.
Subject Area
Business
Instances
39,797
Features
61
Data Types
Multivariate
Tasks
Classification, Regression
Feature Types
Integer, Continuous
Features
| Name | Role | Type | Units | Missing Values |
|---|---|---|---|---|
| url | Id | Categorical | - | No |
| timedelta | Other | Continuous | - | No |
| n_tokens_title | Feature | Continuous | - | No |
| n_tokens_content | Feature | Continuous | - | No |
| n_unique_tokens | Feature | Continuous | - | No |
| n_non_stop_words | Feature | Continuous | - | No |
| n_non_stop_unique_tokens | Feature | Continuous | - | No |
| num_hrefs | Feature | Continuous | - | No |
| num_self_hrefs | Feature | Continuous | - | No |
| num_imgs | Feature | Continuous | - | No |
| num_videos | Feature | Continuous | - | No |
| average_token_length | Feature | Continuous | - | No |
| num_keywords | Feature | Continuous | - | No |
| data_channel_is_lifestyle | Feature | Continuous | - | No |
| data_channel_is_entertainment | Feature | Continuous | - | No |
| data_channel_is_bus | Feature | Continuous | - | No |
| data_channel_is_socmed | Feature | Continuous | - | No |
| data_channel_is_tech | Feature | Continuous | - | No |
| data_channel_is_world | Feature | Continuous | - | No |
| kw_min_min | Feature | Continuous | - | No |
| kw_max_min | Feature | Continuous | - | No |
| kw_avg_min | Feature | Continuous | - | No |
| kw_min_max | Feature | Continuous | - | No |
| kw_max_max | Feature | Continuous | - | No |
| kw_avg_max | Feature | Continuous | - | No |
| kw_min_avg | Feature | Continuous | - | No |
| kw_max_avg | Feature | Continuous | - | No |
| kw_avg_avg | Feature | Continuous | - | No |
| self_reference_min_shares | Feature | Continuous | - | No |
| self_reference_max_shares | Feature | Continuous | - | No |
| self_reference_avg_sharess | Feature | Continuous | - | No |
| weekday_is_monday | Feature | Continuous | - | No |
| weekday_is_tuesday | Feature | Continuous | - | No |
| weekday_is_wednesday | Feature | Continuous | - | No |
| weekday_is_thursday | Feature | Continuous | - | No |
| weekday_is_friday | Feature | Continuous | - | No |
| weekday_is_saturday | Feature | Continuous | - | No |
| weekday_is_sunday | Feature | Continuous | - | No |
| is_weekend | Feature | Continuous | - | No |
| LDA_00 | Feature | Continuous | - | No |
| LDA_01 | Feature | Continuous | - | No |
| LDA_02 | Feature | Continuous | - | No |
| LDA_03 | Feature | Continuous | - | No |
| LDA_04 | Feature | Continuous | - | No |
| global_subjectivity | Feature | Continuous | - | No |
| global_sentiment_polarity | Feature | Continuous | - | No |
| global_rate_positive_words | Feature | Continuous | - | No |
| global_rate_negative_words | Feature | Continuous | - | No |
| rate_positive_words | Feature | Continuous | - | No |
| rate_negative_words | Feature | Continuous | - | No |
| avg_positive_polarity | Feature | Continuous | - | No |
| min_positive_polarity | Feature | Continuous | - | No |
| max_positive_polarity | Feature | Continuous | - | No |
| avg_negative_polarity | Feature | Continuous | - | No |
| min_negative_polarity | Feature | Continuous | - | No |
| max_negative_polarity | Feature | Continuous | - | No |
| title_subjectivity | Feature | Continuous | - | No |
| title_sentiment_polarity | Feature | Continuous | - | No |
| abs_title_subjectivity | Feature | Continuous | - | No |
| abs_title_sentiment_polarity | Feature | Continuous | - | No |
| shares | Target | Integer | - | No |
Introductory Paper
A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News
Kelwin Fernandes, Pedro Vinagre, P. Cortez. 2015.
Portuguese Conference on Artificial Intelligence
Additional Metadata
Keywords
–
Authors
Kelwin Fernandes
Pedro Vinagre
Paulo Cortez
Pedro Sernadela
Year Created
2015
License
CC BY 4.0