Dataset Card for News Popularity in Multiple Social Media Platforms

Dataset Summary

Social sharing data across Facebook, Google+ and LinkedIn for 100k news items on the topics of: economy, microsoft, obama and palestine.

Supported Tasks and Leaderboards

Popularity prediction/shares prediction

Languages

English

Dataset Structure

Data Instances

{ "id": 35873,
  "title": "Microsoft's 'teen girl' AI turns into a Hitler-loving sex robot within 24 ...",
  "headline": "Developers at Microsoft created 'Tay', an AI modelled to speak 'like a teen girl', in order to improve the customer service on their voice",
  "source": "Telegraph.co.uk",
  "topic": "microsoft",
  "publish_date": "2016-03-24 09:53:54",
  "facebook": 22346,
  "google_plus": 973,
  "linked_in": 1009
}

Data Fields

id: the sentence id in the source dataset
title: the title of the link as shared on social media
headline: the headline, or sometimes the lede of the story
source: the source news site
topic: the topic: one of "economy", "microsoft", "obama" and "palestine"
publish_date: the date the original article was published
facebook: the number of Facebook shares, or -1 if this data wasn't collected
google_plus: the number of Google+ likes, or -1 if this data wasn't collected
linked_in: the number of LinkedIn shares, or -1 if if this data wasn't collected

Data Splits

None

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

The source headlines were by journalists, while the titles were written by the people sharing it on social media.

Annotations

Annotation process

The 'annotations' are simply the number of shares, or likes in the case of Google+ as collected from various API endpoints.

Who are the annotators?

Social media users.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

License: Creative Commons Attribution 4.0 International License (CC-BY)

Citation Information

@article{Moniz2018MultiSourceSF,
  title={Multi-Source Social Feedback of Online News Feeds},
  author={N. Moniz and L. Torgo},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.07055}
}

Contributions

Thanks to @frankier for adding this dataset.