Extracted social dimensions

cabbagepatchtapeInternet και Εφαρμογές Web

5 Φεβ 2013 (πριν από 4 χρόνια και 4 μήνες)

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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
associations
schools
Fb
identifier
Fb
identifier
Fb
identifier
Political affiliations
Issues positions
values
Buying habits
Religious views
Framework Overview

Local

network

taxonomy

labels

Social

Dimension

RN

classification

K
-
means

cluster

features

Supv.

classification

behaviors

features

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
0
10
20
30
40
50
60
70
80
90
100
Facebook Accounts
missing labels (normalized)
Accuracy
RN
Bayes
k-Means
Preliminary cluster statistics


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


precision .7 , recall .69




Weightily Averaged One
-
dependence Estimators

(WAODE)

precision .69 recall .70




J48

precision .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/A

See me for friends and neighbors discount….

dostrows@ford.com

Appendix (software)


‘R’ igraph


‘R’ km module


Weka


Ruby
-
Watir