Friends’ Inﬂuence Driven Users’ Value Change
Prediction from Social Media Usage
Md. Saddam Hossain Mukta1, Ahmed Shahriar Sakib2, Md. Adnanul Islam3,
Mohammed Eunus Ali4, Mohiuddin Ahmed5, and Mumshad Ahamed Rifat1
1United International University, Dhaka, Bangladesh
2American International University Bangladesh
3Military Institute of Science and Technology
4Bangladesh University of Engineering and Technology
5Edith 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. The value priority of a person may change over time due
to diﬀerent factors such as life experiences, inﬂuence, social structure
and technology. In this study, we show that we can predict the value
change of a person by considering both the inﬂuence of her friends
and her social media usage. This is the ﬁrst work in the literature
that relates the inﬂuence of social media friends on the human value
dynamics of a user. 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
her value over time. Then, to predict better, we use a particle swarm
optimization based hyperparameter tuning technique. We observe that
these optimized hyperparameters produce more accurate future value
score. We also run our approach with diﬀerent machine learning based
methods and ﬁnd support vector regressor (SVR) outperforms other
regressor models. By using SVR with the best hyperparameters of BCM
model, we ﬁnd the lowest Mean Squared Error (MSE) score as 0.00347.
Keywords: Values ·Facebook Friends ·Inﬂuence ·BCM ·
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) , and personality  of users. Values represent
one’s attitudes, opinions, thoughts, and goals in life. Values of an individual
might change over time due to the inﬂuence of her friends [10,14]. In this paper,
we predict users’ value dynamics (change of value scores over time) based on her
2 Mukta et al.
social media usage (e.g., statuses and comments) and her friends’ inﬂuence in
an egocentric network such as Facebook6.
Values are essentially a set of criteria such as security, self-enhancement,
etc., that inﬂuence individuals to take diﬀerent actions. Chen et al.  predict
ﬁve higher-level values from user word usages in Reddit. In another study ,
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 users’ value priorities (over time) 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 the change
of value scores of users that considers both the inﬂuence of her friends and
her social network interactions. 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.
Based on the above observation, we propose a technique to identify the
changes of a user’s value from her social network interactions with friends by
using Bounded Conﬁdence Model (BCM) [22,11,17]. Users’ value might change
due to other factors; however, for friends’ inﬂuence calculation we use our modiﬁed
version of popular BCM model. We observe the users’ change of values in a time
span of 10 years. Motivated by the work , ﬁrst we collect data of 275 user
networks from Facebook by using a google survey form. We mainly identify
the ego networks with their close friends, i.e., N≤5. 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 .
Then, we generate value scores from users’ each six months interval Facebook
statuses, comments, and shares by using IBM Watson Personality insight API 7.
From the computed value scores of users’ writing content, we compute the
hyperparameters such as convergent factor, µand threshold, and σ, for our
modiﬁed BCM model (see Equation 1). Then, we use particle swarm optimization
(PSO)  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 . In summary, our
contributions in this paper can be highlighted as follows:
–We are the ﬁrst to propose a value change identiﬁcation technique that
predicts user value change dynamics by considering the inﬂuence of friends
and her social media interaction.
6Facebook is an example of an egocentric network  because the network provides
interaction capability only among the friends while preventing any interactions from
the external users to this network.
Value Changes by Friends’ Inﬂuence 3
–We modify the BCM model and capture the friends inﬂuence in an ego
–We develop a PSO based best hyperparameter selection method that predicts
user’s future value score with less MSE score.
2 Preliminaries and Related Work
Basic human values deﬁne the goal, belief and behavior of an individual.
Schwartz et al.  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 .
Self-transcendence satisﬁes the motivational goal for preservation and
enhancement of the welfare of people in the society . Self-enhancement
represents a person’s interest to be socially recognized and attraction for control
over other humans and resources in the society. Conservation emphasizes order,
self-restriction, preservation of the past, and resistance to change. Hedonism
basically means pleasure or sensuous gratiﬁcation for oneself .
2.2 Modiﬁed Bounded Conﬁdence Model (BCM)
BCM is a popular opinion dynamics model to determine the inﬂuence of a
network of people over an individual. Motivated by the BCM devised by Deﬀuant
et al. , we revise the model (see Equation 1) and introduce users’ value change
model. Considering the distance of the corresponding values between two users
(i.e., an ego and each alter) is less than a given threshold σ, the updated value
of each of the users can be computed by using the following equation:
BH V t+1
i=BH V t
j=1(BHV tj−BH V ti)
j=1(BHV tj−BH V ti)
Here, BHViis the value score of the user i,jrepresents her friends, 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 8. According to Dunbar
number, friends can inﬂuence who fall in the intimate sphere where N≤5.
2.3 Values in Social Media
Chen et al.  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 from statuses of 767 Facebook users. They identify
values with a data driven approach. Mukta et al.  identify values from both
4 Mukta et al.
user generated and supported contents in Facebook. Mukta et al.  predict
the temporal change of values of Facebook users by using hybrid LSTM model.
In this paper, we devise a novel technique to identify the value change of an
individual by the social inﬂuence of her friends in Facebook.
2.4 Value Changing Inﬂuence Models
The value of a user might be changed due to diﬀerent oﬄine behaviors such as
life experiences, life events, technological change, social structure, and life style
of others  . In previous studies [21,20], we ﬁnd that value change of an
individual reﬂects in their social media usage behavior, where authors capture
the opinion change over time from social media. Quattrociocchi et al.  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 in diﬀerent time intervals and analyze the interactions
among users. Then, we measure how the value of one person may inﬂuence the
change of value of others through social media interactions.
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: Methodology 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 )
diﬀerent Facebook ego networks where each user holds an ego network and his
Value Changes by Friends’ Inﬂuence 5
Facebook friends are alters. Initially, we randomly 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 comment of each user. Users’ collect their data from their own
proﬁle 9and download her data 10 in diﬀerent time intervals with JSON format.
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 the users based on the number
of their daily public interactions. Table 1 shows the statistics of our dataset.
Table 1: Statistics of our dataset
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 propose a new technique to investigate the change of values over time.
To do so, we apply the BCM model , where we use two hyperparameters:
convergence factor (µ)and threshold (σ)of value diﬀerence. To optimize these
hyperparameters, we use a machine learning based approach to determine the
optimum values (i.e., solution) by using PSO algorithm (Figure 1). We actually
tune the hyperparameters of the regression models. 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 the
Dunbar number . We are interested in showing that when a number of users
ujinﬂuence to value score of a single user (e.g. an ego), her value score converges
to a single uniﬁed score . Hence, in this study, we assume that users may shift
their value scores by the inﬂuence of close friends who fall in the sphere where
6 Mukta et al.
Fig. 2: Group wise inﬂuence based value change from time t1(Fig a) to t2(Fig b)
Similar to a group discussion, we select a primary user to detect his/her value
change from the inﬂuence of other people of the group. Figure 2(a) shows that
independent value scores for a total of 5 Facebook users. When they interact with
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 this
change of user’s value by using our modiﬁed BCM model. The model has diﬀerent
hyperparameters that we optimize by using Particle Swarm Optimization (PSO)
method as discussed next.
4.2 Hyperparameter Estimation
During hyperparamer estimation, for brevity and simplicity we only consider
hedonism value dimension among all top-level 5 value dimensions. We collect
users’ pleasure seeking behavior, i.e., hedonism, by easily extracting their digital
footprints (i.e., restaurant/movie check-ins, purchasing a gadget, etc.) from
Facebook. We could also collect users’ food related data from their check-ins of
diﬀerent restaurants. The convergence factor, µindicates the momentum term
of the inﬂuence dynamics. In our study, we consider µ= 0.4 following the study
of . 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 minimizes the error to the least. To build the regression model, we
consider four features for the training instances - i) value of user, ui, at time
t, ii) value of user’s friend, uj, at time t, iii) value of user, ui, at time t+1,
and iv) convergence Factor, µ. We perform several regression models with these
features by a 10-fold cross-validation with 10 iterations. We use the following
Value Changes by Friends’ Inﬂuence 7
regressors: SVR, Gaussian Process Regressor (GPR), ElasticNet, BayesianRidge,
and MLPRegressor (MLPR) 11.
For hyperparameter tuning, we apply PSO 12 by using Optunity Library 13.
PSO  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. ParticleSwarm 14
has 5 parameters that can be conﬁgured: num_particles, num_generations,
φ1,φ2,and max_speed. In our experiment, we use num_particles = 10 ,
num_generations = 15 ,max_speed = None, φ1= 1.5 , φ2= 2.0 to initialize
ParticleSwarm 15 solver in Optunity. In our case, we consider one set of
hyperparameter conﬁguration as a single particle.
We split the dataset into training, and test data by 70% (a total of 193
instances) and 30% (a total of 82 instances), respectively. Then, we deﬁne our
objective function to minimize the cost of the model which in this case is,
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 values, which are evaluated by the cost function
to be minimized. The particles move through the problem space by following
the current optimum particles. Table 2 shows the optimized hyperparameter
conﬁguration values for diﬀerent regressors.
Table 2: Hyperparameters conﬁguration for diﬀerent regressors
Regression Model Hyperparameter Conﬁguration
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]
Since Optunity can optimize conditional search spaces, we set diﬀerent
hyparameters for SVR based on the kernel (e.g., Radial Basis Function (RBF),
linear, and polynomial. Table 2 shows the search space for SVR hyperparameters.
Moreover, Table 3 shows the performance of the regressors over our test dataset.
11 SVR: https://bit.ly/2OOBDZa, GPR: https://bit.ly/2OWxAKy, ElasticNet:
https://bit.ly/3pDuh7M, BayesianRidge: https://bit.ly/3qJoyys, MLPR:
13 https://homes.esat.kuleuven.be/ claesenm/optunity/
8 Mukta et al.
Table 3: Strength (Low RMSE) of the regression model
Regression Algorithm MSE
Gaussian Process Regressor 0.07138
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
uias value of a user
uj. . . n as values of group users
ui+tas value of a user in
µas convergence factor ( 0.4 )
σas Threshold Value
Proc.: Dataset_Prep_BCM( )
1: Calculate the threshold value via BCM
2: Set predictor variables (X) : ui,uj.. . n ,
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( )
8: Find the optimum σfor BCM model
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., statuses, comments,
shares, and likes) of 275 users. By using PSO, we ﬁne tune the parameters of the
estimators to get the optimized threshold. With these parameters, we predict
the best threshold, σto predict the accurate value scores by using SVR.
Next, Figure 3 presents the amount of loss when compute the σby using
diﬀerent models. Among these models, SVR shows the lowest loss to predict the
hyperparameters. Use this threshold, σvalue, we predict the best ﬁnal values
score of a user inﬂuenced by her close friends. Figure 4 shows the actual future
value scores and predicted value scores using SVR for diﬀerent users.
Berndt  describes that friendships have inﬂuence on user’s attitude and
behavior. For example, adolescents whose friends drink beer at parties likely to
Value Changes by Friends’ Inﬂuence 9
Fig. 3: Comparison of loss among the
Fig. 4: Comparison between users’
actual and predicted value scores.
start drinking. In contrast, user’s value may inﬂuence negatively. For example,
adolescents often have conﬂicts with others which might propagate among others.
Epstein  and Hartup  describe that friends inﬂuence each other in diﬀerent
behaviors, including aspirations, achievements, values and attitudes. Our study
also shows that friends’ inﬂuence can persuade to change one’s value which can
even be predicable from the social media usage.
In this paper, we have extracted 275 diﬀerent ego networks from a Facebook.
Then, we have identiﬁed intimate friends (N≤5) for each of the ego networks.
Later, 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 modiﬁed 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 Facebook interactions.
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