TY - JOUR
T1 - A scholarly divide
T2 - Social media, big data, and unattainble scholarship
AU - Zelenkauskaite, Asta
AU - Bucy, Erik P.
PY - 2016/5/1
Y1 - 2016/5/1
N2 - Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the 'Big Data' paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze - and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing 'scholarly divide' between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.
AB - Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the 'Big Data' paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze - and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing 'scholarly divide' between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.
UR - http://www.scopus.com/inward/record.url?scp=84966564897&partnerID=8YFLogxK
U2 - 10.5210/fm.v21i5.6358
DO - 10.5210/fm.v21i5.6358
M3 - Article
AN - SCOPUS:84966564897
SN - 1396-0466
VL - 21
JO - First Monday
JF - First Monday
IS - 5
ER -