Replication Data for: Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures (doi:10.7910/DVN/TBWMCG)

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Replication Data for: Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures

doi:10.7910/DVN/TBWMCG

Harvard Dataverse

2023-09-06

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Girbau, Andreu; Kobayashi, Tetsuro; Renoust, Benjami; Matsui, Yusuke; Satoh, Shin'ichi, 2023, "Replication Data for: Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures", https://doi.org/10.7910/DVN/TBWMCG, Harvard Dataverse, V1

Replication Data for: Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures

doi:10.7910/DVN/TBWMCG

Girbau, Andreu (National Insitute of Informatics)

Kobayashi, Tetsuro (City University of Hong Kong)

Renoust, Benjami (Osaka University)

Matsui, Yusuke (University of Tokyo)

Satoh, Shin'ichi (National Institute of Informatics)

Harvard Dataverse

Girbau, Andreu

Code Ocean

https://doi.org/10.7910/DVN/TBWMCG

Social Sciences

Analyzing the appearances of political figures in large-scale news archives is increasingly important with the growing availability of large-scale news archives and developments in computer vision. We present a deep learning-based method combining face detection, tracking, and classification, which is particularly unique because it does not require any re-training when targeting new individuals. Users can feed only a few images of target individuals to reliably detect, track, and classify them. Extensive validation of prominent political figures in two news archives spanning 10 to 20 years, one containing three US cable news and the other including two major Japanese news programs, consistently shows high performance and flexibility of the proposed method. The codes are made readily available to the public.

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