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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.
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