Measuring the Topical Specificity of Online Communities

MEASURING THE TOPICAL
SPECIFICITY OF ONLINE
COMMUNITIES
Extended Semantic Web Conference 2013
Montpellier, France
MATTHEW ROWE1, CLAUDIA WAGNER2, MARKUS
STROHMAIER3 AND HARITH ALANI4
1SCHOOL OF COMPUTING AND COMMUNICATIONS, LANCASTER UNIVERSITY, LANCASTER,
UK
@MROWEBOT | M.ROWE@LANCASTER.AC.UK
2INSTITUTE FOR INFORMATION AND COMMUNICATION TECHNOLOGIES, JOANNEUM
RESEARCH, GRAZ, AUSTRIA
3KNOWLEDGE MANAGEMENT INSTITUTE AND KNOW CENTRE, GRAZ UNIVERSITY OF
TECHNOLOGY, GRAZ. AUSTRIA
4KNOWLEDGE MEDIA INSTITUTE, THE OPEN UNIVERSITY, MILTON KEYNES, UK

Measuring the Topical Specificity of Online Communities
1
Why measure the topical specificity of Online Communities?

Sub-Community Creation
2
¨  [Belak et al. 2011] identified community (topical) drift
¨  We found the same…
¨  Understanding topical specificity is important for:
¤  Tracking community focus
¤  Suggesting new communities
0 2 4 6 8 10
0.00.20.40.60.81.0
Entropy
F(Entropy)
High Specificity
Low Specificity
0.00 0.10 0.20 0.30
0.00.20.40.60.81.0
JS−DivergenceF(JS−Divergence)
High Specificity
Low Specificity

Theory of Attention Dynamics
3
Ignorance isn’t Bliss:
An Empirical Analysis of Attention Patterns in
Online Communities
Claudia Wagner⇤, Matthew Rowe†, Markus Strohmaier‡, and Harith Alani†
⇤Institute of Information and Communication Technologies, JOANNEUM RESEARCH, Graz, Austria
Email: claudia.wagner@joanneum.at
†Knowledge Media Institute, The Open University, Milton Keynes, UK
Email: m.c.rowe@open.ac.uk, halani@open.ac.uk
‡ Knowledge Management Institute and Know-Center, Graz University of Technology,Graz, Austria
Email: markus.strohmaier@tugraz.at
Abstract—Online community managers work towards building
and managing communities around a given brand or topic. A
risk imposed on such managers is that their community may die
out and its utility diminish to users. Understanding what drives
attention to content and the dynamics of discussions in a given
community informs the community manager and/or host with the
factors that are associated with attention, allowing them to detect
a reduction in such factors. In this paper we gain insights into
the idiosyncrasies that individual community forums exhibit in
their attention patterns and how the factors that impact activity
differ. We glean such insights through a two-stage approach that
functions by (i) differentiating between seed posts - i.e. posts that
solicit a reply - and non-seed posts - i.e. posts that did not get any
replies, and (ii) predicting the level of attention that seed posts
will generate. We explore the effectiveness of a range of features
for predicting discussions and analyse their potential impact on
discussion initiation and progress.
Our findings show that the discussion behaviour of different
communities exhibit interesting differences in terms of how
attention is generated. Our results show amongst others that
the purpose of a community as well as the specificity of the topic
of a community impact which factors drive the reply behaviour
of a community. For example, communities around very specific
topics require posts to fit to the topical focus of the community in
order to attract attention while communities around more general
topics do not have this requirement. We also found that the
factors which impact the start of discussions in communities often
differ from the factors which impact the length of discussions.
Index Terms—attention, online communities, discussion, pop-
another. For example, what catches the attention of users in a
question-answering or a support-oriented community may not
have the same effect in conversation-driven or event-driven
communities. In this paper we use the number of replies that a
given post on a community message board yields as a measure
of its attention.
To explore these and related questions, our paper sets out
to study the following two research questions:
1) Which factors impact the attention level a post gets in
certain community forums?
2) How do these factors differ between individual commu-
nity forums?
Understanding what factors are associated with attention in
different communities could inform managers and hosts of
community forums with the know-how of what drives attention
and what catches the attention of users in their community.
Empowered with such information, managers could then detect
changes in such factors that could potentially impact commu-
nity activity and cause the utility of the community to alter.
We approach our research questions through an empirical
study of attention patterns in 20 randomly selected forums on
the Irish community message board Boards.ie.1
Our study was
facilitated through a two-stage approach that (i) differentiates
between seed posts - i.e. thread starters on a community
Community specificity has a bearing on attention dynamics

Community Recommendation
4
¨  Users interested in a new topic often visit online communities
¨  Recommending a specific community could overwhelm the user
¤  Nuanced language, expert terms
¨  Need a comparative assessment of specificity between
communities
has sub-forum

5
Can we empirically characterise how specific a given
community is based on what its users discuss?

What do we mean by ‘specificity’?
6
¨  We interpret a community forum’s specificity in
relation to its parent…

Measuring Topical Specificity:
Our Approach
7
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
abstraction
P A c

Our Approach
8
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
abstraction
P A c

Our Approach
9
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
abstraction
P A c

Our Approach
10
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
specificity
P A c

Extracting Concepts and Concept Models
11
¨  Given: posts P published in forum f within [t,t’]
¤  Extract entities from each post’s content using Zemanta
¤  Get the concept that each entity is:
a)  an instance of: <entity rdf:type class>
b)  in the category of: <entity dcterms:subject category>
¨  To build the concept model for the forum:
¤  Record the frequency of each concept’s occurrence
that characterise the forum in the time period. We do this by processing each
post content s 2 St0
t00
f using a concept extraction tool (s) to return the set
of concepts related to the content of s. We build the concept model for the
community by recording the frequency of concept occurrences in the input posts
sets, returning At0
t00
f . This set is derived using the following construct:
At0
t00
f [ci] = |{ci : ci 2 (s), s 2 St0
t00
f }| (2)
4 Measuring Topical Speciﬁcity
Concept Extraction Model Set of post contents

Selecting Concepts using Composite Functions:
Concept Frequency
12
Types Categories
Choose the most frequently cited concept in the forum

Selecting concepts using Composite Functions:
Concept Frequency-Inverse Forum Frequency
13
¨  Measures how unique a concept is to the forum
¤  High CF-IFF = unique forum concept given other forums
¨  We choose the concept that maximises CF-IFF…
2. Concept Frequency-Inverse Forum Frequency: This functions selects the most
unique concept discussed in the forum with respect to all forums. This is a
modification of the existing Term Frequency-Inverse Document Frequency
measure used for term indexation. The Concept Frequency-Inverse Forum
Frequency of each concept in a given forum is measured and the concept
that returns the maximum value is chosen. The abstraction of this concept
is then measured and the reciprocal of this value taken as the specificity of
the forum. We define the Concept Frequency-Inverse Forum Frequency as
follows:
cf iff(c, f, F) =
|At0
t00
f [c]|
max
⇣
At0t00
f [c0] : c0 2 At0t00
f
⌘ ⇥ log
|F|
{f 2 F : c0 2 At0t00
f , c0 = c}
(3)
4.2 Concept Abstraction Measures
The composite functions decide on which concept to measure based on either: a)
the frequency of the concept in the forum, or b) the uniqueness of the concept
with respect to the other forums. To measure concept abstraction we define five
measures as follows, which either leverage the network structure surrounding a
concept or use the semantics of relations in the concept graph.
Network Entropy. Our first measure of concept abstraction (a(c)) is based on
The normalised frequency of the concept
appearing within the forum
How common/rare a concept
is across the forums

Measuring Specificity
14
¨  Given a concept c selected using a composite
function, how can we measure its specificity?
¨  Solution: use information-theoretic measures of
abstraction…
¤  Abstraction of concept c: a(c)
¤  Specificity of concept c: 1/a(c)
¨  We examine five measures of abstraction…

Measuring Abstraction: Network Entropy
15
¨  Premise: more abstract tags co-occurs with many
other tags [Benz et al. 2011]
¤  I.e. increase in variation of a random variable
¨  We adapt this for concepts…
¤  Derive co-occurrence frequencies between concepts
using number of edges (relations) between them
¤  Define conditional probability of concept co-occurrence
work by [3] in which tag abstraction is measured through the uniformity of co-
occurences. The general premise is that a more abstract tag should co-occur with
many other tags, thus producing a higher entropy - as there is more uncertainty
associated with the term. In the context of our work we can also apply the
same notion, however we must adapt the notion of co-occurrence slightly to deal
with concepts. To begin with we need to define certain preamble that will allow
network entropy, and the below network-theoretic measures, to be calculated,
using the same definition as laid out in [4]: let G = {V, E, L} denote a concept-
network, where c 2 V is the set of concept nodes, ecc0 2 E is an edge, or
link, connecting c, c0
2 V and lb(ecc0 ) 2 L denotes a label of the edge - i.e.
the predicate associating c with c0
. We can define the weight of the relation
between two concepts c and c0
by the number of times they are connected to one
another in the graph: w(c, c0
) = |{ecc0 2 E}|. From this weight measurement,
derived from concept co-occurrence, we then derive the conditional probability
of c appearing with c0
as follows, using ego(c) to denote the ego-network of the
concept c - i.e. the triples in the immediate vicinity of c:
p(c0
|c) =
w(c, c0
)
X
c002ego(c)
w(c, c00
)
(4)
Now that we defined the conditional probability of c appearing with another
concept c0
, we define the network-entropy of c as follows:
H(c) =
X
c02ego(c)
p(c0
|c) log p(c0
|c) (5)
The immediate neighbours
of concept c

Measuring Abstraction: Network Centrality
16
¨  Premise: the more central a concept node is to a
network, the greater its abstraction
¤  Given the increased information flow through the node
¨  We gauge centrality using two measures:
¤  Degree Centrality
n  Number of connections from a concept node divided by vertex set
size
¤  Eigenvector Centrality
n  Determine the position of the concept node based on the
eigenstructure of the concept network

Measuring Abstraction: Statistical
Subsumption
17
¨  Premise: generality of a concept can be measured
through the number of concepts that it is broader
than [Schmitz et al. 2006]
¨  Graph semantics are used to measure the number
of specialisations/narrowings of a concept…order to count how many concepts a given concept c is more general than (we
use DBPedia datasets as our concept graphs which is explained in the following
section).
SUB(c) = |{c0
: c0
2 V, ecc0 2 E, lb(ecc0 ) 2 {<skos:narrower>, <rdfs:subClassOf>}|
(8)
Key Player Problem. The ﬁnal measure of abstraction that we use is taken
from Navigli & Lapatta [7] and attempts to measure the extent to which a given
node in a network is a key player in the network’s topology; that is, the extent to
which it is important for information ﬂow through the network. To compute this
If the label of the predicate denotes a specialisation/narrowing

Measuring Abstraction: Key Player Problem
18
¨  Premise: concept node position as a key player in
network topology can be used to gauge its
abstraction [Navaglia & Lapatta, 2010]
¤  Measuring its importance for information flow
¨  To derive Key Player Problem measure:
¤  Measure shortest distance from each concept to every
other
¤  Take the sum of the reciprocal of these distances
¤  Normalise the sum by vertex set size

Approach Evaluation
19
Rank forums by
specificity
values
Generate
ground truth
rank
Compare
predicted and
ground truth
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
specificity
P A c
ˆd d{ }

Approach Evaluation
20
Rank forums by
specificity
values
Generate
ground truth
rank
Compare
predicted and
ground truth
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
specificity
P A c
ˆd d{ }

Approach Evaluation
21
Rank forums by
specificity
values
Generate
ground truth
rank
Compare
predicted and
ground truth
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
specificity
P A c
ˆd d{ }

Approach Evaluation
22
Rank forums by
specificity
values
Generate
ground truth
rank
Compare
predicted and
ground truth
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
specificity
P A c
ˆd d{ }

Approach Evaluation
23
Rank forums by
specificity
values
Generate
ground truth
rank
Compare
predicted and
ground truth
Retrieve
community
posts in time
window [t,t’]
Derive
community
concept model
Select concept
using composite
function
Measure
concept
specificity
P A c
ˆd d{ }
We test combinations (20 in total) of:
•  Composite function (2)
•  Abstraction Measures (5)
•  Concept Graphs (2)

Experimental Setup
24
¨  Dataset:
¤  Irish community message board Boards.ie
¤  230 forums selected (filtered out low activity forums)
¨  Selecting window of analysis:
¤  Start date = 23/04/2005. Width = 1 week
within a k-week window, and found the densities to all be normally-distributed
with variance in their tails and skews. We wanted to select the most stable dis-
tribution of posts across the forums and therefore measured the kurtosis and
the skewness of each window size’s distribution - as shown in Figure 1(b). We
then chose the week that produced the minimum of these measures: 1 week. By
choosing this time period we are provided with reduced variation in the forum
post distribution and therefore a stable picture, with no large ﬂuctuations, of
community activity.
●● ●
0 1000 2000 3000 4000 5000 6000 7000
Posts−per−day
(a) Boxplot of posts density in
2005
0 2 4 6 8 10
020406080
Number of Weeks
Kurtosis|Skewness
Kurtosis
Skewness
(b) Kurtosis and Skewness of den-
sity distributions
Fig. 1. Plots of posts-per-day distribution in 2005 (1(a)) and the distribution properties
of posts-per-forum in increasing week windows from 23/3/2005 (1(b)).

Experimental Setup: Concept Graphs
25
¨  Concept models come in two flavours:
a)  Entity types (classes that an entity is an instance of)
b)  Categories (skos categories that the entity belongs in)
¨  Experiments use two concept graph types:
a)  Type Graph: DBPedia Ontology Graph
n  Nodes: classes
n  Edges: ontological relations
b)  Category Graph: DBPedia Category Structure
n  Nodes: categories
n  Edges: skos relations (broader, narrower)

Experimental Setup: Evaluation Measures
26
¨  Compare predicted rank against the ground truth rank
¨  Measure rank quality using:
¤  Kendall Tau-b (1 is better)
n  Difference in the number of concordant and discordant pairs
¤  Impurity@k (0 is better)
n  Distance from each wrongly positioned forum to its true position
n  Measure for k={1,5,10,20,50,100} and average
show:broader relations. Our evaluation therefore, not only looks for the op-
timum combination of abstraction measure and composite function, but also
which concept graph to use: the Type graph or the Category graph.
Table 1. Example rankings of forums in two predicted ranks from model 1 (M1)
and model 2 (M2) together with the ground truth. The label function l(.) returns the
level of the forum from the ground truth. Our evaluation measures (Kendall ⌧b and
Impurity@k) are provided with the ordered levels as input.
GT M1 M2
Rank Index d l(d) ˆd1 l( ˆd1) ˆd2 l( ˆd2)
1 a 1 c 2 a 1
2 b 1 d 2 b 1
3 c 2 g 3 c 2
4 d 2 h 3 d 2
5 e 2 a 1 f 2
6 f 2 e 2 g 3
7 g 3 i 3 e 2
8 h 3 b 1 h 3
9 i 3 j 3 i 3
10 j 3 f 2 j 3
Evaluation Measures. To evaluate our approach we use the di↵erent combina-
tions of: a) composite functions, b) abstraction measures, and c) concept graphs,
to produce a predicted rank (ˆd) - ordering the most speciﬁc forum to the most
general - which is then compared against a ground truth rank (d). The ground
truth rank of the forums is derived from the hierarchical structure of Boards.ie
which allows a given forum to be declared as either a parent or a child of another
forum, thereby creating a nested structure. In this setting there are three levels
that a given forum can be placed in: 1 is most speciﬁc, 3 is most general and 2 is
in-between. In order to aid comprehension of our evaluation setting we present
example rankings produced by two hypothetical models (M1 and M2) in Table
1 along with the ground truth (GT). We refer to this evaluation setting as level-
based ranking as each model (M1, M2) returns a level ordering (using a label
Level-
based
Ranking

Experiments: Results
27
¨  Best model: Eigenvector Centrality with Type Graph
¤  For full rank: concept frequency
¤  For top-k: CF-IFF
¨  Type graph > category graph
(Type graph with Concept Frequency and Eigenvector Centrality) we do slightly
worse than the random baseline, thereby failing to achieve the best performance
when focussing on top-k ranks.
Kendallτb
−0.2−0.10.00.10.2
Concept Frequency
Cf−iff
N
etw
ork
Ent
D
egree
C
ent
Eigenv
C
ent
StatSub
KPP
(a) Types - Kendall ⌧b
Kendallτb
−0.20.00.10.2
Concept Frequency
Cf−iff
N
etw
ork
Ent
D
egree
C
ent
Eigenv
C
ent
StatSub
KPP
(b) Categories - Kendall ⌧b
AverageImpurity
0.000.050.10
Concept Frequency
Cf−iff
N
etw
ork
Ent
D
egree
C
ent
Eigenv
C
ent
StatSub
KPP
(c) Types - Impurity@k
AverageImpurity
0.000.050.10
Concept Frequency
Cf−iff
N
etw
ork
Ent
D
egree
C
ent
Eigenv
C
ent
StatSub
KPP
(d) Categories - Impurity@k
Fig. 2. Plots of the results obtained when measuring forum speciﬁcity using: a) the
Lower is better Random model baseline

Evaluation: Qualitative Insights
28
¨  ‘Discworld’ appears top for Concept Frequency
¤  Both measures return a similarly specific assessment
¨  ‘Subscribers’ appears in CF-IFF lists:
¤  Selected by the function, but different values obtained using
the measures
duced by the models. Similarities are evident when the same composite function
is used: Discworld appears at the top of both abstraction measures when us-
ing Concept Frequency - indicating that the concept selected from this forum
has the same specificity levels for both abstraction measures - while Subscribers,
despite being a mid-level forum, appears towards the top rank of each abstrac-
tion measure when using CF-IFF - indicating the existence of a concept unique
to this forum which shares a similar specificity level across the measures. Such
qualitative analysis indicates that despite the composite functions selecting the
same concept to measure the abstraction of, the measures produce, in general,
di↵erent rankings based on the concept’s network position.
Table 2. Forum rankings using the Type Graph and di↵erent combinations of com-
posite functions and abstraction measures. The integers in parentheses represent the
level of the forum on Boards.ie: 1=most specific, 3= most general.
Concept Frequency CF-IFF
Network Entropy Eigenv’ Cent’ Network Entropy Eigenv’ Cent’
Discworld (1) Discworld (1) Languages (1) Magic the Gathering (1)
The Cuckoo’s Nest (2) Angling (2) Hunting (1) Subscribers (2)
Models (2) Paganism (1) File Exchange (2) Unreal (2)
Slydice Specials (1) Feedback (2) Game Threads (1) LAN Parties (2)
Battlestar Galactica (1) Personal Issues (2) Magic the Gathering (1) World of Warcraft (1)
FS Motors (1) Mythology (2) Bangbus (1) Role Playing (2)
Gadgets (1) Films (1) Biology & Medicine (2) Midwest (2)
FS Music Equipment (1) Business Managem’ (1) Snooker & Pool (2) Game Threads (1)
Pro Evolution Soccer (2) Xbox (1) Subscribers (2) GAA (2)
Call of Duty (2) Help Desk (2) HE Video Players (1) Midlands (2)
Anime & Manga (2) DIT (2) Discworld (1) Discworld (1)

Conclusions
29
¨  Presented an approach to measure the topical specificity of
online community forums:
¤  Extracted entities from forums, recorded frequencies
¤  Selected concepts to measure using composite functions
¤  Measured concept specificity using information-theoretic measures
¨  Findings showed:
a)  Type graph > category structures
b)  Eigenvector centrality was the best measure
c)  Differences in composite function…
n  Top ranks: CF-IFF
n  Full rank: Concept Frequency

Current and Future Work
30
¨  Community tracking
¤  Grouped high and low specificity
communities
¤  Found significantly greater
semantic divergence in low
specificity communities
¨  Attention Dynamics Theory
¤  Plan to group communities by
topical specificity
¤  Examine how attention dynamics
differ
¤  Learn heuristics for dynamic model
adaptation
The Semantic Evolution of General and !
Specific Communities!
Matthew Rowe and Claudia Wagner!
1.  School of Computing and Communications, Lancaster University, Lancaster, UK. m.rowe@lancaster.ac.uk!
2.  Institute for Information and Communication Technologies, JOANNEUM Research, Graz, Austria. claudia.wagner@joanneum.at!
[1] Q Hong, S Kim, S. C. Cheung, and C Bird. Understanding a developer social network and its evolution. In Proceedings of the 2011 27th IEEE
International Conference on Software Maintenance, ICSM ’11, pages 323–332, Washington, DC, USA. (2011)!
[2] C. Wagner, M. Rowe, M. Strohmaier, and H. Alani. Ignorance isn’t bliss: an empirical analysis of attention patterns in online communities. In AES
Conference on Social Computing, (2012)!
[3] M Rowe, C Wagner, M Strohmaier and H Alani. Measuring the Topical Specificity of Online Communities. In the proceedings of the Extended
Semantic Web Conference. Montpellier, France. (2013)!
•  Past work [1] examined community lifecycle events (creation, growth, etc.), eschewing topical dynamics!
•  Our prior work [2] found a link between the topical focus of a community and its attention dynamics!
•  E.g. ‘Golf’ community forum required posted content to exactly match the community!
•  Changes in a community’s topics require content injection models to be re-trained!
•  Expensive and time-consuming process, and often performed offline!
•  Our solution: measure community specificity, then examine how community evolution relates to specificity!
Motivation!
•  Dataset: 230 community forums from Boards.ie!
•  Took all posts published within a 1-week window from 23/03/2005!
•  Extracted entities from posts using Zemanta!
•  Identified the type of each entity using DBPedia ontology!
<entity> rdf:type <class>!
•  Measured the specificity of each forum [3] using combinations of:!
•  Class abstraction measures (e.g. Network entropy)!
•  Composite Functions (e.g. Most specific concept)!
•  Compared performance against the Knuth Shuffle (random model)!
•  Evaluated using Kendall Tau-b and Max-Levels (proposed measure)!
•  Max-levels judges the maximal potential outlier level!
•  Best model: Eigenvector Centrality with Concept Frequency!
Measuring Topical Specificity!
MaxLevels
0.850.900.95
Most Spec
Mean Spec
Concept Freq
CF−IFF
N
etw
ork
Ent
D
egree
C
ent
Eig
env
C
ent
H
its
Authority
H
its
H
ub
StatSub
KPP
MaxLevels
0.850.900.95
N
etw
ork
Ent
D
egree
C
ent
Eig
env
C
ent
H
its
Authority
H
its
H
ub
StatSubKPP
0.850.900.95M
ostSpec
M
ean
Spec
C
onceptFreqC
F−IFF
●
2 4 6 8 10
2 4 6 8 10
0.00.10.20.30.4
Eigenvector Centrality + Concept Frequency
Density
References!
•  Divided forums up into 10 equal-frequency bins based on specificity!
•  Measured: Eigenvector Centrality with Concept Frequency!
•  High-specificity forums = members of top bin!
•  Low-specificity forums = members of bottom bin!
•  For these forums, randomly selected four 1-week periods and derived
concepts over each period, and concept vectors: !
•  Calculated cosine similarity between consecutive concept vectors!
•  Examined difference between cosine similarity distributions of high
and low forums:!
•  Low=0.783, high=0.854. p<0.05 with Student T-test (2 tailed)!
•  The result indicates that general communities exhibit greater
semantic drift than topically specific communities!
Semantic Evolution!

c1,

c2,

c3,

c4{ }
0 10 20 30 40 50
0.00.20.40.60.81.0
Cumulative Specificity
F(CumulativeSpecificity)
High Specificity
Low Specificity
0 10 20 30 40 50
0.00.20.40.60.81.0
Cumulative Specificity Difference
F(CumulativeSpecificityDiff)
High Specificity
Low Specificity
High%
Low%

@mrowebot
m.rowe@lancaster.ac.uk
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Measuring the Topical Specificity of Online Communities

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