Online News Popularity Data Set
Kelwin Fernandes and Pedro Vinagre and Paulo Cortez and Pedro Sernadela

@article{,
title= {Online News Popularity Data Set },
doi= {10.1007/978-3-319-23485-4_53},
keywords= {},
journal= {},
author= {Kelwin Fernandes and Pedro Vinagre and Paulo Cortez and Pedro Sernadela},
year= {2015},
url= {https://archive.ics.uci.edu/dataset/332/online+news+popularity},
license= {https://creativecommons.org/licenses/by/4.0/},
abstract= {##Data Set 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.


##Attribute Information:

Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) 

0. url: URL of the article (non-predictive) 
1. timedelta: Days between the article publication and the dataset acquisition (non-predictive) 
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)


##Relevant Papers:

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.



##Citation Request:

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.

##Source:

Kelwin Fernandes (kafc ‘@’ inesctec.pt, kelwinfc ’@’ gmail.com) - INESC TEC, Porto, Portugal/Universidade do Porto, Portugal. 
Pedro Vinagre (pedro.vinagre.sousa ’@’ gmail.com) - ALGORITMI Research Centre, Universidade do Minho, Portugal 
Paulo Cortez - ALGORITMI Research Centre, Universidade do Minho, Portugal 
Pedro Sernadela - Universidade de Aveiro

**Reference:** Fernandes, Vinagre and Cortez, A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. EPIA 2015. https://doi.org/10.1007/978-3-319-23485-4_53},
superseded= {},
terms= {}
}