The Future of Social Media Research!Or how to re-invent social media monitoring in 10 steps Francesco D’Orazio @abc3d | PulsarPlatform.com

legacy!Since it emerged 15 years ago, the industry has been largely responsible for driving some of the most interesting evolutions in the research space, such as the democratization of text mining and computational approaches to mining qualitative information.

qual/quant!blurring!

This is an approach that for the first time enables both a granular and a birds-eye view of the data, making it possible to produce qualitative observations on a mass scale. A new perspective that is blurring the lines between qualitative and quantitative thinking.

a new epistemology !

And with computation also comes the ability to mine larger (and messier) datasets, which is in turn steadily shifting the focus of what we call knowledge, from understanding causation to identifying correlations.

Social media monitoring is a growing industry but one that is stuck in its old ways. And in need a of a urgent re-think. Or As McLuhan would have put it, we look at social data through the rear view mirror

the 480+ smm platforms are

broken!

Add to this the more systemic revolutions the industry is facing, such as the visualization of social media, which is going to pose huge challenges to an industry that’s been entirely built on text mining.

#tumblr: !85m post / day!84% images !

social media research is not web analytics!

It’s not quantitative

data!

It’s qualitative data on a

quantitative scale!

Drop the ‘media’. !

It’s ‘social media data’!

It needs social science not

‘media monitoring’!

How can social research

fix smm in 10 steps!

sampling beyond

keywords!

1!

09  am  

10  am  

11  am  

10  pm  12  pm  

1  pm  

2  pm  

3  pm  4  pm  5  pm  

6  pm  

7  pm  

8  pm  

9  pm  

11  pm  

07  May  2013,  10  pm:  the  rumour  spreads  on  Twi?er  

Great  Britain  Indonesia  USA  Malaysia  

Spain  India  Ireland  

France  

Nigeria  

Other  

South  Africa  

How fast does news travel? Conversations about Alex Ferguson

retirement by the hour by country

4  maps  by  visibility  

4  maps  by  visibility  

@Brand follower activity!

Mentions of brand x!

0.1%!

Decoding your online audience Discovering clusters of fans using Social Network Analysis

"Detecting social communities in "Question Time’s Twitter audience

OwenJones84  

PennyRed!

davidschneider! fleetstreetfox!

bbc5live!

bbcquestiontime  BBCNews!

SalmaYaqoob!

johnprescott  

mehdirhasan!

afneil!

ChrisBryantMP!

paulwaugh!

WillBlackWriter!

RippedOffBriton!

NHAparty!

richardcalhoun!

steveclarkuk!

oflynnexpress!

RicHolden!

Green Cluster - 28 y.o., White British - Students, Musicians and actors - From London (41%), Manchester (10%), Liverpool (4%) - Christian and Jewish - Into partying , reading, comedy, football; - Following @stephenfry, @RealDMitchell and @daraobriain

Blue Cluster - 35 y.o., White Birtish - Senior Managers , Journalists, Writers and Lawyers - From London (50%), Manchester (3%), Leeds (2%) - Christian and Jewish - Into Politics, dining and wining, tennis, football; - Following @David_Cameron, @stephenfry, @Number10gov

80%  

20%  

63%  

37%  56%  

44%  

Fuchsia Cluster - 32 y.o., White British - Manufacturing professionals, Nurses, Teachers - From London (48%), Manchester (6%), Cardiff (3%) - Jewish, Christian - Into Business news, reading, history, rugby, tennis and golf; - Following @BBCBreaking @Number10gov @Lord_Sugar.

59%  41%  

Orange Cluster - 42 y.o. White British - Manufacturing professionals, Nurses, Teachers - From London (48%), Manchester (6%), Liverpool (5%) - Jewish, Christian - Into politics, tech news, environment, cooking, tennis; - Following @BBCBreaking, @Ed_Miliband, @Queen_UK.

69%  

31%  

Pale Blue Cluster - 20 y.o, White British, Black - Manufacturing professionals, Nurses, Teachers - From London (48%), Manchester (8%), Belfast (4%) - Muslim, Christian - Into Gaming , comedy/humor, sports; - Following @jimmycarr, @andy_murray, @rioferdy5

2! from content to

context!

© Alexandre Farto aka Vhils 2010

Most Social Media Monitoring platforms focus on just Content

© Alexandre Farto aka Vhils 2010

But we also wanted to understand Context and Behaviour

Social Graph Interest Graph

Content Demographics Behaviours

Dimensions of Social Data

Nose-to-tail indexing or Bigger Big Data

Content  

Behaviours  

Social  Graph  

Interest  Graph  

Demographics  

Data anthropology

3! from analytics to intelligence frameworks!

Analytics Intelligence How many

Tweets/Hour?

How many negatives?

What time of the week is best for what? What’s the brand equity?

Measuring Visibility

4!break down

the social silo!

Predicting the Oscars Integrating social data with multiple external datasets to increase predictability

EXP #1 Reality Mining

Images

Location

Call Log

SMS Log

Bluetooth

Accelerometer

Gyroscope

Orientation

Activity

Running Apps

Battery

Screen

Browser Searches

THE  PICTURES  AS  TEMPORAL  AND  SPATIAL  MARKERS  OF  THE  JOURNEY  

5!scalable

human-analysis!

6!machine-learning!

7!dataUX !

A data canvas

Visual Mining

Data Transparency

8!decision-making!

9!hybrid

methods!

Real-time Audience Insights

Dynamic Segments

SOCIAL PANELS > real-time segments

Pulsar  |  Social  Data  Intelligence        @pulsar_social  |  PulsarPla]orm.com  

50k readers

23,000 Tweets

50k readers 50k readers

50k readers 50k readers 50k readers

22,000 Tweets 47,000 Tweets

63,000 Tweets 32,000 Tweets 12,000 Tweets

A Nation Divided? A Social Panels case study

The Daily Mail 23,000 Tweets

The Guardian 22,000 Tweets

The Independent 47,000 Tweets

The Daily Mirror 63,000 Tweets

The Telegraph 32,000 Tweets

16%

23% 8%

26%

10%

19%

8%

23%

15%

18%

12%

19%

Positive towards Thatcher

Negative towards Thatcher

Neutral: hidden

A Nation Divided? How 300,000 readers of six top UK newspapers are feeling about the death of Margaret Thatcher

10!

making research

programmable !

Social  Data  Intelligence  

     @pulsar_social  |  PulsarPla]orm.com