Multi-Source Social Feedback of Online News Feeds

TL;DR

This paper introduces a comprehensive dataset of news articles and their social feedback across multiple platforms, enabling evaluation of predictive analytics and other research tasks in social media and news analysis.

Contribution

It provides a large, multi-platform dataset of news items and social feedback, addressing the lack of comprehensive baseline data for evaluative comparisons.

Findings

Dataset covers 8 months of social feedback data

Includes about 100,000 news items across four topics

Supports various research tasks like prediction, sentiment analysis, and topic detection

Abstract

The profusion of user generated content caused by the rise of social media platforms has enabled a surge in research relating to fields such as information retrieval, recommender systems, data mining and machine learning. However, the lack of comprehensive baseline data sets to allow a thorough evaluative comparison has become an important issue. In this paper we present a large data set of news items from well-known aggregators such as Google News and Yahoo! News, and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The data collected 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.