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ACCEPTED AND PRESENTED
at SBP-BRiMS 2021
Predicting Users’ Value Changes by the Friends’
Influence from Social Media Usage
Md. Saddam Hossain Mukta1, Ahmed Shahriar Sakib2, Md. Adnanul Islam3,
Mohiuddin Ahmed4, and Mumshad Ahamed Rifat1
1United International University, Dhaka, Bangladesh
2American International University Bangladesh
3Military Institute of Science and Technology
4Edith Cowan University, Australia
Abstract. Basic human values represent a set of values such as security,
independence, success, kindness, and pleasure, which we deem important
to our lives. Each of us holds different values with different degrees
of significance. Existing studies show that values of a person can be
identified from their social network usage. However, the value priority
of a person may change over time due to different factors such as life
experiences, influence, social structure and technology. Existing studies
do not conduct any analysis regarding the change of users’ value from the
social influence, i.e., group persuasion, form the social media usage. In
our research, first, we predict users’ value score by the influence of friends
from their social media usage. We propose a Bounded Confidence Model
(BCM) based value dynamics model from 275 different ego networks
in Facebook that predicts how social influence may persuade a person
to change their value over time. Then, to predict better, we use particle
swarm optimization based hyperparameter tuning technique. We observe
that these optimized hyperparameters produce accurate future value
score. We also run our approach with different machine learning based
methods and find support vector regression (SVR) outperforms other
regressor models. By using SVR with the best hyperparameters of BCM
model, we find the lowest Mean Squared Error (MSE) score 0.00347.
Keywords: Values ·Facebook Friends ·Influence ·BCM ·Hyperparameters
·PSO.
1 Introduction
In recent times, Social Networking Sites (SNS) have become a major platform
of communications among users on the web. These SNS data provide a wide
range of opportunities to identify cognitive and psychological attributes such
as basic human values (aka values) [8], personality [16], and behavior [21].
Values represent one’s attitudes, opinions, thoughts, and goals in life. Values
of an individual might amend over time due to the influence of her group of
arXiv:2109.08021v1 [cs.SI] 12 Sep 2021
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2 Mukta et al.
friends [10,14]. In this paper, we predict users’ future value scores, which might be
changed by the influence of friends in an egocentric network such as Facebook5.
Values represent a set of criteria such as security, self-enhancement, etc.,
that are used by individuals to take different actions. Chen et al. [8] predict
five higher-level values from user word usages in Reddit. In another study [19],
authors predict users’ values from generated (i.e., statuses) and supported (i.e.,
likes and shares) contents in Facebook. However, these approaches largely fail
to capture the change of users’ value priorities from the social network usage.
Several socio-psychological studies [9,14] show that values of a user might be
reshaped by the influence of other members in the same interest group. To the
best of our knowledge, no study has been conducted to identify users’ change of
value scores by the influence of friends from the social network usage. However,
identifying the change of values of an individual from friends’ influence has a
number of applications such as identifying university course major or career
path shifting trends, prediction of customers’ purchasing behavior, transforming
customers’ product selection preference or marketing policies, and understanding
transition of economics and business.
To this context, we propose a technique to identify the changes of users’
value from their social network interactions by using Bounded Confidence Model
(BCM) [22,11,17]. Motivated by the work [8], first we collect data of 275 user
networks from Facebook by using a google survey form. Since users’ change
of values are observed in terms of time intervals, we separate users’ Facebook
statuses, comments, and shares according to a time span of six months [20].
Then, we generate value scores from users’ each six months interval Facebook
statuses, comments, and shares by using IBM Watson Personality insight API 6.
From the computed value scores of peer friends, we compute the hyperparameters
(convergent factor, µand threshold, σ) for our BCM model (see Equation 1).
Then, we use particle swarm optimization (PSO) [3] method for finding the best
hyperparameter configuration. Finally, we use these optimized hyperparameters
of the BCM model to predict next value score by using support vector regressor
(SVR) model [2]. In summary, our contributions in this paper can be highlighted
as follows:
–We demonstrate the change of values by the influence of group of friends
using BCM model.
–We develop a PSO based best hyperparameter selection method that predicts
user’s future value score with less MSE score.
5Facebook is an example of an egocentric network [1] because the network provides
interaction capability only among the friends while prevent any interactions from
the external users to this network.
6https://www.ibm.com/watson/services/personality-insights/
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Value Changes by Friends’ Influence 3
2 Preliminaries and Related Work
In this section, we describe the preliminaries about values, BCM model, and how
influence may reshape users’ behavior from social media usage.
2.1 Values
Basic Human Values define the goal, belief and behavior of an individual. Schwartz
et al. [26] categorize the value dimension into five higher-level values. Openness-
to-change mainly refers to the ability to “think outside of the box” which
consists of two broad personal values: self-direction and stimulation [25]. Self-
transcendence satisfies the motivational goal for preservation and enhancement
of the welfare of people with whom one is in frequent social contact [25]. Self-
enhancement refers economic well-being and notoriety, control or strength
over individuals and resources. Conservation emphasizes order, self-restriction,
preservation of the past, and resistance to change. Hedonism basically means
pleasure or sensuous gratification for oneself [25].
2.2 Bounded Confidence Model (BCM)
BCM is a popular opinion dynamics model to determine the influence of a
network of people over an individual. We use the BCM devised by Deffuant
et al. [12] to understand the nature of such changes which can be different from
one to another. Considering the distance of the corresponding values between
two users is less than a given threshold σ, the updated value of each of the users
can be calculated using the following equation:
BH V t+1
i=BH V t
i+µego(BHV t
j−BH V t
i)Θ(σego − |BH V t
j−BH V t
i|)(1)
where BHViis the value score of the user i,µego is a convergence factor, σego is
the threshold within which the users interact or adapt with each other, and Θ()
is a Heaviside’s theta function 7.
2.3 Values in Social Media
Chen et al. [8] identify values from Reddit, an online news sharing community.
The authors identify five higher-level values from user’s pattern of word using in
social media. They predict the value scores by using linear regression. Boyd et
al. identify values [7] from statuses of 767 Facebook users. They identify values
with a data driven approach. Mukta et al. [19] identify values from both user
generated and supported contents in Facebook. Mukta et al. [20] predict the
temporal change of values of Facebook users by using different ML techniques.
In this paper, we devise a novel technique to identify the value change of an
individual by the social influence of the friends in Facebook.
7https://mathworld.wolfram.com/HeavisideStepFunction.html
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4 Mukta et al.
2.4 Value Changing Influence Models
Value of a user might be changed her values for different offline behaviors such
as life experiences, life events, technological change, social structure, and life
style of others [10] [15] [10]. In a previous study [20], we find that value change
of an individual reflects in their social media usage behavior. Authors capture
the opinion change over time from social media. Quattrociocchi et al. [22] show
that inner dynamics of information systems, i.e., TV, newspaper, social network
platforms, - play a vital role on the evolution of the public network.
In this paper, we compute value scores of different time intervals and analyze
the interactions among users. Then we measure how the value of one person may
influence the change of value on others through social media interactions.
3 Methodology
In this section, we first describe the process of value change modeling by the
influence of close friends using BCM model. Figure 1 shows the complete pipeline
of our value change modeling and its hyperparameter optimization process. We
discuss our methodology in the subsequent sections.
Fig. 1: A complete pipeline of value change modeling and BCM hyperparameter
optimization.
3.1 Data Collection
We randomly select a total of 275 (motivated by the study of Golbeck [16])
different Facebook ego networks where each user holds an ego network and his
Facebook friends are alters. Initially we invite a total of 320 Facebook friends
to share their ego networks, but only a total of 275 users show their interest to
share their data. After selection we extract the list of users; then collect every
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Value Changes by Friends’ Influence 5
comment of each user. Users’ collect their data from their own profile 8and
download her data 9in different time intervals with different formats: HTML
and Json. Later, they share their data with us through the google form.
We extract public profile data like statuses, posts and shares that support
comments and likes. Then, we divide the data into temporal segments. Each
year is divided by 2 segments, each segment contains data of 6 months. In our
experiment, we have Facebook posts of maximum 10 years for a single user which
we can divide into 20 segments. We select users based on their number of their
daily public interactions. Table 1 shows the statistics of our dataset.
Table 1: Statistics of our dataset
Attributes Values
Number of ego Networks 275
Number of total Comments 75,625
Number of Maximum Comments of a User 237
Number of Minimum Comments of a User 25
Total Time Duration (years) 10
Maximum Time Span for a user (years) 10
Minimum Time Span for a user (years) 7
3.2 Influence Modeling for Value Change
Next, we first propose a new model to investigate the change of values over time.
During applying the BCM model [12], we use two hyperparameters: convergence
factor (µ)and threshold (σ)of value difference. To optimize these hyperparameters,
we use a machine learning based approach where we find optimum values (i.e.,
solution) by using PSO algorithm (Figure 1). From these optimized hyperparameters,
we find the threshold (σ)value for predicting the next value score through using
SVR.
4 Experimental Evaluation
4.1 Ego Network Configuration
We consider the adjustment of the value score of an ego (ui) by interacting all
users uj. We collect interaction data such as likes, comments, and sharing of
object between an ego u, and their friends, ujwhere j≤5according to Dunbar
number [1]. We are interested in showing that when a number of users ujinfluence
to value score of a single user (e.g. an ego), his/her value score converges to a
single unified score. This idea is borrowed from mean field theory used in the
research paper by Boudec et al. [6].
8https://www.facebook.com/settings?tab=your_facebook_information
9https://www.facebook.com/dyi/?referrer=yfi_settings
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Fig. 2: Group wise influence based value change from time t1(Fig a) to t2(Fig b)
Ego interacts with a total of five close alters (according to Dunbar number
of close friends) [1]. In this study, we assume that users may shift their value
scores by the influence of close friends who fall in the sphere where N=5 [1].
Like a group discussion, we select a primary user to detect his/her value
change from the influence of other people from the group. Figure 2(a) shows
that independent value scores for a total of 5 Facebook users. When they interact
each other in a group, value score for a user might be changed by the influence
of other alters, i.e., Facebook friends. Figure 2(b) shows the final score for
a value dimension after being influenced by the members of the group. We
predict the change of users value by using BCM model. The model has different
hyperparameters that we optimize by using Particle Swarm Optimization (PSO)
method in subsection 4.2.
4.2 Hyperparameter Estimation
The convergence factor, µindicates the momentum term of the influence dynamics.
In our study, we consider µ= 0.4 following the study of [22]. However, to obtain
the appropriate value of the threshold (σ) for the BCM model, we build a
regression model. The regression model actually predicts the σwhich minimize
the To build the regression model, we consider four features for the training
instances. The features are: i) value of user, ui, at t time, ii) value of user’s friend,
uj, at t time, iii) value of user, ui, at t+1 time, and iv) Convergence Factor, µ.
We perform several regression models with these features by a 10-fold cross-
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Value Changes by Friends’ Influence 7
validation with 10 iterations. We use the following regressors: SVR 10, Gaussian
Process Regressor 11 , ElasticNet 12, BayesianRidge 13 and MLPRegressor 14.
For hyperparameter tuning, we apply PSO 15 by using Optunity Library 16.
Particle swarm optimisation (PSO) [3] is a typical algorithm of the swarm
intelligence family. The algorithm is a population-based meta-heuristic optimization
technique which initializes a number of individual search ‘particles’, each representing
a possible solution. This population of particles change their positions by an
evolutionary process. Each of these particles is in movement with a velocity
allowing them to update their position over the iterations to find the global
minimum.
Table 2: Hyperparameters Configuration for different Regressors
Regression Model Hyperparameter Configuration
SVR
kernel -RBF, gamma- [0, 50] , C-[1, 100] ;
kernel -linear,C-[1, 100] ;
kernel -poly, degree- [2, 5] , C-[1000, 20000], coef0-[0,1]
Gaussian Processes normalize_y= [True,False], alpha = [1e-10 - 1e-2]
ElasticNet alpha - [0, 1.0] , l1_ratio - [0, 1.0] , tol - [1e-4, 0.01]
BayesianRidge alpha_1 - [1e-6, 0.01], alpha_2 - [1e-6, 0.01],
lambda_1 - [1e-6, 0.01], lambda_2 = [1e-6, 0.01], tol -[1e-4, 0.01]
MLPRegressor hidden_layer_sizes - [( 50, 50, 50), ( 50, 100, 50 ), ( 100, )] ,
activation - [’tanh’, ’relu’], alpha - [1e-4, 0.01]
Table 3: Strength (Low RMSE) of the regression model
Regression Algorithm MSE
SVR 0.00334
Gaussian Process Regressor 0.07138
MLPRegressor 0.08912
ElasticNet 0.05312
BayesianRidge 0.01183
ParticleSwarm 17 has 5 parameters that can be configured: num_particles,
num_generations, φ1,φ2,and max_speed. In our experiment, we use num_particles
10 https://bit.ly/2OOBDZa
11 https://bit.ly/2OWxAKy
12 https://bit.ly/3pDuh7M
13 https://bit.ly/3qJoyys
14 https://bit.ly/3qEivez
15 https://bit.ly/3ucqcLf
16 https://homes.esat.kuleuven.be/ claesenm/optunity/
17 https://bit.ly/37zcGYb
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= 10 , num_generations = 15 ,max_speed = None, φ1= 1.5 , φ2= 2.0 to
initialize ParticleSwarm 18 solver in Optunity. In our case, we consider one set
of hyperparameter configuration as a single particle.
We split the dataset into training, test, and validation data by 70%, 20% and
10%, respectively. Then, we define our objective function to minimize the cost of
the model which in this case, Mean Squared Error (MSE). We initialize different
box constrained configuration sets of hyperparameters for different regressors.
Each particle represents a configuration for hyperparameters of the Machine
Learning Model. All of the particles have MSE, i.e. fitness value which are
evaluated by the cost function to be minimized. The particles move through
the problem space by following the current optimum particles. Since Optunity
can optimize conditional search spaces , we set different hyparameters based on
the kernel. We use Radial Basis Function (RBF),linear and polynomial kernel.
Table 2 shows the search space for SVR hyperparameters. Table 2 shows the
optimized hyperparameter configuration values for the regressors. Table 3 shows
the performance of the regressors. We find SVR regressor shows the best average
performance (MSE-0.00337) to predict users’ threshold value on the BCM model.
Algorithm 1 presents the process of our PSO based hyperparameter optimization
method.
Algorithm 1: PSO_based_BCM_Hyperpar_Optimization
initialize:
uias value of a user
uj. . . n as values of group users
ui+tas value of a user in
time t
µas convergence factor ( 0.4 )
σas Threshold Value
Proc.: Dataset_Prep_BCM( )
1: Calculate the threshold value via BCM
Model
2: Set predictor variables (X) : ui,uj.. . n ,
ui+t,µ
3: Set Dependent variable : σ
4: Divide the Data set by 70% and 30%
as D1, and D2, Respectively
Proc.: Model_Training( )
5: initialize D1 as train dataset
6: initialize D2 as test dataset
7: Run ML models to predict σ
Proc.: Hyperparam_Optim( )
PSO: Initialize
num_particles=10
num_generations=15
max_speed=None
φ1=1.5
φ2=2.0
8: Find the optimum σfor BCM model
using PSO
9: Save ML model as M1
10: Calculate the ui+tusing M1
11: Calculate MSE on D2 dataset with
respect to M1
5 Results and Discussion
In this experiment, we take Facebook interactions (i.e., status, comments, shares,
and likes) of 275 users. Figure 3 presents the variation of convergent factor and
18 https://bit.ly/3aBdSw3
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Value Changes by Friends’ Influence 9
Fig. 3: Users’ value Scores computation and its hyperparameters.
Fig. 4: Loss of users’ predicted value
scores by SVR.
Fig. 5: Users’ Value scores by using the
best hyperparameters.
threshold for computing values by using BCM models. By using PSO, we find the
best set of parameters for computing SVR. With these parameters, we predict
the best σby using SVR. Figure 4 presents the loss to compute the σby using
different models. Among these models, SVR shows the lowest loss to predict
the hyperparameters. When we use the σvalue, we predict the best final values
scores of a user by the influence her close friends. Figure 5 shows the actual
future value scores and predicted value scores.
Bardi et al. [4] show that wide range of influence can change one’s values
across contexts and time. Rokeach et al. [24] observe that in addition to cultural
and societal changes, personal values might be changed. The author also observe
that values likely to change during adulthood. Roberts et al. [23] personality and
values are subject to change and adaption across different life stages.
Berndt [5] describes that friendships have influence on user’s attitude and
behavior. For example, adolescents whose friends drink beer at parties likely to
start drinking. In contrast, user’s value may influence negatively. For example,
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adolescents often have conflicts often have conflicts with others which might
propagate among others. Epstein [13] and Hartup [18] describe that friends
influence each other in different behaviors, including aspirations, achievements,
values and attitudes, social skills, and appropriate sex roles.
6 Conclusion
In this paper, we have extracted 275 different ego networks from a Facebook.
Then, we have identified intimate friends for each of the ego networks by using
Dunbar number. Then, we have segmented users’ interaction in a time frame
of 6 months. Then, we have computed users’ value scores from their Facebook
interactions by using IBM personality insight API. Based on the users’ value
scores, we have proposed a value dynamic technique based on BCM influence
model. During modeling, we have also proposed a PSO based hyperparameter
estimation technique. Our model have showed an outstanding performance (i.e.,
lower MSE) in predicting change of users’ value from their social media interactions.
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