At the moment I am struggling with a theoretical question. Maybe some of you could help me out.
I'm trying to make social network images out of video data in order to visualize the interaction of team members during team meetings. The data looks as follows (each row represents an utterance) :
Now here is my question: is it fair to assume that the person that is speaking after someone else has spoken (so say the person in the second row) is the person to whom the utterance is directed? If yes, are you familiar with literature/empirical papers that say/do so? If not, same question?
I am very interested in your professional opinion on this matter.
Hildert:
If the person comes after the very first person who spoke, then you can probably make that assumption, but it would be a stretch to do so if the person is peaking after several persons spoke. The latter is my personal opinion as I have not done a lot of work on social network analysis, so I have attached the link to related RG studies that I hope would be useful to you. I extend best wishes for every success in your research.
Many thanks,
Debra
https://www.researchgate.net/search.Search.html?type=publication&query=social%20network%20analysis&tabViewId=571673ee217e202ed5513aa1&previous=researcher
https://www.researchgate.net/topic/video_analysis?ev=tp_pst_dtl_xkey
I recommend reading this document "Who Shall Survive ? " foundations of Sociometry , group psychotherapy and sociodrama , J. L. Moreno , M.D.
Or consider using an ANFIS ( adaptive neuro-fuzzy inference system), fuzzy logic to detect the degree of interaction.
Also put your data set has adjacency matrix whose nodes represent individuals and indicate by weights on the links the level of interaction between each node. You can use PYCX (http://pycx.sourceforge.net).
I hope I have been helpful.
If you have time, you can look at my article "The importance of networks for newly qualified teachers in upper secondary education". In
Educational Research 56 (4), 453-472
Unfortunately I have no experience with analysis of video recordings of network activity. My bee the book "Analyzing sosial media network with nodexl", by D.L. Hansen & B. Shneiderman (2011) can help you.
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