Methodology
and the data source
Research design for this study had first to determine
the set of Raga-s to be considered. The
list of “Candidate” Raga-s was compiled by merging the under-graduate and
post-graduate syllabi of the major teaching universities and examining bodies
granting certifications/ degrees/ diplomas in Hindustani music. This list was
submitted for suggestions/ approval to a panel of musicians known for their
vast Raga repertoire and academic orientation. Through this two-tier process, a
list of 97 Raga-s was finalized, for which audience engagement measures were to
be computed.
For compiling the data, the YouTube platform was
searched for each Raga. The coverage of “significant” musicians performing each
raga was attempted as a census (100% coverage), and not as a sample. The assessment of “significance” utilized my
own knowledge of musicianship over the last century on the national as well as
regional levels. Considering the global scope of the enquiry and the diversity
of material, my judgement of “significance” could have been less than 100%
defensible. Care was taken to include significant musicians representing (i)
vocal music and all significant instruments (ii) Dhrupad, Khayal, and Thumree
genres (iii) contemporary/ fusion genres where recordings explicitly claimed
and performed Raga-based music (iv) musicianship originating in Pakistan,
Bangladesh, and Afghanistan performing Raga-based music (v) musicianship
originating in the US, Europe with credible performing attainments (vi)
musicianship originating from the Carnatic tradition but performing Hindustani/
shared Raga-s in Hindustani style.
Media researchers have found that YouTube on-screen data
is singularly unhelpful for systematic research. This would seem intentional because
unambiguous data can be used for competitive purposes (can help some musicians
at the cost of others). This can jeopardize YouTube’s impartial status as a
media owner/ platform provider, and damage its commercial interests. Having
conducted a major study in 2015 based on YouTube data, (Refer Chapters 15
through 20, in The Musician and His Art – Essays on Hindustani music: DK
Printworld, New Delhi 2019) I had developed a method of usable data acquisition
from YouTube. This method obliged me to personally create every one of the 8100
records for analysis. Despite utmost diligence in database creation, I am
unable to vouch 100% for the soundness of my results. YouTube’s evasive purpose is
fulfilled.
A notional limit of 100 data-points was envisaged for
each Raga. The data-base compilation was completed in the last week of March
2020 and the first week of April 2020.
The Audience Engagement Indicator has been computed as
follows:
YouTube as data source is far from ideal for
systematic research, and that I am using it in full awareness of its
limitations. Despite this, it is now being used by researchers even in the more
advanced research environments. Presumably because it has usable value, its use
will not stop; and because of its known limitations, the controversy over its
value will not end.
The perception of limitations will obviously be guided
by the researcher’s objectives – what he is looking for, and what he may
instead be measuring. My concerns are focused on the following infirmities.
YouTube reports “views” for each upload. The basic
question is: what is a view? What minimum duration of exposure qualifies a
“visit” as a “view”? The information displayed on the screen does not
distinguish between a 10% viewing of a video and a 40% viewing of it. The
difference matters to me.
The duration aspect of viewership is also connected to
a change in YouTube policy some years ago. There was once a duration limit on
uploads. This affected Hindustani music very significantly, as most
performances exceeded the limit, and had to be split into 2, 3, and sometimes 4
parts for upload. After the duration limit was lifted, mostly complete
performances were uploaded. Looking at that data today, we implicitly equate the
engagement measure of a concert split into 3 parts with that of a complete performance.
This seems unreasonable.
YouTube viewership data is cumulative from the date of
uploading. By using that data, I am implicitly accepting one view of 2010 on
par with one view of 2018. Intuitively,
we know the assumption is flawed. During this period, YouTube content has
changed substantially, viewership has grown exponentially, and audience profile
for every kind of content has almost certainly changed radically. No correction
factor can help.
Another problem with cumulative data is that it effectively
equates 20,000 views accumulated over 20 months with 40,000 views accumulated
over 40 months. Intuitively, this equation does not look reasonable. If the
propensity of a recording to accumulate viewers is important, 40,000 over 4
years is more valuable. And, if the speed of audience accumulation is
considered important, 20,000 over 20 months is more valuable. YouTube data is
unhelpful in this respect.
The YouTube audience is global, and so is the audience
for Hindustani music. But, we have no data on the geographical spread. By
implication, we are accepting that foreign audiences of Hindustani music and
foreign musicians —across all nationalities and cultures -- have the same
relationship with the music, as Indians have. This is unrealistic. The opacity
of the geographical spread can easily mislead us – as it has the possibility of
doing in this study.
YouTube viewership – by whatever criterion registered
– is a partially manipulated expression of audience engagement. This is because
a sophisticated program guides the viewer into “viewing” content beyond his
purpose, and spending much more time on YouTube than was necessary or planned. As
a result, the numbers we see include “incidental” viewership. A separate
reporting of the primary (search word) and incidental viewership would be very
helpful.
Although YouTube is a video medium, the nature of the
content is itself not uniform. In fact, even the notion of “viewership” may be
irrelevant to a lot of the content. Do 100 people listening to an audio
recording with just a photograph of the musician on the screen represent the
same level of audience engagement as 100 people watching him or another
musician in action on film? If not, how much can we depend on a standard
measure of audience engagement across different content formats?
With specific reference to Hindustani music, YouTube
neither offers a uniform media experience to its audience, nor publicly
provides a rigorous measurement of audience engagement. What, then, does YouTube
data provide? It cannot be said to provided “statistics”, but can suggest
“orders of magnitude”. Inferences can be drawn judiciously from its analysis,
with every inference reflecting the analyst’s awareness of data limitations.
YouTube has invested heavily in generating analytics
for user management and advertising value assessment. At some stage, it will
have to start understanding itself as a cultural force. It will then benefit by
cultivating communities of media researchers through a more transparent stance
with respect to its data assets.
Until this happens, the Indian musicologist should be
content with indicative inferences. Is this better than the “Delphi method” of
polling 10 veteran connoisseurs and observers of the music scene? I believe so
because, firstly, surveying this population globally is an almost impossible
task, and secondly, present-day oracles – whether of Delphi or anywhere else --
are susceptible to "personal" preferences, biases and prejudices;
impersonally generated numbers are not.
Concluded
