task_categories:text-classification task_ids:text-scoring annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:cc-by-4.0 social-media-shares-prediction arxiv:1801.07055 region:us
This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn.
The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine.
This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation.
@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}
}
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: News Popularity in Multiple Social Media Platforms
tags:
- social-media-shares-prediction
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: title
    dtype: string
  - name: headline
    dtype: string
  - name: source
    dtype: string
  - name: topic
    dtype: string
  - name: publish_date
    dtype: string
  - name: facebook
    dtype: int32
  - name: google_plus
    dtype: int32
  - name: linked_in
    dtype: int32
  splits:
  - name: train
    num_bytes: 27927641
    num_examples: 93239
  download_size: 30338277
  dataset_size: 27927641
---

# Dataset Card for News Popularity in Multiple Social Media Platforms

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [UCI](https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms)
- **Repository:**
- **Paper:** [Arxiv](https://arxiv.org/abs/1801.07055)
- **Leaderboard:** [Kaggle](https://www.kaggle.com/nikhiljohnk/news-popularity-in-multiple-social-media-platforms/code)
- **Point of Contact:**

### 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](https://github.com/frankier) for adding this dataset.