DISTINGUISHING TOPICAL AND SOCIAL GROUPS BASED ON COMMON IDENTITY

AND BOND THEORY

Przemyslaw A. GrabowiczLuca M. Aiello

Vìctor M. EguìluzAlejandro Jaimes

We built a classifier that distinguishesif a given set of people is either

a social or a topical group.

What aresocial

andtopicalgroups?

Social groups

Friends

www.news.com.au

Characteristics of social groups

• Direct reciprocity of interactions

• Small talk (broad range of topics in conversations)

Topical groups

http://www.flickr.com/photos/59571907@N03/5545401056/

A cameraclub

Characteristics of topical groups

• General reciprocity of interactions

• Conversations on a narrow range of topics

Two types of metrics

Based on reciprocity of

interactions

Based on diversity of topics (Shannon’s entropy)

Reciprocity metrics

intra-group reciprocity

intra-reciprocity:

--------------------------------------------------

inter-reciprocity:

1.

2.

Diversity of topics’ metrics

H(g) – Shannon’s entropy of terms/tags

normalized by the average for all groups having the same number of terms

1.

2.

Dataset – Flickr, 2008

Tags extracted from photos:

• from a group pool• commented in a group

• favorited in a group

Human labeling of groups

Consists of exploring:

• text of comments• group profiles

• photos• tags

• maps

Results

Normalized entropyReciprocity

Classifier

score Sg

3 tg

3 ug 3

hg AUC0.75

Accuracy0.76

1.

2.classifie

r3 tg

3 ug

3 hg

3 Hg

3 ag3 bg

size

3 Eg

AUC0.88

Accuracy0.80

hg for comments1

tg for comments2

ug for comments3

hg for favorites4

bg for comments5

AUC0.87

Accuracy0.80

ConclusionsFindings:• The metrics work as the theory predicts• Agreement and accuracy depend on value of the

score• Groups found with a community detection algorithm

are more social than declared groups

Future work:• Entropy is a simple measure, could be replaced with

something what understands texto NLP

• Binary classifier has its limitationso multi-label classification