Predictive Semantic Social Media Analysis

David A. Ostrowski System Analytics and Environmental Sciences

Research and Advanced Engineering

Ford Motor Company

Social media

• Influential• Sample of the web

– News driven• CRM

– Real-time– Less biased

• Unique opportunities for analytics

Opportunities

• Old Model– Reactionary

• Damage control• Inquiries• Confirm positive reaction

• New Model– Preemptive

• Focused engagement– Promotions– Events– Media

• Anticipatory

Social Dimensions

• Describes affiliations across a network

• Values / Community

• Reinforced by relationships

• Utilize to predict purchase behavior

Relational Learning

• ‘Birds of a Feather’

• Leverage each local network to semantic understanding

• Relational Learning =>Social dimensions

Framework Overview

• Relational learning– Strengthen representation– Support knowledge

• Unsupervised classification– Generation of dimensions

• Supervised classification– Dimensions => behavior

Movies Television Shows associationsschools

Fb identifier Fb identifier Fb identifier

Political affiliations Issues positions

values

Buying habits

Religious views

Framework Overview

Localnetwork

taxonomylabels

SocialDimension

RNclassification

K-meanscluster

features

Supv.classification

behaviorsfeatures

Higher level features

Case Study One

• 4000 facebook identifiers

• Associations to two vehicle lines

• Question:– What can we extract to characterize between these

two purchase behaviors

Relational Learning Step

• Extracted data from FB

• Consolidated interests

• Applied the RN algorithm

• Guided by taxonomy

45 50 55 60 65 70 75 80 85 90

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Facebook Accounts

missing labels (normalized)

Acc

ura

cy

RNBayesk-Means

Preliminary cluster statistics

1 2 3 4 5 6veh1 k=3 46 39 13veh2 k=3 21 42 36veh1 k=4 44 16 12 26veh2 k=4 14 27 24 32veh1 k=5 21 8 1 0.3 45veh2 k=5 35 22 12 15 14veh1 k=6 7 43 6 13 9 19veh2 k=6 20 14 16 8 9 35

normalized differences between vehicle lines

Extracted social dimensions

• Applied feature sets to k-means (3-6)

• Each classification attempt to characterize between vehicle line and a social dimension (value / interest ..)

• All classification to be considered towards behavioral training

• Also considered community detection– Via maximization of a modularity matrix via leading eigenvectors

Applied Supervised Classification for the Behavior prediction

•Applied sets through three Machine Learning algorithm

•Simple Bayesprecision .7 , recall .69

• Weightily Averaged One-dependence Estimators(WAODE)precision .69 recall .70

•J48precision .69 recall .70

Case Study 2

• 20000 Facebook IDs across four vehicle lines

• Relational modeling– Similar performance as first case study

• Social Dimensions generated for k=(3-7)– Not as much separation after k=6 clustering

• Precision recall (among simple bayes, WAODE, J48).469, .483.591, .588.534, .536

Next Steps

• Institutionalization– Extract / define exactly what our dimensions are

explaining in our data sets.

• Relate to specific association – Values– community

Q/ASee me for friends and neighbors discount…. dostrows@ford.com

Appendix (software)

• ‘R’ igraph• ‘R’ km module• Weka• Ruby -Watir