Rationale for Using Video Analysis
I chose to analyze dance videos, in the quest to identify the key movements of Egyptian raqs sharqi, that remains roughly constant, in spite of stylistic and performance context changes.
This search touches on the issue of authenticity on the level of form, as discussed in the authenticity section 2.5.
Moreover, as the videos available online span from the late 1800s to the contemporary period and are performed by people belonging to different cultures, in different settings and places, my aim was to create a panoramic of raqs sharqi across time and cultures, to see how it has changed in terms of movements, feelings and according to the styles of individual dancers.
Hence, the idea was that analyzing dance videos would shed light on the issue of transmission and the dialectic between tradition and creativity.
Heath et al. (2010, p. 2) posit that video ‘records can be subject to detailed scrutiny. They can be repeatedly analysed’.
Thus, the videos I have analyzed provide a record of props and costumes and how they change over time; what social spaces the dancer was performing in and a lot more.
Dance videos were the core source for the dance analysis, because they include choreological, embodied and socio-cultural elements, all of which, if analyzed, contribute to the creation of a holistic picture of dance/heritage. As Pink (2006, p. 4) argues, ethnography ‘should account . . . also for objects, visual images’.
Advantages and Limitations of Analyzing Dance Videos
In dance research, it is commonly accepted that videos for the study and documentation of dance have both advantages and limitations, as they can never replace live performances, but have great documentary value (Blacking, 1983; Adshead, 1988; Desmond, 1997; Preston-Dunlop and Sanchez-Colberg, 2010).
Preston-Dunlop and Sanchez-Colberg (2010, p. 125), for instance, argue that ‘video recordings have their own syntax and cameramen [sic] do not have an impartial eye . . . the video is a record of one version of the work, dance by one cast, in one interpretation, on one occasion, filmed by one cameraman’.
Moreover, according to Blacking (1983, p. 92), videos and other forms of notation ‘cannot describe or explain what is happening as human experience’.
Whilst acknowledging these limitations, I found online videos invaluable to explore raqs sharqi as a form of heritage.
These videos provided me with a wide range of performances, from a variety of dancers, across a huge spatiotemporal spectrum, thus allowing me to start tracing a history of the dance.
Moreover, the way of recording and transmitting the dance through technological devices (many videos were originally filmed for cinema or TV, before being shared on the Internet) shows the influence that technology can have on heritage safeguarding.
There are over a million bellydance videos online (2,640,000 results for the term ‘bellydance’ at the time of writing), mainly on YouTube and Vimeo.
They range from videos dating back to the late 1800s (very few) to the present and feature dancers worldwide.
The first decision was to focus on Egyptian raqs sharqi performances (either by Egyptian or non-Egyptian dancers), rather than other bellydance styles/genres.
The dancers in the videos had to be famous worldwide between raqs sharqi practitioners, as they would be the most likely to have influenced other dancers across generations and geographical locations.
My sampling strategy for the videos was, therefore, purposeful.
As Cohen (2007, pp. 114–115) explains, in purposeful (or purposive) sampling ‘researchers handpick the cases to be included in the sample on the basis of their judgment of their typicality or possession of the particular characteristics being sought’.
The majority of dancers I chose for my search were Egyptian and a few from other countries (of these, some who lived and performed in Egypt). I identified who the most famous and influential were in three phases:
- Based on my knowledge, having being involved in raqs sharqi for 14 years to the point of starting the data collection.
- Researching websites about raqs sharqi to see which dancers’ names appeared more frequently.
- Using the Google AdWords Keyword Planner tool to assess which, out of the dancers I had identified in the first two phases, were the most searched for online. This tool allows the user to see how many times, on average every month, a keyword has been entered in Google searches. I set the options to ‘all languages’ and ‘all countries’ to have an idea of dancers’ popularity globally. The results of this search are not necessarily significant on their own, but, if triangulated with data from interviews and from raqs sharqi written sources, can be significant (indeed, the dancers who were most frequently mentioned during the interviews, are also in the top 10 in this search).
Most Searched Dancers on YouTube
As a result, I listed 54 dancers’ names.
The top three most searched online globally were, in order:
- Fifi Abdou
- Samia Gamal
- and Dina Talaat
- Other Egyptians in the top ten were Nagwa Fouad, Randa Kamel and Naima Akef.
Searching for Belly Dance Videos
The next step was to search these 54 dancers’ names online (I used Google, YouTube and Vimeo) to find their videos.
As I found videos and as I was also discovering other online material about raqs sharqi, though, new directions emerged for the video research.
Thus, dancers who were not initially in the list of 54 were added and some of the initial 54 dancers were not included in the analysis, as it became clearer which dancers would be more or less relevant, based on my purposive sampling strategy.
The final list of dance videos I watched and analysed consisted of 1,028 videos. In 4.6.1, I will explain how I analysed the videos and the dance.
Youtube Algorithm Impact
A final consideration with regards to sampling for the videos I searched for using YouTube, has to do with how YouTube itself influences the results of the search when looking for videos and, therefore, how this impacts on sampling.
Sampling was influenced not only but my strategy and decisions, but also by what I was able to find online and the way in which Google and YouTube (which belongs to Google) work.
As Pietrobruno (2014) notes, YouTube runs algorithms that interact with users, as they upload videos, add tags, like videos, post comments, in a recursive process.
These algorithms influence the visibility of videos and will have influenced the ways in which YouTube responded to my queries.
This raises the question of what type of sampling strategy this human/machine interaction is.
I would suggest that this could be identified as ‘virtual snowball sampling’. Baltar and Brunet (2012) use this expression for their research, which employed surveys administered via Facebook.
They searched for Facebook groups in which they could find their target population (Argentinian business people living in Spain), contacted these people via Facebook and then asked the people they contacted if they knew somebody else who was interested in taking part in the research (either online or offline).
I propose though, that the expression ‘virtual snowballing’ should be extended to include not only interaction with humans in the virtual world, but also with machine-generated algorithms, given their role in guiding how we find information and how we follow leads online.
A practical consideration (which applies to all online material), is that videos that are available one day may no longer be there the next. In some cases, users choose to remove videos or to close their channel.
Often though, particularly on YouTube, accounts are closed forcibly, if a company alerts YouTube of copyright infringements (this was the case, especially, for some of the dance scenes from old movies).
Some of the videos I first analyzed unfortunately disappeared from the web. Hence, it is essential to keep a record of the videos before they are removed. I will discuss this issue more in detail in 4.5 and in 7.3.
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