Data
OnlineNewsPopularity

OnlineNewsPopularity

active ARFF Publicly available Visibility: public Uploaded 17-02-2016 by Hilda Fabiola Bernard
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Author: Kelwin Fernandes (INESC TEC, Universidade doPorto), Pedro Vinagre (ALGORITMI Research Centre, Universidade do Minho), Paulo Cortez - ALGORITMI Research Centre (Universidade do Minho), Pedro Sernadela (Universidade de Aveiro) Source: UCI Please cite: 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. 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. Attribute Information: Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) Attribute Information: 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.

61 features

shares (target)numeric1454 unique values
0 missing
urlnominal39644 unique values
0 missing
timedeltanumeric724 unique values
0 missing
n_tokens_titlenumeric20 unique values
0 missing
n_tokens_contentnumeric2406 unique values
0 missing
n_unique_tokensnumeric27281 unique values
0 missing
n_non_stop_wordsnumeric1451 unique values
0 missing
n_non_stop_unique_tokensnumeric22930 unique values
0 missing
num_hrefsnumeric133 unique values
0 missing
num_self_hrefsnumeric59 unique values
0 missing
num_imgsnumeric91 unique values
0 missing
num_videosnumeric53 unique values
0 missing
average_token_lengthnumeric30136 unique values
0 missing
num_keywordsnumeric10 unique values
0 missing
data_channel_is_lifestylenumeric2 unique values
0 missing
data_channel_is_entertainmentnumeric2 unique values
0 missing
data_channel_is_busnumeric2 unique values
0 missing
data_channel_is_socmednumeric2 unique values
0 missing
data_channel_is_technumeric2 unique values
0 missing
data_channel_is_worldnumeric2 unique values
0 missing
kw_min_minnumeric26 unique values
0 missing
kw_max_minnumeric1076 unique values
0 missing
kw_avg_minnumeric17003 unique values
0 missing
kw_min_maxnumeric1021 unique values
0 missing
kw_max_maxnumeric35 unique values
0 missing
kw_avg_maxnumeric30834 unique values
0 missing
kw_min_avgnumeric15982 unique values
0 missing
kw_max_avgnumeric19438 unique values
0 missing
kw_avg_avgnumeric39300 unique values
0 missing
self_reference_min_sharesnumeric1255 unique values
0 missing
self_reference_max_sharesnumeric1137 unique values
0 missing
self_reference_avg_sharessnumeric8626 unique values
0 missing
weekday_is_mondaynumeric2 unique values
0 missing
weekday_is_tuesdaynumeric2 unique values
0 missing
weekday_is_wednesdaynumeric2 unique values
0 missing
weekday_is_thursdaynumeric2 unique values
0 missing
weekday_is_fridaynumeric2 unique values
0 missing
weekday_is_saturdaynumeric2 unique values
0 missing
weekday_is_sundaynumeric2 unique values
0 missing
is_weekendnumeric2 unique values
0 missing
LDA_00numeric39337 unique values
0 missing
LDA_01numeric39098 unique values
0 missing
LDA_02numeric39525 unique values
0 missing
LDA_03numeric38963 unique values
0 missing
LDA_04numeric39370 unique values
0 missing
global_subjectivitynumeric34501 unique values
0 missing
global_sentiment_polaritynumeric34695 unique values
0 missing
global_rate_positive_wordsnumeric13159 unique values
0 missing
global_rate_negative_wordsnumeric10271 unique values
0 missing
rate_positive_wordsnumeric2284 unique values
0 missing
rate_negative_wordsnumeric2284 unique values
0 missing
avg_positive_polaritynumeric27301 unique values
0 missing
min_positive_polaritynumeric33 unique values
0 missing
max_positive_polaritynumeric38 unique values
0 missing
avg_negative_polaritynumeric13841 unique values
0 missing
min_negative_polaritynumeric54 unique values
0 missing
max_negative_polaritynumeric49 unique values
0 missing
title_subjectivitynumeric673 unique values
0 missing
title_sentiment_polaritynumeric813 unique values
0 missing
abs_title_subjectivitynumeric532 unique values
0 missing
abs_title_sentiment_polaritynumeric653 unique values
0 missing

62 properties

39644
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
60
Number of numeric attributes.
1
Number of nominal attributes.
22.3
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
39644
The minimal number of distinct values among attributes of the nominal type.
98.36
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
39644
The maximum number of distinct values among attributes of the nominal type.
-4.58
Minimum skewness among attributes of the numeric type.
1.64
Percentage of nominal attributes.
4.01
Third quartile of skewness among attributes of the numeric type.
198.79
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
55.06
Third quartile of standard deviation of attributes of the numeric type.
214502.13
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.63
First quartile of kurtosis among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.15
First quartile of means among attributes of the numeric type.
2111.1
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
17694.95
Mean of means among attributes of the numeric type.
0.34
First quartile of skewness among attributes of the numeric type.
-4072.46
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.24
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
39644
Average number of distinct values among the attributes of the nominal type.
3.31
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
14.09
Mean skewness among attributes of the numeric type.
0.31
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
8633.94
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.66
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.28
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.38
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
39560.29
Maximum kurtosis among attributes of the numeric type.
-0.52
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
32.76
Third quartile of kurtosis among attributes of the numeric type.
752324.07
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

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