Credibility by automation
Expectations of future knowledge production in social media analytics
Bibliographic Data
| ID | 21690302 |
|---|---|
| Authors | Juho Pääkkönen (0000-0002-0378-845X, University of Helsinki, Finland; Aalto University, Finland, corresponding author), Salla-Maaria Laaksonen (0000-0003-3532-2387, University of Helsinki), Mikko Jauho (0000-0002-6999-6788, University of Helsinki) |
| Year | 2020 |
| Volume | 26 |
| Issue | 4 |
| Pages | 790-807 |
| Publication date | 2020-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Convergence The International Journal of Research into New Media Technologies (JOURNAL) |
| Journal identifiers | ISSN: 1354-8565 • E-ISSN: 1748-7382 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/1354856520901839 |
| OpenAlex | W3004497393 |
| Language | EN |
| Citations received | 3 |
| References cited | 35 |
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
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2024 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |