More Media, More People—On Social & Multimodal Media Intelligence
- Publisher: Umeå universitet, Humlab
CIBAS-project | competitive intelligence | fashion analytics | media intelligence | social & multimodal media intelligence | Media Studies | Medievetenskap
The purpose of this article is to address some challenges facing media intelligence in general, and competitive intelligence in particular within an altered information landscape. To understand this new situation, the notion of social and multimodal media intelligence are introduced. With cases taken primarily from the Swedish media intelligence sector, we argue that data driven media intelligence today needs to pay increasing attention to new forms of (A.) crowd-oriented and (B.) multimedia-saturated information. As a subcategory of media intelligence, competitive intelligence refers to the gathering of publicly available information about an organisation or a company’s competitors—using it to gain business advantages. Traditionally such intelligence has implied a set of techniques and tools that transforms numerical or textual data into useful information for business analysis. Today, however, we argue that such techniques need to consider media alterations in both a social and multimodal direction. Our analysis hence offers a conceptual understanding of a rapidly evolving field, were methods used within media intelligence need to change as well. By presenting some findings from the so called CIBAS-project, we describe how Swedish organisations and companies rely on social networking structures and individual decision making as a means to increase rapid response and agile creativity. If competitive intelligence was traditionally based on insights gleaned from statistical methods, contemporary media analytics are currently faced with audiovisual data streams (sound, video, image)—often with a slant of sociality. Yet, machine learning of other media modalities than text poses a number of technical hurdles. In this article we use fashion analytics as a final case in point, taken from a commercial sector where visual big data is presently in vogue.