Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape

TL;DR

This paper introduces a large, diverse political news dataset with extensive features to facilitate research on media strategies, bias, and misinformation, supporting various analytical use cases.

Contribution

It provides a comprehensive dataset of over 136,000 news articles with computed features, enabling systematic studies of news production and dissemination strategies.

Findings

Dataset includes 136K articles from 92 sources

Features cover bias, persuasion, misinformation

Demonstrates use cases like news characterization and attribution

Abstract

The complexity and diversity of today's media landscape provides many challenges for researchers studying news producers. These producers use many different strategies to get their message believed by readers through the writing styles they employ, by repetition across different media sources with or without attribution, as well as other mechanisms that are yet to be studied deeply. To better facilitate systematic studies in this area, we present a large political news data set, containing over 136K news articles, from 92 news sources, collected over 7 months of 2017. These news sources are carefully chosen to include well-established and mainstream sources, maliciously fake sources, satire sources, and hyper-partisan political blogs. In addition to each article we compute 130 content-based and social media engagement features drawn from a wide range of literature on political bias, persuasion, and misinformation. With the release of the data set, we also provide the source code for feature computation. In this paper, we discuss the first release of the data set and demonstrate 4 use cases of the data and features: news characterization, engagement characterization, news attribution and content copying, and discovering news narratives.