using human filters to make social media data useful

Post on 17-Jan-2015

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(NB. download it to read the notes which explain each slide) This is the PPT of a talk I gave at Starcom Mediavest during Social Media Week London 2013. It relates to the challenges of using Twitter data to mine useful information and how a network intelligence model, i.e. defining a set of relevant people against a specific topic, makes the data you extract more useful. It debunks the notion that social media derived "Big Data" can be useful without extremely complex maths and analysis, but demonstrates how even simple data samples when filtered by a group of people you've decided to track in advance, and keyword filtered to only look at their posts when relevant to a specific topic, can be extremely insightful. It relates to news, public opinion and using a rich data source without getting lead astray by faulty maths or overblown statistical assumptions.

TRANSCRIPT

Making social media data useful with human filters

Hello I’m Andrew Walker from @Tweetminster

www.electionista.comwww.tweetminster.co.uk

Social media is a rich source of data – but

it’s useless until you define basic human

filters to explain it

See, I’m the fire hoseAnd everybody knowsThat I’ll knock you downAnd kick that ass

Without human filters, social media data is thermal data, i.e. a theoretical average

Temp = 10°

That means doing anything with that data is more complex than that… because the relationships the data describes are more complex than that:

Temp =

Within the firehose we look

for influencers, but influence is a

self-referencing paradox.

It’s numberspam

UKIP Influences mainstream politics…

… but lacks political influence

Do we mean fame or influence?Depends on the context….

And who really cares anyway?

It’s Heisenberg’s uncertainty principle…

…there’s a fundamental limit to the precision of understanding complimentary variables.

(Sort of)

…until you apply some

simple human filters to your

sample and intent

Human filters let the data ask and answer the questions for you…

Q: what was the biggest event of the German election campaign?

Human filters show patterns than enable strategic planning…

Q: How does the electoral news cycle differ from the daily political news cycle?

Human filters explain local social factors…

Q: What’s the temperature of politics in Australia vs. Germany

Human filters define different categories of the same thing…

Q: Is politics all the same? What kind of election is this?

Official Party 13%

Gov. Depts 14%

News Media 16%

News Media Blogs 16%

Blogs 19%

Campaign / NGO 22%

Official PartyGov. DeptsNews MediaNews Media BlogsBlogsCampaign / NGO

Human filters explain the nature of influence in context e.g. the quality vs. quantity paradox

Number of shares per article by source = quality / influence

Official Party 4%

Gov. Depts 13%

News Media 39%

News Media Blogs 13%

Blogs 23%

Campaign / NGO 9%

Number of mentions per source = Variety & volume, not influence

but it works

It ain’t Bieber

Thanks

The most important part of social media is the social

bit, so if you want to chat…

I’m @killdozer

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