Tweetdeck as a tool for Grounded Theory Research in Twitter

Next Friday I’ll give a lecture to the DigitalMethods PhD Course in Milan. It will focus on some ongoing reflections on the use of the Twitter browser Tweetdeck as a tool for Grounded Theory Research within Twitter, with Folksonomies as a self-coding system.

Here a quick cut & paste from Wikipedia:

Grounde Theory: “Grounded theory method is a research method, which operates almost in a reverse fashion from traditional research and at first sight may appear to be in contradiction to the scientific method. Rather than beginning with a hypothesis, the first step is data collection, through a variety of methods. From the data collected, the key points are marked with a series of codes, which are extracted from the text. The codes are grouped into similar concepts in order to make them more workable. From these concepts, categories are formed, which are the basis for the creation of a theory, or a reverse engineered hypothesis. This contradicts the traditional model of research, where the researcher chooses a theoretical framework, and only then applies this model to the phenomenon to be studied.”

Tweetdeck: “TweetDeck is a social media dashboard application for management of Twitter and Facebook accounts. Like other Twitter applications it interfaces with the Twitter API to allow users to send and receive tweets and view profiles.”

Folksonomy: “A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content;[1][2] this practice is also known as collaborative tagging,[3] social classification, social indexing, and social tagging. Folksonomy, a term coined by Thomas Vander Wal, is a portmanteau of folk and taxonomy.

Folksonomies became popular on the Web around 2004[4] as part of social software applications such as social bookmarking and photograph annotation. Tagging, which is one of the defining characteristics of Web 2.0 services, allows users to collectively classify and find information. Some websites include tag clouds as a way to visualize tags in a folksonomy.[5] A good example of a social website that utilizes folksonomy is 43 Things.

An empirical analysis of the complex dynamics of tagging systems, published in 2007,[6] has shown that consensus around stable distributions and shared vocabularies does emerge, even in the absence of a central controlled vocabulary. For content to be searchable, it should be categorized and grouped. While this was believed to require commonly agreed on sets of content describing tags (much like keywords of a journal article), recent research has found that, in large folksonomies, common structures also emerge on the level of categorizations.[7] Accordingly, it is possible to devise mathematical models that allow for translating from personal tag vocabularies (personomies) to the vocabulary shared by most users.[8]

Other lecturers will be Ivana Pais (Identifying networks, communities and influencers), Alessandro Rozza (Machine Learning) and Alessandro Caliandro (Finding data).


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