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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. |