- 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).
- Predict the number of shares
- More datasets
If you use this dataset in your research, please credit the author
-- Creators: Kelwin Fernandes (kafc ‘@’ inesctec.pt, kelwinfc ’@’ gmail.com), Pedro Vinagre (pedro.vinagre.sousa ’@’ gmail.com) and Pedro Sernadela
-- Donor: Kelwin Fernandes (kafc ’@’ inesctec.pt, kelwinfc '@' gmail.com)
-- Date: May, 2015
1. K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal. -- Results: -- Binary classification as popular vs unpopular using a decision threshold of 1400 social interactions. -- Experiments with different models: Random Forest (best model), Adaboost, SVM, KNN and Naïve Bayes. -- Recorded 67% of accuracy and 0.73 of AUC. - Predicted attribute: online news popularity (boolean)
-- 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.
0. url: URL of the article 1. timedelta: Days between the article publication and the dataset acquisition 2. n_tokens_title: Number of words in the title 3. n_tokens_content: Number of words in the content 4. n_unique_tokens: Rate of unique words in the content 5. n_non_stop_words: Rate of non-stop words in the content 6. n_non_stop_unique_tokens: Rate of unique non-stop words in the content 7. num_hrefs: Number of links 8. num_self_hrefs: Number of links to other articles published by Mashable 9. num_imgs: Number of images 10. num_videos: Number of videos 11. average_token_length: Average length of the words in the content 12. num_keywords: Number of keywords in the metadata 13. data_channel_is_lifestyle: Is data channel 'Lifestyle'? 14. data_channel_is_entertainment: Is data channel 'Entertainment'? 15. data_channel_is_bus: Is data channel 'Business'? 16. data_channel_is_socmed: Is data channel 'Social Media'? 17. data_channel_is_tech: Is data channel 'Tech'? 18. data_channel_is_world: Is data channel 'World'? 19. kw_min_min: Worst keyword (min. shares) 20. kw_max_min: Worst keyword (max. shares) 21. kw_avg_min: Worst keyword (avg. shares) 22. kw_min_max: Best keyword (min. shares) 23. kw_max_max: Best keyword (max. shares) 24. kw_avg_max: Best keyword (avg. shares) 25. kw_min_avg: Avg. keyword (min. shares) 26. kw_max_avg: Avg. keyword (max. shares) 27. kw_avg_avg: Avg. keyword (avg. shares) 28. self_reference_min_shares: Min. shares of referenced articles in Mashable 29. self_reference_max_shares: Max. shares of referenced articles in Mashable 30. self_reference_avg_sharess: Avg. shares of referenced articles in Mashable 31. weekday_is_monday: Was the article published on a Monday? 32. weekday_is_tuesday: Was the article published on a Tuesday? 33. weekday_is_wednesday: Was the article published on a Wednesday? 34. weekday_is_thursday: Was the article published on a Thursday? 35. weekday_is_friday: Was the article published on a Friday? 36. weekday_is_saturday: Was the article published on a Saturday? 37. weekday_is_sunday: Was the article published on a Sunday? 38. is_weekend: Was the article published on the weekend? 39. LDA_00: Closeness to LDA topic 0 40. LDA_01: Closeness to LDA topic 1 41. LDA_02: Closeness to LDA topic 2 42. LDA_03: Closeness to LDA topic 3 43. LDA_04: Closeness to LDA topic 4 44. global_subjectivity: Text subjectivity 45. global_sentiment_polarity: Text sentiment polarity 46. global_rate_positive_words: Rate of positive words in the content 47. global_rate_negative_words: Rate of negative words in the content 48. rate_positive_words: Rate of positive words among non-neutral tokens 49. rate_negative_words: Rate of negative words among non-neutral tokens 50. avg_positive_polarity: Avg. polarity of positive words 51. min_positive_polarity: Min. polarity of positive words 52. max_positive_polarity: Max. polarity of positive words 53. avg_negative_polarity: Avg. polarity of negative words 54. min_negative_polarity: Min. polarity of negative words 55. max_negative_polarity: Max. polarity of negative words 56. title_subjectivity: Title subjectivity 57. title_sentiment_polarity: Title polarity 58. abs_title_subjectivity: Absolute subjectivity level 59. abs_title_sentiment_polarity: Absolute polarity level 60. shares: Number of shares (target)
Missing Attribute Values: None
Class Distribution: the class value (shares) is continuously valued. We
transformed the task into a binary task using a decision
threshold of 1400.
Shares Value Range: Number of Instances in Range:
< 1400 18490
= 1400 21154
