Today, more and more physical communities of practice, a concept that describes a group of people that share a passion and interact regularly at events to exchange knowledge, utilize social media, such as Twitter. Brotaru, for instance, is such a physical community of practice for media professionals in Brussels. It is a monthly meet-up of videogame developers in various locations in Brussels. Furthermore, Twitter becomes widely acknowledged as important instrument for learning and community formation in the virtual world. But, do these communities of practice use Twitter only to promote their physical activities of learning? Or, are the activities of the physical communities further extended into the virtual world meaning that virtual communities of practice emerge from them? This article suggests a novel mixed-methods approach based on qualitative and quantitative data to measure the role of Twitter for physical communities of practice. The method applies different statistical measures and analysis on harvested Twitter data and additionally brings two of the most used methods in Twitter analysis together, social network analysis and text data analysis (a.k.a., content analysis). Four different communities of practice in Brussels' media industry and their activities and followers on Twitter have been analysed. The findings showed that the activities of the communities of practice extend into the Twitter sphere as the online communities are characterised by (1) a shared domain, (2) a lively community and (3) shared practices. The analysis further revealed that Twitter offers three main opportunities for the activities of communities of practice: it offers geographical extension; it gives temporal autonomy; and, it can be used to diversify the practices.
A push by governments to foster the development of local media clusters is currently taking place. This newly emerged focus is responsible for cities, regions, nations and scholars investing in better understanding media clusters. Yet, there is no common method for mapping the phenomenon in terms of key variables. This article proposes a framework in which empirical observations as well as theoretical considerations can be placed to fill the gap. The framework consists of seven parameters: place, proximity, population, profile, path-dependency, policy and performance. These parameters unite several heterogeneous approaches towards media clusters into one common analysis framework.
Is the clustering of audiovisual companies in London's Soho really the same as the clustering of Berlin's new media industry? The media cluster approach has gained a lot of attention not only in academia, but also in political discourse. But, as appealing as the media cluster concept is, one of the most fundamental issues is the comparability of the phenomenon. This article tackles this issue and an analysis of 43 case studies has been conducted. The case studies have been grouped to find a new typology for media clusters. The research revealed six different types: The Creative Region, the Giant Anchor, the Specialized Area, the Attracting Enabler, the Real Estate and the Pooling Initiative. The typologies showed that they distinguish especially in their geographical scale and specialization in media activities, while at the same time cluster types can be found in the same area. They are driven by four rationales: agglomeration, urbanization, localization economies and artificial formation.
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