Lifting the Veil on the Use of Big Data News Repositories: A Documentation and Critical Discussion of A Protest Event Analysis
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Lifting the Veil on the Use of Big Data News Repositories: A Documentation and Critical Discussion of A Protest Event Analysis. / Hoffmann, Matthias; Santos, Felipe G.; Neumayer, Christina; Mercea, Dan.
I: Communication Methods and Measures, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Lifting the Veil on the Use of Big Data News Repositories: A Documentation and Critical Discussion of A Protest Event Analysis
AU - Hoffmann, Matthias
AU - Santos, Felipe G.
AU - Neumayer, Christina
AU - Mercea, Dan
PY - 2022
Y1 - 2022
N2 - This paper presents a critical discussion of the processing, reliability and implications of free big data repositories. We argue that big data is not only the starting point of scientific analyses but also the outcome of a long string of invisible or semi-visible tasks, often masked by the fetish of size that supposedly lends validity to big data. We unpack these notions by illustrating the process of extracting protest event data from the Global Database of Events, Language and Tone (GDELT) in six European countries over a period of seven years. To stand up to rigorous scientific scrutiny, we collected additional data by computational means and undertook large-scale neural-network translation tasks, dictionary-based content analyses, machine-learning classification tasks, and human coding. In a documentation and critical discussion of this process, we render visible opaque procedures that inevitably shape any dataset and show how this type of freely available datasets require significant additional resources of knowledge, labor, money, and computational power. We conclude that while these processes can ultimately yield more valid datasets, the supposedly free and ready-to-use big news data repositories should not be taken at face value.
AB - This paper presents a critical discussion of the processing, reliability and implications of free big data repositories. We argue that big data is not only the starting point of scientific analyses but also the outcome of a long string of invisible or semi-visible tasks, often masked by the fetish of size that supposedly lends validity to big data. We unpack these notions by illustrating the process of extracting protest event data from the Global Database of Events, Language and Tone (GDELT) in six European countries over a period of seven years. To stand up to rigorous scientific scrutiny, we collected additional data by computational means and undertook large-scale neural-network translation tasks, dictionary-based content analyses, machine-learning classification tasks, and human coding. In a documentation and critical discussion of this process, we render visible opaque procedures that inevitably shape any dataset and show how this type of freely available datasets require significant additional resources of knowledge, labor, money, and computational power. We conclude that while these processes can ultimately yield more valid datasets, the supposedly free and ready-to-use big news data repositories should not be taken at face value.
U2 - 10.1080/19312458.2022.2128099
DO - 10.1080/19312458.2022.2128099
M3 - Journal article
JO - Communication Methods and Measures
JF - Communication Methods and Measures
SN - 1931-2458
ER -
ID: 319879217