The big head and the long tail: An illustration of explanatory strategies for big data Internet studies

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The big head and the long tail : An illustration of explanatory strategies for big data Internet studies. / Helles, Rasmus.

I: First Monday, Bind 18, Nr. 10, 07.10.2013.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Helles, R 2013, 'The big head and the long tail: An illustration of explanatory strategies for big data Internet studies', First Monday, bind 18, nr. 10. https://doi.org/10.5210%2Ffm.v18i10.4874

APA

Helles, R. (2013). The big head and the long tail: An illustration of explanatory strategies for big data Internet studies. First Monday, 18(10). https://doi.org/10.5210%2Ffm.v18i10.4874

Vancouver

Helles R. The big head and the long tail: An illustration of explanatory strategies for big data Internet studies. First Monday. 2013 okt. 7;18(10). https://doi.org/10.5210%2Ffm.v18i10.4874

Author

Helles, Rasmus. / The big head and the long tail : An illustration of explanatory strategies for big data Internet studies. I: First Monday. 2013 ; Bind 18, Nr. 10.

Bibtex

@article{392b72dbabaa4c9f80ec6f9e90ac9305,
title = "The big head and the long tail: An illustration of explanatory strategies for big data Internet studies",
abstract = "This paper discusses how the advent of big data challenges established theories in Internet studies to redevelop existing explanatory strategies in order to incorporate the possibilities offered by this new empirical resource. The article suggests that established analytical procedures and theoretical frameworks used in Internet studies can be fruitfully employed to explain high–level structural phenomena that are only observable through the use of big data. The present article exemplifies this by offering a detailed analysis of how genre analysis of Web sites may be used to shed light on the generative mechanism behind the long–tail distribution of Web site use. The analysis shows that the long tail should be seen as a tiered version of popular top sites, and argues that downsizing of large–scale datasets in combination with qualitative and/or small–scale quantitative procedures may provide qualitatively better understandings of macro phenomena than purely automated, quantitative approaches.",
author = "Rasmus Helles",
year = "2013",
month = oct,
day = "7",
doi = "10.5210%2Ffm.v18i10.4874",
language = "English",
volume = "18",
journal = "First Monday",
issn = "1396-0466",
publisher = "First Monday Editorial Group",
number = "10",

}

RIS

TY - JOUR

T1 - The big head and the long tail

T2 - An illustration of explanatory strategies for big data Internet studies

AU - Helles, Rasmus

PY - 2013/10/7

Y1 - 2013/10/7

N2 - This paper discusses how the advent of big data challenges established theories in Internet studies to redevelop existing explanatory strategies in order to incorporate the possibilities offered by this new empirical resource. The article suggests that established analytical procedures and theoretical frameworks used in Internet studies can be fruitfully employed to explain high–level structural phenomena that are only observable through the use of big data. The present article exemplifies this by offering a detailed analysis of how genre analysis of Web sites may be used to shed light on the generative mechanism behind the long–tail distribution of Web site use. The analysis shows that the long tail should be seen as a tiered version of popular top sites, and argues that downsizing of large–scale datasets in combination with qualitative and/or small–scale quantitative procedures may provide qualitatively better understandings of macro phenomena than purely automated, quantitative approaches.

AB - This paper discusses how the advent of big data challenges established theories in Internet studies to redevelop existing explanatory strategies in order to incorporate the possibilities offered by this new empirical resource. The article suggests that established analytical procedures and theoretical frameworks used in Internet studies can be fruitfully employed to explain high–level structural phenomena that are only observable through the use of big data. The present article exemplifies this by offering a detailed analysis of how genre analysis of Web sites may be used to shed light on the generative mechanism behind the long–tail distribution of Web site use. The analysis shows that the long tail should be seen as a tiered version of popular top sites, and argues that downsizing of large–scale datasets in combination with qualitative and/or small–scale quantitative procedures may provide qualitatively better understandings of macro phenomena than purely automated, quantitative approaches.

U2 - 10.5210%2Ffm.v18i10.4874

DO - 10.5210%2Ffm.v18i10.4874

M3 - Journal article

VL - 18

JO - First Monday

JF - First Monday

SN - 1396-0466

IS - 10

ER -

ID: 51416610