20140918 ceadar case study identitymatch_identifying persons of interest in social network_idiro and...

Copyright © CeADAR 2014 1 Copyright © CeADAR 2014

IdentityMatch: Identifying Persons of Interest in Social Networks Thursday 18th September 2014, Dublin Tuesday 23rd September 2014, Cork Dr. Oisín Boydell, CeADAR Aidan Connolly, Idiro Kevin Neary, ConnectorsMarketplace

Copyright © CeADAR 2014 2

Research Theme

Social Identity Fingerprinting concerns identifying people of interest across social networks

Visualisation & Analytic Interfaces

• ‘Beyond the desktop’

• Ease of interaction

• Changing user behaviour

• Passive analytics

Data Management for Analytics

• Reduce data management effort for analytics

• Data validation

• Relevance of events to relationships

• Data curation (determining useful data)

• Adaptive ETL (Extract, Transform, Load)

Advanced Analytics

• Causation challenge

• Live topic monitoring

• Social trending and contextualisation

• Continuous analytics

• Social Identity fingerprinting


Research Theme

This is a common analytics need within different industries and different types of data.

Social networks? – not just Twitter, Facebook, Google+ etc.

– Telecoms and communications data

– Financial transactions

– Other records of interactions between people or entities


Research Theme

People of interest?

– Wide ranging and dependent on the domain

– Examples: • People with specific skills or expertise

• People exhibiting certain behaviors (fraud, churn, propensity to buy, multi-sim use etc.)

• Influencers in a particular area

• Bots and fake users


Two project partners

Both from different industries with different sources of data, but both share a similar analytics need.

Project Partners


Challenge & Solution

• World class Irish researchers

• Low risk Rapid Prototyping

• Industry-led engagement

model

– easy & convenient

• Analyse Social Signals at Scale

• Convert large amount of raw data

to valuable information

• Integrate data from different

sources

Requirement: - Viable Business Tool

CeADAR Solution: - IdentityMatch Technology


Aidan Connolly, CEO


Idiro’s Reach

Idiro have analysed data for 12% of the world’s population!


Contagion


Samsung users

Apple users


0

1000

2000

3000

4000

5000

6000

Operator Idiro

Marketing Campaign

Over 400% improvement


Dual-SIM Behaviour

● Dual-SIM usage is a major challenge for many mobile operators. ● How can we identify dual-SIM users from non dual-SIM users? ● Enter CeADAR Research Project.


CeADAR Platform Solution

Key approach: Combine both network based features with content analysis

Example network features: – How people are related in the social graph – Diversity of outgoing/incoming connections – Social influence

Example content analysis – Textual analysis of posts, links, profile description etc – Analysis of actions and patterns e.g. time of day of communication type, mobile

top-up amount and frequency etc.


CeADAR Solution: ConnectorsMarketplace

ConnectorsMarketplace required an interactive user-facing solution to aid finding good ‘connectors’ with defined areas of expertise

Specific challenges:

– User friendly and intuitive

– Search over live data (Twitter)

– An iterative, supervised machine learning approach whereby selected results refine the system in further search iterations.


CeADAR Solution: ConnectorsMarketplace


CeADAR Solution: Idiro

Idiro required a solution that could leverage very faint signals in the data

Specific challenges:

– High volume telecoms data

– Ability to identify and exploit very faint signals and patterns to detect dual-sim users


CeADAR Solution: Idiro

By applying machine learning techniques over many different features (both network and content based) we were able to detect dual-sim vs single-sim behavior with 63% accuracy.

?


Conclusion

Common industry requirement: identifying people of interest in social networks

CeADAR solution: Combine network based features with content analysis

Approach applied in two different industries on different data sources


Conclusion

Thanks to our project partners

CeADAR Advanced Analytics research team at UCD

– Dr Gerard Lynch, Dr Guangyu Wu, Dr Jing Su, Hodei Iraola, Dr Oisín Boydell