Identifying Personality-Based Communities in Social Networks

Abstract

In this paper we present a novel algorithm for forming communities in a graph representing social relations as they emerge from the use of services like Twitter. The main idea centers in the careful use of features to characterize the members in the community, and in the hypothesis that well formed communities are those that designate diversity in the features of the participating members.

Full text

Identifying Personality-based Communities in
Social Networks
Eleanna Kafeza1, Andreas Kanavos2, Christos Makris2and Dickson Chiu3
1. Athens University of Economics and Business, Greece, kafeza@aueb.gr
2. Computer Engineering and Informatics Department, University of Patras, Greece
{kanavos, makri}@ceid.upatras.gr
3. The University of Hong Kong, Hong Kong, dchiu88@hku.hk
Abstract. In this paper we present a novel algorithm for forming com-
munities in a graph representing social relations as they emerge from the
use of services like Twitter. The main idea centers in the careful use of
features to characterize the members in the community, and in the hy-
pothesis that well formed communities are those that designate diversity
in the features of the participating members.
1 Introduction
The topic of the paper is to present a novel methodology in order to characterize
interesting communities as they arise in social networks, such as those that are
formed in Twitter. The novelty of our approach lies in the fact that we are looking
for emerging communities, according to the diversity among the characters of
the involved users.
Until now, most practices on message transmission are based on finding the
influential users and try to use them to transmit a message. Moreover, recent
work on data flow on social networks deals with the problem of predicting the
information current. Our approach is different in the sense that we examine ways
to ”drive” the information within the network. We look for sub-networks that
demonstrate a high degree of information flow and as a second step; we aim at
using these networks for increasing information continuance.
There is a lot of work from different areas for creating communities from
graphs. For a thorough survey, we propose [5]. In our approach we argue that
communities in social media, e.g. Twitter, are more probable to contact informa-
tion easily if they are not ”biased” with respect to user personality. A balanced
community can handle information flow quicker and deeper. Hence, here we di-
vide the Twitter graph related to the personality of users which is extracted
based on their behavior.
2 Related Work
Analysis in social networks has a long history, which is related to graph cluster-
ing algorithms, web searching algorithms, as well as bibliometrics; for a complete

2 Eleanna Kafeza, Andreas Kanavos, Christos Makris and Dickson Chiu
review of this area one should consult [4], [5], [10], [12], [14] and [17]. The field
is related to link analysis in the web with cornerstone the analysis of the si-
gnificance of web pages in Google using the PageRank citation metric [3], the
HITS algorithm proposed by Kleinberg [9] as well as their numerous variants
proposed in [11]. PageRank employs a simple metric based on the importance of
the incoming links while HITS uses two metrics emphasizing the dual role of a
web page as a hub and as an authority for information. Both metrics have been
improved in various forms and a related review can be found in [11].
Concerning community detection, various algorithms in literature have been
proposed. It should be noted that HITS by itself if exploring non principal eigen-
vectors, can be used in order to compute communities. Concerning communities,
the problem with which one can come across in bibliography, is related to graph
partitioning. A breakthrough in the area is the algorithm proposed in [6], for
identifying the edges lying between communities and their successive removal;
a procedure that after some iterations leads to the isolation of the communities
[6]. The majority of the algorithms proposed in the area are related to spectral
partitioning techniques. Those are techniques that partition objects by using the
eigenvectors of matrices, which form themselves in the specific set [8], [15], [18]
and [19]. One should also mention techniques that use modularity, a metric that
designates the density of links inside communities against the density outside
communities [5], [13], with the most popular being the algorithm proposed by
[2].
Besides finding emerging communities, estimating authorities has also at-
tracted attention. In [1], they extracted several graph features such as the users’
degree distribution, hubs and authority scores in order to model a user’s relative
importance. Other works in this area include Expertise Ranking [7] and [21],
where they identified authorities using link analysis by considering the induced
graph from interactions between users.
Interesting is the work presented in [20], which employs Latent Dirichlet
Allocation and a variant of the PageRank algorithm that clusters according
to topics and finds the authorities of each topic; the proposed metric is called
TwitterRank. A method proposed in [16], though similar to TwitterRank, differs
in the use of additional features, in the employment of clustering, and in its
applicability in real-time scenarios since it can be easily implemented.
3 A methodology for identifying personality-based
communities in Social Networks
In our work we address the problem of identifying networks that can potential
exhibit maximum flow of information regarding a subject matter. As already
mentioned in most cases in literature authors deal with the problem of finding
influential nodes, and there are several metrics developed to address this issue.
Our approach is different in two aspects; first we identify influential networks
and not individuals ones. Then we extract the networks related to a specific

Identifying Personality-based Communities in Social Networks 3
subject, and compute the influence based on user personality as extracted and
computed by quantitative metrics retrieved by Social Networks.
Social Networks provide metrics to measure different aspects of user behavior.
In this paper, we will use Twitter as a case study but our approach can be easily
extended to any Social Network.
3.1 Basic Twitter Metrics
In this section we examine the basic metrics that we have exported from Twitter
so as to extract users personality. Primarily, we can categorize users’ tweets into
two categories: direct tweets and indirect tweets:
–Direct tweets (D): Here we can find tweets that are produced by an author.
This category comes from the option Compose new Tweet and by this, a
user can potentially start a new conversation.
–Indirect tweets (I1, I2): In this category, tweets come from another user
and can take place with one of two following ways: when a user copies or
forwards a specific tweet so as to spread it in his network (retweets) or in
the second possible way, a user makes a comment to another tweet and as a
matter of fact, a possible conversation may be started (conversations). More
specifically, I1 represents the number of retweets of a user for a specific time
interval and in contrast, I2 represents the number of times there actually
was a conversation upon a tweet.
Other metrics we look into are:
–Number of followers (F): The number of users that follow a specific user.
–Frequency (F R): It calculates the frequency of users tweets. Hence this me-
tric indicates how often an author posts tweets. The way to calculate the
frequency is given as a set of time e.g. half an hour, how many times the
user tweeted.
–Number Hashtag keywords (HK): These keywords are words starting with
the symbol #. Under this symbol, anyone can put a specific tweet into a
certain thematic category. These metrics count the number of hashtags a
user has used upon a set of tweets that have occurred for a specific set of
time.
3.2 Using metrics to extract user personality
Related to the above metrics, we identify users personality as it appears in Twit-
ter. We have classified users in four basic categories based on their personality
as perceived by their peers and as reflected by their behavior. We call them per-
sonality traits. A personality trait is a type of behavior exhibited by a Twitter
user and can get one of the following values:
1. Popular: when a user is followed by many other users (e.g. followers).

4 Eleanna Kafeza, Andreas Kanavos, Christos Makris and Dickson Chiu
Table 1. Twitter basic metrics
Metric Sense/Meaning
F Number of followers
D Number of direct tweets
I1 Number of indirect tweets (retweets)
I2 Number of indirect tweets (conversations)
FR Frequency of user’s tweets
HK Number of hashtag keywords
2. Energetic: when a user posts tweets frequently. This means that this specific
user is energetic and enjoys talking hence he/she tweets on a regular basis.
3. Conversational: when a user takes part in conversations either by com-
menting other people’s posts or republishing them.
4. Multi-systemic: where a user has a high number of interests and likes to
state his opinion in a variety of subjects.
Given the above basic behavioral characteristics that a user can show in
any social network, we associate features in each one of them so as to have a
qualitative insight.
An atomic personality trait for a user x, is a tuple (F1, F 2, F 3, F 4) where
each Fiis defined as follows:
1. Atomic Popular (F1): the number of followers computed as F.
2. Atomic Energetic (F2): the number of direct tweets divided by time interval,
computed as F R.
3. Atomic Conversational (F3): the number of retweets plus the number of
conversations computed as I1 + I2.
4. Atomic Multi-systemic (F4): the number of hashtags found in a given set of
tweets that occurs in a specific time interval, computed as HK.
Related to the above definitions, given a user of the Twitter xi, the atomic
personality trait is a tuple (F1, F 2, F 3, F 4) where each Fi, 1 ≤i≤4, holds the
degree that a user’s personality is associated with each one of the personality
traits. As a next step, we need to identify the dominant characteristics for each
user. As a result, for each metric, we set a range of values such that for the chara-
cteristic Fiif the atomic values of Fiare within the given range, we characterize
the user as having the corresponding behavior. For example, let us assume that
we have the user Helen14,3,2,1and that the range for each Fiis set to (10-14,
1-5, 3-7,0-4); then Helen is Popular, Energetic as well as Multi-systemic.
Let P={Popular (p1), Energetic (p2), Conversational (p3), Multi-systemic
(p4)}be the set of personality traits and xF1,F 2,F 3,F 4the atomic personality
trait for the user x. Moreover, we define with RR1,R2,R3,R4, a set of values that
determine the dominance of personalities; then color(x) is a tuple (c1, c2, c3, c4)
(personality tuple) such that cihas the value piif Fi≤Ri, for 1 ≤i≤4.

Identifying Personality-based Communities in Social Networks 5
3.3 The community extraction algorithm
Based on the above we can now derive the personality traits of each user of the
Twitter. We conceptualize Twitter as a graph where users are the nodes and we
color each node of the graph related to user personality.
We map Twitter as a graph where each node is a user and there is an edge
between two users if there is a relation between them i.e. one user follows the
other or vice versa. We then associate each node with one of the 15 possible
personality traits. Based on the above description of personality there are 4
possible personality traits and each user can have any of the 24−1 possible
values as his personality tuple. We use a breadth first approach to traverse the
graph and for each node we use the definition of color(x) to decide upon the
color of each node/user.
After having decided upon the color/personality of each user, we define ”per-
sonality balanced networks”. A personality balances network is a network that
contains at least one node of every possible personality tuple. Related to ob-
servations regarding the flow of information in human networks, we notice that
balanced networks which are composed by users with different personality traits
tend to demonstrate higher degrees of information flow. In our approach we
create sub-graphs based on the coloring of the nodes. Given the initial Twitter
graph, we traverse the graph using BFS until we find nodes from each one of the
15 personality tuples. The algorithm then extracts that sub-graph.
4 Experimental Evaluation and Results
We examined the validity of our approach through experiments. We then imple-
mented the Twitter graph using Twitter4J, and have colored our graph according
to our methodology and finally have extracted the personality-based community
graph. Twitter4J is a Java library for the Twitter API, with which one can easily
integrate a Java application with the Twitter service.
Firstly, we created a Twitter graph as follows: we made a query on Twitter
on the subject of #SocialNetworks and we retrieved all the associated infor-
mation regarding users and tweets for a time interval of 7 days (01/07/2013 −
08/07/2013).
We also defined the dominance of personality ranges as follows. Initially, we
specified ranges of a user that has all the personality trait as follows; (15%, 35%,
35%, 25%). Consequently, we identified the users that satisfy the above ranges.
As a next step, for the following more similar personality traits, we increased
each particular range by (33%, 25%, 25%, 33%) respectively.
For example, suppose that the initial user has (500,2,3,6) and is characte-
rized as Popular, Energetic, Conversational as well as Multi-systemic. Then a
user in order to be characterized as Popular, Energetic, Conversational, he/she
has to demonstrate this tuple (750,3,4, < 8).
We use this approach to set ranges in order to incorporate the concept that
these personality traits are inter-related. For example, a user with 1000 tweets

6 Eleanna Kafeza, Andreas Kanavos, Christos Makris and Dickson Chiu
is characterized as Popular, but also a user with 500 tweets and a number of
retweets plus conversations is also characterized as Popular.
Our results show that the selected nodes are approximately the 3% of our
graph for the time interval we use. Furthermore, the percentage of the number
of all tweets that the users of this graph exchange divided by the total number of
tweets for the given time is approximately 10%. Tweets consist of direct tweets,
retweets, as well as conversational tweets.
5 Conclusions and Future Work
Our conclusions are that although the community graph was the 3% of the
whole graph (number of community nodes divided by the number of total nodes
in the graph), we had in this network almost 10% of tweets (direct, retweets,
conversations) of the whole Twitter traffic. Hence we can conclude that our
assumption of the personality based communities playing a dominant role in
data traffic, has been verified.
This is a preliminary work. Further work is necessary to identify other per-
sonality traits, different clusters of networks and different ranges for dominant
personalities. Moreover, this work could be extended to other Social Networks
as well.
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