Encyclopædia Britannica defines data mining as “knowledge discovery [through] the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence with database management to analyse large digital collections, known as data sets.” Or put slightly differently, the term describes the process of extracting insights and knowledge from large data sets, that is the processing of information, e.g. available and extracted (mined) from social media platforms.
We will soon provide you with more on this topic, but until then, check out this introductory article by Chen et al. (1996). Also, check out the SIGKDD, the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining, and its publications on data mining.
By Julie Uldam
Drawing on ethnographic methods such as participant observation, netnography was coined as a methodological term by American professor of marketing Kozinets during his thesis work in the mid 1990s. Netnography has been most prominent in consumer and marketing research, examining consumer preferences as they are expressed in bulletin boards and social media platforms such as Twitter (Arvidsson and Caliandro, 2016; Kozinets, 2002, 2011). However, netnography has also been adopted in other fields such as media studies where Postill and Pink (2012) have developed the approach so as to sensitise it to ‘digital socialities’ and the interplay between the online and offline in activists’ uses of social media platforms.
Netnography is arguably distinct from related digital methods such as digital ethnography and online participant observation in that it provides a particular framework for analysis (Snee et al., 2016, see Hine 2000 for virtual ethnography as an example of another framework with particular procedures and focal points), including ethical reflections on covert and overt research (see Uldam and McCurdy for a discussion of covert and overt participant observation in online and offline contexts). The adaptation and development of netnography demonstrates the usefulness of the (developed) approach for uncovering the dynamics of interactions between different societal actors, facilitating research beyond the confines of media-centric approaches and a focus merely on technological affordances. These potentialities of netnography makes it a useful approach for studying the role of digital media in strategic communication, especially when strategy is seen as an on-going process influenced by multiple actors as in Guldbrandsen and Just’s perspective. However, further development of netnography is necessary in order to sensitise the approach to the analytics of the power relations that underpin the possibilities for different actors to influence communication, online and offline.