1. Title: Online News Popularity 2. Source Information -- 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 3. Past Usage: 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) 4. Relevant Information: -- 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. 5. Number of Instances: 39797 6. Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) 7. Attribute Information: 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) 8. Missing Attribute Values: None 9. 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 Summary Statistics: Feature Min Max Mean SD timedelta 8.0000 731.0000 354.5305 214.1611 n_tokens_title 2.0000 23.0000 10.3987 2.1140 n_tokens_content 0.0000 8474.0000 546.5147 471.1016 n_unique_tokens 0.0000 701.0000 0.5482 3.5207 n_non_stop_words 0.0000 1042.0000 0.9965 5.2312 n_non_stop_unique_tokens 0.0000 650.0000 0.6892 3.2648 num_hrefs 0.0000 304.0000 10.8837 11.3319 num_self_hrefs 0.0000 116.0000 3.2936 3.8551 num_imgs 0.0000 128.0000 4.5441 8.3093 num_videos 0.0000 91.0000 1.2499 4.1078 average_token_length 0.0000 8.0415 4.5482 0.8444 num_keywords 1.0000 10.0000 7.2238 1.9091 data_channel_is_lifestyle 0.0000 1.0000 0.0529 0.2239 data_channel_is_entertainment 0.0000 1.0000 0.1780 0.3825 data_channel_is_bus 0.0000 1.0000 0.1579 0.3646 data_channel_is_socmed 0.0000 1.0000 0.0586 0.2349 data_channel_is_tech 0.0000 1.0000 0.1853 0.3885 data_channel_is_world 0.0000 1.0000 0.2126 0.4091 kw_min_min -1.0000 377.0000 26.1068 69.6323 kw_max_min 0.0000 298400.0000 1153.9517 3857.9422 kw_avg_min -1.0000 42827.8571 312.3670 620.7761 kw_min_max 0.0000 843300.0000 13612.3541 57985.2980 kw_max_max 0.0000 843300.0000 752324.0667 214499.4242 kw_avg_max 0.0000 843300.0000 259281.9381 135100.5433 kw_min_avg -1.0000 3613.0398 1117.1466 1137.4426 kw_max_avg 0.0000 298400.0000 5657.2112 6098.7950 kw_avg_avg 0.0000 43567.6599 3135.8586 1318.1338 self_reference_min_shares 0.0000 843300.0000 3998.7554 19738.4216 self_reference_max_shares 0.0000 843300.0000 10329.2127 41027.0592 self_reference_avg_sharess 0.0000 843300.0000 6401.6976 24211.0269 weekday_is_monday 0.0000 1.0000 0.1680 0.3739 weekday_is_tuesday 0.0000 1.0000 0.1864 0.3894 weekday_is_wednesday 0.0000 1.0000 0.1875 0.3903 weekday_is_thursday 0.0000 1.0000 0.1833 0.3869 weekday_is_friday 0.0000 1.0000 0.1438 0.3509 weekday_is_saturday 0.0000 1.0000 0.0619 0.2409 weekday_is_sunday 0.0000 1.0000 0.0690 0.2535 is_weekend 0.0000 1.0000 0.1309 0.3373 LDA_00 0.0000 0.9270 0.1846 0.2630 LDA_01 0.0000 0.9259 0.1413 0.2197 LDA_02 0.0000 0.9200 0.2163 0.2821 LDA_03 0.0000 0.9265 0.2238 0.2952 LDA_04 0.0000 0.9272 0.2340 0.2892 global_subjectivity 0.0000 1.0000 0.4434 0.1167 global_sentiment_polarity -0.3937 0.7278 0.1193 0.0969 global_rate_positive_words 0.0000 0.1555 0.0396 0.0174 global_rate_negative_words 0.0000 0.1849 0.0166 0.0108 rate_positive_words 0.0000 1.0000 0.6822 0.1902 rate_negative_words 0.0000 1.0000 0.2879 0.1562 avg_positive_polarity 0.0000 1.0000 0.3538 0.1045 min_positive_polarity 0.0000 1.0000 0.0954 0.0713 max_positive_polarity 0.0000 1.0000 0.7567 0.2478 avg_negative_polarity -1.0000 0.0000 -0.2595 0.1277 min_negative_polarity -1.0000 0.0000 -0.5219 0.2903 max_negative_polarity -1.0000 0.0000 -0.1075 0.0954 title_subjectivity 0.0000 1.0000 0.2824 0.3242 title_sentiment_polarity -1.0000 1.0000 0.0714 0.2654 abs_title_subjectivity 0.0000 0.5000 0.3418 0.1888 abs_title_sentiment_polarity 0.0000 1.0000 0.1561 0.2263 Citation Request: Please include this citation if you plan to use this database: 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.