[PPT] - Social Mining & Big Data Ecosystem H2020 - www.sobigdata.eu PowerPoint Presentation

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Social Mining & Big Data Ecosystem

H2020 - www.sobigdata.eu September 2015- August 2019 SoBigData meets EUI -– 11th October 2017

@SoBigData (h7ps://twi7er.com/SoBigData) h7ps://www.facebook.com/SoBigData

SoBigData@EUI – 11th October 2017

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SOBIGDATA VISION AND VALUES

SoBigData@EUI – 11th October 2017

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Shopping paJerns & lifestyle Desires, opinions, senKments RelaKonships & social Kes Movements

Big data “proxies” of social life

SoBigData@EUI – 11th October 2017

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Social mining: making sense

f big data to

understand society

SoBigData@EUI – 11th October 2017

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What is Social Mining

Automated discovering pa7erns and models of human

behaviour across the various social dimensions that have big data “proxies”

– desires and opinions – relaKonships and social Kes – life-styles – mobility

SoBigData@EUI – 11th October 2017

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SoBigData@EUI – 11th October 2017

How opinion emerge and polarize with social media data

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h7ps://www.buzzfeed.com/jamesball/3-million-brexit-tweets-reveal-leave-voters-talked-about-imm?utm_term=.jmDQE9JNR#.fuOOrb145 SoBigData@EUI – 11th October 2017

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SoBigData@EUI – 11th October 2017

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EsWmaWng traffic fluxes on road network from mobile phone data

A B C H W

SoBigData@EUI – 11th October 2017

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Measuring happiness with twiJer data

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Measuring wellbeing with GSM data

SoBigData@EUI – 11th October 2017

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What is percepKon of refugee crises

Internal and external percepKon by country

– Index ρ - the raKo between pro refugees users and against refugees users – Red means a higher predominance of posiKve senKment, higher ρ – Yellow means a higher predominance of negaKve senKment, lower ρ (a) Overall. (b) Internal percepKon. (c) External percepKon.

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SoBigData@EUI – 11th October 2017

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What’s special in social mining?

Any data science experiment is composed by:

– data acquiring (open data, crowdsourcing, crowdsensing,) – model building (data mining, machine learning, network science, …and very complex validaKon phase), – creaKon of an exploraKon scenario (what-if analysis) (different validaKon seing),

….similar to many other data-driven science

process,…but data are produced by humans

SoBigData@EUI – 11th October 2017

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What is needed

responsibility and trust

– FACT: Fairness, Accuracy, ConfidenKality and Transparency

harness social mining for scienKfic

advancement and for the social good

– FAIR: Findable, Accessible, Interoperable, Reproducible

responsible open data science

SoBigData@EUI – 11th October 2017

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Social mining as OPEN SCIENCE

SoBigData@EUI – 11th October 2017

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SOBIGDATA GOALS

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SoBigData GOAL is…

TO CONSTRUCT THE MulWdisciplinary European Infrastructure on Big Data and Social Data Mining (the Social Mining CERN) providing an integrated ecosystem for ethic-sensiWve scienWfic discoveries and advanced applicaKons of social data mining on the various dimensions of social life, as recorded by “big data”.

SoBigData@EUI – 11th October 2017

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an ever-growing, distributed data ecosystem

for procurement, access and curaWon of big social data, within an ethic-sensiWve context, based on

– innovaKve strategies for acquiring social big data for research purposes, – using both opportunisKc means offered by social sensing technologies and – parKcipatory means based on user involvement as prosumers of social data and knowledge.

SoBigData@EUI – 11th October 2017

The pillars for reaching the goal

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an ever-growing, distributed plaform of

interoperable, social data mining methods and associated skills:

– tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, – harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining.

SoBigData@EUI – 11th October 2017

The pillars for reaching the goal

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SoBigData@EUI – 11th October 2017

The pillars for reaching the goal

Building the Social Mining community of

scienWfic, industrial, and other stakeholders (e.g. policy makers),

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The path to achieve the goals

Integrate European (naWonal) infrastructures

and centres of excellence in big data analyKcs, social mining and data science

1. Text and Social Media Mining (TSMM)
2. Social Network Analysis (SNA)
3. Human Mobility Analy?cs (HMA)
4. Web Analy?cs (WA)
5. Visual Analy?cs (VA)
6. Social Data (SD)

SoBigData@EUI – 11th October 2017

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SoBigData@EUI – 11th October 2017

IntegraWng naWonal research Infrastructures

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The ConsorWum

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The path to achieve the goals

Grant access (both virtual and trans-naWonal
n-site) to the SoBigData RI to mulK-

disciplinary scienKsts, innovators, public bodies, ciKzen organizaKons, SMEs, as well as data science students at any level of educaKon.

joint research, and extensive networking and

innovaWon acWons

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SoBigData@EUI – 11th October 2017

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SoBigData e-infrastructure

It is powered by the D4Science plaform
Used as a producKon system by other communiKes
Users: +3600 (SoBigData is 16.5%, fast growing), CompuKng: 1500 cores,

Storage: 400 TB)

Supports basic services
AuthenWcaWon, AuthorizaWon, AccounWng framework
Resource Catalogue
Virtual Research Environments management framework

(VREs)

e-Infrastructure

VRE VRE VRE Resources

SoBigData@EUI – 11th October 2017

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City of CiKzens
Societal Debates
Wellbeing
MigraKon
Tag-me
Smaph

SoBigData VREs

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Exploratories

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Exploratories Virtual Research Environments tailored

n specific where

Social Mining is applied

Promotes results sharing among scienKsts and

communiKes

Promotes the use of RI through Virtual and

TransnaKonal Access

SoBigData@EUI – 11th October 2017

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Big Data for Societal Debates

PolarizaKon, controversy and topic trends on societal debates through social media Lead by Aris Gionis and Dominic Rout

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Polarized PoliWcal Debates

Monitoring Topics across Time and space

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Exploratory: Big Data for City of Citizens

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Social Sensing

Real-Kme crisis mapping

AvvenuK, M., Cresci, S., Del Vigna, F., & Tesconi, M. (2016). Impromtu crisis mapping for prioriKzed emergency response. IEEE Computer, (to appear in May 2016).

SoBigData@EUI – 11th October 2017

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EsWmaWng traffic fluxes on road network

A B C H W

SoBigData@EUI – 11th October 2017

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Big Data for Well Being and Economic Performance

DeprivaKon Index (in France) predicted with Mobile Phone traces Lead by Peep Kungas

SoBigData@EUI – 11th October 2017

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Measuring happiness with twiJer data

SoBigData@EUI – 11th October 2017

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Network effects are relevant in propagation of financial distress

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Big Data for Migration Studies

DeprivaKon Index (in France) predicted with Mobile Phone traces Lead by Peep Kungas

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Ethics and trust

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CreaWng a new generaWon of data scienWsts

SoBigData@EUI – 11th October 2017

8 events and 337 trainees

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SoBigData@EUI – 11th October 2017

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Become a SoBigData Supporter

Share and make findable your

data science results

Become part of the scienWfic

network

hJp://www.sobigdata.eu/join-us

SoBigData@EUI – 11th October 2017