NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

Gruppi, Maurício; Horne, Benjamin D.; Adalı, Sibel

doi:10.48550/arxiv.2102.04567

Cited by 6 publications

(5 citation statements)

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“…Studies on this topic have examined the phenomenon of Twitter content being used in news articles, but these studies have been on small data sets of national news (Broersma and Graham 2013;Oschatz, Stier, and Maier 2021), not local news. Because the NELA-Local dataset contains the original URLs to all articles, embedded social media data can be scraped using a similar method to that used in (Gruppi, Horne, and Adalı 2021). If social media content is quoted or used as the source of the article, this will be captured in the article text already in the dataset.…”

Section: Characterizing Hybrid Local Mediamentioning

confidence: 99%

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

Horne

Gruppi

Joseph

et al. 2022

ICWSM

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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.

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Section: Characterizing Hybrid Local Mediamentioning

confidence: 99%

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

Horne

Gruppi

Joseph

et al. 2022

ICWSM

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“…To the best of our knowledge, our data is the most comprehensive corpus of local news outlets available for research. While there are similarly complete news datasets at a national level (Nørregaard, Horne, and Adalı 2019;Gruppi, Horne, and Adalı 2021), no such collections exist at the local level.…”

Section: Introductionmentioning

confidence: 99%

Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience

Joseph¹,

Horne²,

Green³

et al. 2021

Preprint

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We present analyses from a real-time information monitoring system of online local news in the U.S. We study relationships among online local news coverage of COVID, cases and deaths in an area, and properties of local news outlets and their audiences. Our analysis relies on a unique dataset of the online content of over 300 local news outlets, encompassing over 750,000 articles over a period of 10 months spanning April 2020 to February 2021. We find that the rate of COVID coverage over time by local news outlets was primarily associated with death rates at the national level, but that this effect dissipated over the course of the pandemic as news about COVID was steadily displaced by sociopolitical events, like the 2020 U.S. elections. We also find that both the volume and content of COVID coverage differed depending on local politics, and outlet audience size, as well as evidence that more vulnerable populations received less pandemic-related news.

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“…Note that such claims are inherently expert annotated. Other sources of claims are social media (Potthast et al, 2018;Shu, Sliva, Wang, Tang and Liu, 2017), news outlets (Horne, Khedr and Adali, 2018;Gruppi, Horne and Adalı, 2021;Nørregaard, Horne and Adalı, 2019), blogs, discussions in QA forums, or similar user-generated publishing platforms (Mihaylova, Nakov, Màrquez, Barrón-Cedeño, Mohtarami, Karadzhov and Glass, 2018).…”

Section: Veracity Predictionmentioning

confidence: 99%

The State of Human-centered NLP Technology for Fact-checking

Das,

Liu,

Kovatchev

et al. 2023

Preprint

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Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today.In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies.

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NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

Cited by 6 publications

References 0 publications

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

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

Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience

The State of Human-centered NLP Technology for Fact-checking

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