Cultural Computing at Literature Scale: Encoding the Cultural Knowledge of Tens of Billions of Words of Academic Literature

Leetaru, Kahlev H.; Perkins, Timothy K.; Rewerts, Chris C.

doi:10.1045/september2014-leetaru

Cited by 13 publications

(5 citation statements)

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“…This article uses GDELT’s Global Knowledge Graph (GKG) as its data source (Leetaru, 2012a, 2015a). On a daily basis, GDELT monitors news globally and employs a computer-assisted content analysis that identifies people, locations, themes, emotions, narratives, and events (Leetaru, 2015).…”

Section: Methodsmentioning

confidence: 99%

The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016

Vargo

Гуо

Amazeen

2017

New Media & Society

429

260

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This study examines the agenda-setting power of fake news and fact-checkers who fight them through a computational look at the online mediascape from 2014 to 2016. Although our study confirms that content from fake news websites is increasing, these sites do not exert excessive power. Instead, fake news has an intricately entwined relationship with online partisan media, both responding and setting its issue agenda. In 2016, partisan media appeared to be especially susceptible to the agendas of fake news, perhaps due to the election. Emerging news media are also responsive to the agendas of fake news, but to a lesser degree. Fake news coverage itself is diverging and becoming more autonomous topically. While fact-checkers are autonomous in their selection of issues to cover, they were not influential in determining the agenda of news media overall, and their influence appears to be declining, illustrating the difficulties factcheckers face in disseminating their corrections.

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Section: Methodsmentioning

confidence: 99%

The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016

Vargo

Гуо

Amazeen

2017

New Media & Society

429

260

View full text Add to dashboard Cite

show abstract

“… 3. Leetaru (2012b) offered concrete details on the underlying algorithms used. In validating a theme, the system includes the manual review of randomly selected articles as verification that the algorithm used is accurate (aka, externally valid) and on par with leading computer-assisted classification systems (Leetaru, 2012b; Leetaru, Perkins, & Rewerts, 2014). …”

mentioning

confidence: 99%

Networks, Big Data, and Intermedia Agenda Setting: An Analysis of Traditional, Partisan, and Emerging Online U.S. News

Vargo

Гуо

2016

Journalism & Mass Communication Quarterly

123

111

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This large-scale intermedia agenda–setting analysis examines U.S. online media sources for 2015. The network agenda–setting model showed that media agendas were highly homogeneous and reciprocal. Online partisan media played a leading role in the entire media agenda. Two elite newspapers— The New York Times and The Washington Post—were found to no longer be in control of the news agenda and were more likely to follow online partisan media. This article provides evidence for a nuanced view of the network agenda–setting model; intermedia agenda–setting effects varied by media type, issue type, and time periods.

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“…The GDELT Global Knowledge Graph (GKG) is a software suite that analyses global newspaper articles in realtime to extract entities such as persons, organizations, locations, dates, themes and emotions (Leetaru et al, 2014). The extraction of location data is done through a process called full text geocoding, developed by Leetaru (Leetaru, 2012).…”

Section: Methodsmentioning

confidence: 99%

Macroeconomic forecasting with statistically validated knowledge graphs

Tilly¹,

Livan²

2021

Preprint

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This study leverages narrative from global newspapers to construct theme-based knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies compared to a number of benchmarks. Our analysis relies on a filtering methodology that extracts "backbones" of statistically significant edges from large graph data sets. We find that changes in the eigenvector centrality of nodes in such backbones capture shifts in relative importance between different themes significantly better than graph similarity measures. We supplement our results with an interpretability analysis, showing that the theme categories "disease" and "economic" have the strongest predictive power during the time period that we consider. Our work serves as a blueprint for the construction of parsimonious -yet informative -theme-based knowledge graphs to monitor in real time the evolution of relevant phenomena in socio-economic systems.

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Cultural Computing at Literature Scale: Encoding the Cultural Knowledge of Tens of Billions of Words of Academic Literature

Cited by 13 publications

References 0 publications

The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016

The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016

Networks, Big Data, and Intermedia Agenda Setting: An Analysis of Traditional, Partisan, and Emerging Online U.S. News

Macroeconomic forecasting with statistically validated knowledge graphs

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