Credibility by automation

Expectations of future knowledge production in social media analytics

Dados Bibliográficos

ID21690302
AutoresJuho Pääkkönen (0000-0002-0378-845X, University of Helsinki, Finland; Aalto University, Finland, autor correspondente), Salla-Maaria Laaksonen (0000-0003-3532-2387, University of Helsinki), Mikko Jauho (0000-0002-6999-6788, University of Helsinki)
Ano2020
Volume26
Fascículo4
Páginas790-807
Data de publicação2020-08-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoConvergence The International Journal of Research into New Media Technologies (JOURNAL)
Identificadores do periódicoISSN: 1354-8565 • E-ISSN: 1748-7382
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/1354856520901839
OpenAlexW3004497393
IdiomaEN
Citações recebidas3
Referências citadas35

Social media analytics is a burgeoning new field associated with high promises of societal relevance and business value but also methodological and practical problems. In this article, we build on the sociology of expectations literature and research on expertise in the interaction between humans and machines to examine how analysts and clients make their expectations about social media analytics credible in the face of recognized problems. To investigate how this happens in different contexts, we draw on thematic interviews with 10 social media analytics and client companies. In our material, social media analytics appears as a field facing both hopes and skepticism – toward data, analysis methods, or the users of analytics – from both the clients and the analysts. In this setting, the idea of automated analysis through algorithmic methods emerges as a central notion that lends credibility to expectations about social media analytics. Automation is thought to, first, extend and make expert interpretation of messy social media data more rigorous; second, eliminate subjective judgments from measurement on social media; and, third, allow for coordination of knowledge management inside organizations. Thus, ideas of automation importantly work to uphold the expectations of the value of analytics. Simultaneously, they shape what kinds of expertise, tools, and practices come to be involved in the future of analytics as knowledge production

Analytics · Big data · Business · Business analytics · Business Intelligence · Business model · Credibility · Cultural Analytics · Data science · Epistemology · Knowledge management · Qualitative research · Semantic analytics · Social media · Social media analytics · Social science · Sociology · Thematic analysis · World Wide Web · Computer Science · Ethics and Social Impacts of AI · Mobile Crowdsensing and Crowdsourcing · Privacy, Security, and Data Protection · Marketing