NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems

03/16/2022
by   Benjamin D. Horne, et al.
0

In this paper, we present a dataset of over 1.4M online news articles from 313 local U.S. news outlets published over 20 months (between April 4th, 2020 and December 31st, 2021). These outlets cover a geographically diverse set of communities across the United States. In order to estimate characteristics of the local audience, included with this news article data is a wide range of county-level metadata, including demographics, 2020 Presidential Election vote shares, and community resilience estimates from the U.S. Census Bureau. The NELA-Local dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GFE66K.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/18/2020

NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

In this paper, we present an updated version of the NELA-GT-2018 dataset...
research
02/01/2021

Counting Protests in News Articles: A Dataset and Semi-Automated Data Collection Pipeline

Between January 2017 and January 2021, thousands of local news sources i...
research
05/11/2023

Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide

Local news has become increasingly important in the news industry due to...
research
01/13/2023

Using the profile of publishers to predict barriers across news articles

Detection of news propagation barriers, being economical, cultural, poli...
research
05/26/2020

Do All Good Actors Look The Same? Exploring News Veracity Detection Across The U.S. and The U.K

A major concern with text-based news veracity detection methods is that ...
research
01/08/2020

SirenLess: reveal the intention behind news

News articles tend to be increasingly misleading nowadays, preventing re...
research
08/27/2018

Models for Predicting Community-Specific Interest in News Articles

In this work, we ask two questions: 1. Can we predict the type of commun...

Please sign up or login with your details

Forgot password? Click here to reset