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EXPLORING REALITY TV DATA Roddy Cowie & Cate Cox Why reality data? How real does it need to be? We want to look at emotion in action and interaction Not episodes contrived to produce ‘pure’ emotion, because most of the time, emotion shares a person’s head with other constraints (plans, demands, etc). There is no guarantee we will recognise emotion as it appears in ‘headshare’ mode by collecting samples of pure emotion Unfortunately, we don’t know very much about emotion as it appears when people are engaged in ‘natural’ action & interaction. So, this end of the project is about situations where emotion appears in action and interaction. How we have begun to look at reality TV data What we have been looking at: Approximately 5 hours of pre-edit footage from a television series called Castaway 2000 The TYPE of data can be described as complex, within the terms of the exemplar, i.e., Definitely provocative – forces one to expand and restructure thinking about data, Definitely falls within the WP5 remit of emotional behaviour in the type of contexts which normally surround emotion in everyday life, i.e., in interaction with other people and in the activities which we conduct in a normal day Without attempting to describe or label the data/footage at this stage, in any particular way, purely through prima facie observation the data/footage falls into 5 broad categories so far: 1. Personal individual interviews with candidates, on their own, being interviewed by a production crew member Demo1.mpg 2. Interviews after having been through a fairly traumatic challenge Demo2.mpg 3. Group interview footage, where the group is addressed, where an individual is focused on the context of the group, where an individual is removed slightly away fro the group Demo3.mpg 4 Group interaction 5. Field challenges Demo3a.mpg Demo4.mpg Demo5.mpg Demo5a.mpg How we have begun to describe/approach the data Basic approach at this stage: let the footage ‘speak for itself’ - suggest categories/classification BUT we need to set up ‘filters’ so that the messages are possible to handle ‘just listening’ pulls you all over the place. (Hansen – all data are theory-laden – the trick is to get a decent match) Develop a systematic approach without loading it too heavily with preconceived notions of verbal categorical labels. In mind of the exemplar - developing new labelling techniques in tandem with new data, without yielding too many labels or developing in an ad-hoc or un-coordinated way. Trying to ‘unpack’ the process a little, initially by adapting the dimensional approach in tandem with a coarse to fine approach – getting coarse description allows you to get to know the data and to focus in a systematic way on the informative parts. A broad dimensional approach As a first step, we are looking at the data without trying to classify or describe an emotion, but simply to establish whether there is any emotionality there at all. We start with an adaptation of Feeltrace which presents one dimension only: E-Trace Definitely Absent vs Definitely Present There are some subtleties in here – getting a scale which is intuitive to use. Definitely Absent Definitely Present Definitely Absent Definitely Present not edge towards edge towards yes not What kind of information does this give us? low incidence of unequivocal emotionality clear absence of emotionality is equally rare we see where to look for emotionality (eg long section at the end) and how it is distributed (different rhythms in different episodes) R 1.0 0.5 0.0 1 1415 2829 4243 5657 7071 8485 9899 11313 12727 14141 15555 16969 18383 19797 21211 22625 24039 25453 26867 Fig 1. Graph Representation E-Trace Tape 1_1 0.3 0.2 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Fig 2. Graph Frequency Distribution E-Trace Tape 1_1 Upper limit of confidence category 1 t The exercise has already thrown up some other dimensions that are critical: Is ‘it’ discernable Contextual Issues yes/no which is a different question to whether emotionality is there or not, you may feel that it’s there but it’s not clearly discernable Coarse to fine context coding starts to emerge Group/non-group yes/no group dynamics come into play which seems to lead the interaction into a different area. Other Dimensions being considered as they have emerged (so far) from the data, whether emotionality is: overt vs masked mixed vs clear genuine vs fake ………. More Questions… There are still many questions that will arise as we continue to explore this type of data: What to do with the data? Physically separate clips (BT database model)? – or Data Mining & Extraction mechanism (Like Ferret)? Bearing in mind that this kind of naturalistic data shows emotional behaviour that may not be discrete, constant or of short duration. So, we are exploring how to explore.