WHEN IS SOCIAL MEDIA MINING GOOD ENOUGH?
OR
HELP! I THINK I MIGHT BE A SCIENTIST.
Nick Buckley
Social Media Director GfK NOP
1
1. What are we talking about?
2
What exactly are we talking about?
Definition* of social media monitoring:
“Social Media Monitoring (SMM) means the identification, observation, and analysis of
user-generated social media content for the purpose of market research.”
News sites
Forums
Review sites
Professional & Consumer
Client sites
Blogs/
Microblogs
Video sites
Public
Communities
What they say
* http://www.social-media-monitoring.org
3
What was that 2.0 thing again?
The “era of shout marketing” is over*:
Before the rise of the internet
Web 2.0
Eh?
* Marshall, 2012
4
Web Mining, Social Media Monitoring or Social Media Mining?
I like “Mining”. User generated content in social media lays down a rich seam of activity,
opinion, thought and information… mess, echoes and ‘whimsy’.
For some time marketing and PR professionals have been
monitoring Social Media to capture headline ‘buzz’ in real
time, and to detect sudden changes requiring a response.
But collecting and counting this content is only the
beginning of a process which can add value via many
techniques… including integration with other sources
such as market research data.
5
Rapid supply-side evolution. What has driven it?
For the original PR and Marketing Users…
•
Boring outputs – flat lining “buzz share”
•
Commoditisation [seeming] of the core process by technology
newcomers
•
Differentiation by interface… the “Dashboard” – to emphasise
use-cases
•
Making user self-service easier – for all kinds of reasons
•
Increasingly sophisticated users… looking for outputs
suggestive of insights
•
The ‘social CRM’ branch
http://blog.glennz.com/evolution/
6
2. What happens when Market Researchers get
hold of it?
7
Sony brand damage was driven by PlayStation breach
(2011)
sony buzz this year
sony sentiment this year
sony buzz in april
sony sentiment in april
playstation buzz
playstation sentiment
8
Market Researchers believe that SMM can also give clients a
window on other dimensions of online conversations
SMM provides insights into:
• Category Dynamics
 Consumer needs
 Problems and issues consumer discuss
 Product usage discussions
 New product entries
• Corporate
 Corporate mentions related to reputation
 Crises
 Social issues
• Brand
 Brand/sub-brand mentions, brand “buzz”
 Number of positive vs. negative sentiments for
each brand
 Brand content analysis, what’s being said
about brand
 Advertising noticed most and related
discussion
 Source of mentions (specific sites.) and the
most influential sites
• Competition
 All the above for competition
9
Inevitably they think about comparison with surveys…
Strengths
•
Very immediate
•
Unconditioned by participant awareness of a
research process Often more emotive than
considered survey responses
•
Spontaneously generated content unconstrained by research frame.
•
Offers insight into active social media users
•
Potentially global
•
You can ‘ask a new question’ without having
to issue a new questionnaire*
•
Low cost – under certain circumstances
© 2012 GfK NOP
Weaknesses
10
•
Not necessarily representative of the general
population
•
Difficult to weight back to general population,
as demographic data is sparse
•
Automated sentiment analysis only as good
as the algorithms [and these vary greatly]
•
Automated harvesting can capture a lot of
‘noise’ for certain words or brands
•
No guarantee of sufficient data
•
Costs rise when we use supplementary
analysis to overcome some of these issues
*within certain technical limitations
Different client needs indicate different SMM approaches
For example - Precision Extraction vs ‘Trawl & Filter’
Quantitative Brand tracking
and integration
with traditional
research
More post
processing,
applied to
data by MR
agency - to
reduce noise
and refine
sentiment
attribution
Indicative Qual
Exploratory Qual – more
e.g. using trends and
volumes to guide focus of
analysis
complex collection. Manually
manageable volumes and ‘tuning’
Lower data volumes
Higher data volumes
from targeted & compound search terms
© 2012 GfK NOP
Crude
mention &
mood tracking
from simple search terms
11
Accept
raw data
output
from
application
3. Too Abstract?
12
The raw material - Results from search terms
 SMM applications extract results from wholesale supplies of
data, conducting searches defined by “search terms”
• These can be anything from a simple and distinctive brand
or product name, to a complex expression configured to
capture discussions about a category or concept.
• A search term combines words or phrases via logical
instructions such as AND, OR, NOT. They may also employ
functions such as WITHIN to detect words in a certain
proximity to each other. Finally – just as in mathematical
equations – brackets can dictate the sequence in which
the instructions are applied, e.g.
• “word1” AND ( “word2” OR “word3” )
13
Typical SMM application offers a dashboard view of data returned by these search terms
– and the facility to export the underlying data
14
Analyses
Whatever the Search Terms define – here is what can be measured about the results
returned… in combination or in isolation
Volume – “how much is it
talked about, and how is this
changing over time”
Location – “where in the
Channels – “where on
the web is it being talked
about… twitter, blogs,
forums, comments?”
world is it being talked about?”
Verbatims - drill-down to individual
posts, in their own words – “what do people
actually say?”
Themes – “what other
words and phrases are most
regularly associated with it?”
People – “who is talking about it?” That may be by
influence – according to various proprietary indices – or by
demographics [to be used with caution]
Sentiment: Across all of these variables is superimposed automatically generated “Sentiment” analysis
– positive, negative or neutral language associated with the subject of the posts…
15
Examples of outcomes from SMM studies
FINDING: Focus on the right social media channels at the right time. A manufacturer
used a video from a high profile pop star to drive a major campaign. Predictably, when aired,
the video generated a ‘spike’ of twitter activity. BUT – looking back down the timeline showed
there had also been a burst of activity on forums, and some blogs, from fans of the artist when
the video was being shot.
FINDING: Differentiate ‘trade press’ buzz from real engagement. A manufacturer used a
novel approach, through Facebook, to support advice and collaboration between users of its
product. This appeared to have some success in stimulating social media conversations about
the product. However – deeper scrutiny revealed that this traffic was almost exclusively
blogging by sector and marketing industry press, attracted by the novel approach, with further
blog, forum and link-tweeting activity amongst sector insiders and social media enthusiasts.
16
Examples of outcomes from SMM studies (2)
FINDING: Consumers don’t always talk about the product features that you highlight. Analysis
of conversations about a newly launched electronics product revealed that the functional features
most discussed [particularly those with largely positive sentiment attached] were not those which the
manufacturer had chosen to highlight. Subsequent marketing was able to adjust to take account of
these ‘more loved’ features.
FINDING: ‘The world’ can sometimes throw up more interesting stories about you than you
could hope to generate for yourself… but not always with the connotations you would like. An
automotive manufacturer which had enjoyed modest online buzz as a result of its own sponsorship
activities experienced a ‘spike’ in online mentions which was 10 times the size – as a result of a
much repeated witty comment. A high profile celebrity had appeared on TV news being interviewed
from the drivers’ seat of one of their vehicles. The comment – linking the celebrity to a negative ‘folk
image’ of the vehicle – spread rapidly across a range of social media channels. The moral is that
spontaneous, and genuinely social, media can currently still outperform marketers.
17
BUT!
18
There are many forces* which erode this nice model…
Accuracy?
Reach?...................................................
Relevance?
Reach image from titletrack.com
19
Accuracy
Is the searched-for phrase even in the returned “snippet”?
Is it ‘content’ – or is it
• Navigation?
• Ticker or title content?
• Ad Content?
• Various species of spam [overlaps with ‘Relevance’]?
Is meta-data about the poster
• Present?
• Reliable?
Understanding this, apart from making your own manual checks, is about understanding your third party
suppliers’ processes and content and, often, that of their ‘wholesale data suppliers’ – each of which may
differ from the others.
20
Reach
[T]here are known knowns; there are things we know that we know.
There are known unknowns; that is to say there are things that, we now know we don't know.
But there are also unknown unknowns – there are things we do not know, we don't know.
Donald Rumsfeld
•
•
•
•
•
•
Are these results from scrutiny of the entire [English speaking] social web
Are they results from a very large, sometimes stated, number of social sources?
Could this range be skewed relative to the subject under scrutiny?
Where it’s Twitter data – is it from the whole of Twitter
Is historical data always the same basis as current data,
or data gathered since the search was defined?
Do we always have a good idea of what the ‘Reach’ is?
No
Yes
Yes
Maybe
Not always
No
21
Relevance
Even when the application has collected exactly what we asked for, and it is legitimate
content, with some nice useful data about the poster… it might not be relevant
“Cats are great company.”
“#EMT Bolt one cool cat!”
“Also, the Cat is a great resort”
“I love my aunt Cat!”
“I think Cat Stark is worse than any Lanister.”
“I think this hurricane was a scam cooked up by the fat cats in Big Grocer.”
22
Challenges include
However , commencing too early public smoking facts will just overstress your
pet ; quite a fresh pet will not learn everything from services. Just after he has
ended up perched for some a few moments, supply him with the particular
take care of, plus for instance in advance of, make sure you compliment the
pup. When dog house teaching your dog, continue to keep the dog house in
the vicinity of the spot where you as well as the canine are usually conversing.
23
And I haven’t mentioned automated Sentiment Analysis yet!
Irony – really?
Slang/Dialect/Register
Multiple meanings – “50 strong”
Adjacent subjects – “My beautiful FIAT next to a BMW”
24
4. And what is Good, and what is not Good?
25
To Recap
•
SMM tools make it very easy to “Super Google” certain Brands, people, objects and even
categories or concepts – quickly generating convincing-looking tables and charts.
•
But underneath there’s a complex story about accuracy, reach and relevance… which
becomes apparent on scrutiny of drilled-down text samples – and can only fully be
understood by getting inside the provider’s systems and sources.
•
It doesn’t mean they are misleading users – it just means that they started out somewhere
else.
•
The conclusion is that you have to carefully consider use cases, or build your own better
mouse trap, or wait for proprietary solutions to get better at certain things
•
Sentiment analysis is part of this story – but doesn’t define it.
26
Natural Language Processing [NLP] to the rescue?
Definition
“Specifically, it is the process of a computer extracting meaningful information from
natural language input and/or producing natural language output”*
Most SMM applications claim some level of NLP.
Whilst this may be legitimately contrasted with simple
vocabulary, combination and probabilistic methods, it
can end up meaning little. It may only mean that
some rules of language have been ‘attended to’ in
what is still essentially a pattern-matching exercise
*Warschauer, M., & Healey, D. (1998). Computers and language learning: An overview
27
But clearly sophisticated NLP would make a big
difference
• Improved Accuracy – including filtering out of unstructured spam
• More tools available to achieve/check Relevance
• Much-improved Sentiment Analysis
Some commercial tools have become available in the last 12 months which offer an
assessment of their confidence in their own NLP analysis – dividing snippets into
those with Low, Medium and High confidence.
Significantly, ‘High’ is a minority of the output.
28
Barking up the wrong Tree?
The recap assumes that the Market Researcher’s instinct is correct… to make the fuzzy
working of the social web itself… the collection mechanisms and enterprises, and the
analytical engines… into a familiar data collection process, somehow isomorphic with surveys.
But “what is good” is, as many of the ancient philosophers would tell us, about
function and purpose.
I think we’ve now learned enough,
•
and experienced enough un-straightforwardness
•
and contemplated enough need for manual evaluation or augmentation - dispelling the
notion that this is a self-evident labour saving device along the way…
to stop and ask, “what
was it we were trying to do?”
29
To Recap
•
SMM tools make it very easy to “Super Google” certain Brands, people, objects and even
categories or concepts – quickly generating convincing-looking tables and charts.
•
But underneath there’s a complex story about accuracy, reach and relevance… which
becomes apparent on scrutiny of drilled-down text samples – and can only fully be
understood by getting inside the provider’s systems and sources.
•
It doesn’t mean they are misleading users – it just means that they started out somewhere
else.
•
The conclusion is that you have to carefully consider use cases, or build your own better
mouse trap, or wait for proprietary solutions to get better at certain things
•
Sentiment analysis is part of this story – but doesn’t define it.
30
What are we trying to do?
• Use the social web as a proxy for the population?
• Understand how the social web is responding – for
the benefit of those solely interested in this sub-set
of the population as a channel or marketplace?
• Access particularly niches which are more
concentrated online than off?
• Detect significant events?
• Measure shifts and changes?
• Make rough comparisons?
• Discover new insights, themes and connections?
31
How useful is extracted Social Media content?
Mechanically extracted content is inevitably imperfect as regards:
• relevance
• comprehensiveness relative to ‘total web’
• accuracy of classification, sentiment etc
• representativeness of general population
It’s important to know when this matters, and how much. It is vital to work honestly with the constraints and
exploit the strengths…
In general web mining is
therefore useful for:
•
•
•
•
•
relative measures
measuring and detecting
change or discontinuity
iterative discovery of related
concepts and drivers
comparing channels
matching to events and
schedules
…and, of course,
integration with other
sources of data.
32
Different client needs indicate different SMM approaches
For example - Precision Extraction vs ‘Trawl & Filter’
Not radical
enough!
Sensible
Exploratory Qual – more
complex collection. Manually
manageable volumes and ‘tuning’
Lower data volumes
from targeted & compound search terms
© 2012 GfK NOP
Quantitative Brand tracking
and integration
with traditional
research
More post
processing,
applied to
data by MR
agency - to
reduce noise
and refine
sentiment
attribution
Indicative Qual
e.g. using trends and
volumes to guide focus of
analysis
Crude
mention &
mood tracking
Too much like
hard work Higher data volumes
from simple search terms
33
Accept
raw data
output
from
application
Rather than wait for NLP utopia…
Settle for:
1. SMM as a powerful and novel Qual exploration tool
2. Do big number crunching on brands but take a
“hyena” approach.
Accept all* occurrences of a brand or product name in posts as an
indication of significance… even the spam and the adverts and the
competitions
Similarly look for pure correlations between words/phrases and
other word/phrases
Or between trends in these numbers and classes of offline events –
such as sales, complaints and other behaviours… with a view to
predicting, explaining or causing such events in the future.
*Except for the most obvious duplication errors such as over-indexing
34
5. Some Conclusions
35
I am not a scientist
OK – I’m a scientist amongst researchers, and possibly amongst programmers
But amongst scientists – and text analysis specialists – I’m a mere researcher.
Because I couldn’t use these tools “as is” with confidence I had to start delving…
… and delving is time consuming in a commercial environment.
Our technology suppliers have become more like partners… increasingly transparent as they’ve understood,
but not challenged, what we tried to do. The software and services will now adapt to us – whether they
should or not.
PR monitors, real time trackers and ‘social CRM’ folks will carry on using the tools the same way they
always have… and may even benefit from changes my industry has now initiated.
36
But
How will commercial SMM applications and services with the best accuracy, reach
and relevance capabilities be recognised, validated and promoted?
Is the ‘bit in the middle’ just a holy grail until such time as the NLP part of the
reckoning makes a step change – driven by all its other exploitations, such as ordinary language
driven IT interfaces.
If you’re a researcher and you want to use this stuff tomorrow… what must be
done?
Fortunately – there’s enough to learn by “super-googleing”, browsing and crude
trend tracking to keep us going… and learning… for some time to come.
37
38
Dr Nick Buckley
Social Media Director
GfK NOP
M: 07958 516967 T: @grimbold
E: nick.buckley@gfk.com
[from August 2012. E: nick@soshall.net]
39