Revista Argentina de Clínica Psicológica 2020, Vol. XXIX, N°1, 1370-1377
DOI: 10.24205/03276716.2020.198 1370
A
D
ECISION
-M
AKING
M
ODEL FOR
S
OCIAL
N
ETWORK
U
SERS
B
ASED
ON
P
SYCHOLOGICAL
P
ROCESS
Sheng Bin*, Gengxin Sun
Abstract
The traditional models for social network users often ignore the cause of user behavior and the psychological decision mechanism. To make up for the gap, this paper constructs a decision-making model for social network users based on the theory of social psychology and the extended closed-loop psychological process. The established model mainly consists of four process models, namely, emotion incentive model, two-channel decision model, intention fluctuation model and experience feedback model. Through experiments on actual user data of social network, our model was proved more accurate than the traditional user decision-making model. The research results provide new insights into the psychological decision mechanism of user behavior in social networks.
Key words: Psychological Process, Behavior Modeling, Decision-Making Mechanism, Social Network.
Received: 05-02-19 | Accepted: 03-08-19
INTRODUCTION
In recent years, the rise of social networks has connected the human more closely. The research on human behavior and decision-making mechanism has expanded from the individual to the whole social network. Social network user behavior modeling not only needs to consider the user's macro network structure, explore the interaction between users, but also needs to go deep into the user's psychological level. The internal decision-making process of an individual is connected with the external behavior performance, and then a model that conforms to the real behavior characteristics of the individual will be established.
User behavior is an expression of the human will, which reflects the externalization of human internal needs and the influence of external situations. There are three main theories about
School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China.
E-Mail: [email protected]
the internal mechanism of human behavior: internal determinism, external determinism and tripartite reciprocity (Harcum, 1988). According to the theory of internal determinism, behavior is determined by the internal characteristics of the subject, such as psychology, emotion, rationality, motivation, etc. Different individuals have different behaviors due to their internal heterogeneity. Psychology studied the internal mechanism of human behavior, the most famous of which is motivation theory. Starting from the internal driving force of decision-making, the theory holds that when one's internal needs are not met, one's self will would drive one's self to find ways to meet such needs to fill the lack of such needs state. When the needs are met, the inner desire would calm down.
If human is regarded as an information processing system, when the external environment information is input into the information processing system, the system will form corresponding output after information processing, but just because the main body of information processing is human, and the internal psychological mechanism of human is
BIN,SUN 1371
complex, that is, different individuals with the same external information input will have different behavior output. Therefore, we need to start from the social network user's internal psychological process, construct the user's psychological process in the decision-making, and modeling the psychological process can be more suitable for the user's decision-making in the social network.
RELATED WORKS
The early researchers' modeling of the internal mechanism of human behavior mainly stayed in the level of rational reasoning. They thought that the behavior subject could always judge the gains and losses of an action based on some probability functions, which could be used to choose the behavior with the most effective execution. Adams (1962) proposed a mathematical theories' models of human social or individual behavior for decision-making. But it is obviously inconsistent with the reality of decision-making. Kuder (1955) proposed to incorporate emotion as an input factor into cognitive decision-making model. Based on this idea, a large number of theoretical models (Turnbull, 2002; Gutnik, Hakimzada, Yoskowitz et al., 2006; Hollings, 2013; Li, Ashkanasy, & Ahlstrom, 2014) have been proposed, including regret theory, disappointment theory, mood congruence hypothesis, body marking theory, and so on. These theories recognize the role of emotion in decision-making, but only regard emotion as an input factor of decision-making, and do not integrate emotion into cognitive decision-making process.
In recent years, researchers believed that emotion could participate in decision-making process, and proposed some relevant models. Jatupaiboon, Pan-Ngum, & Israsena (2015) defined emotion space through external induced variables. The model considers emotion as a variable that is gradually updated with the changes of mood, personality and external stimulus signals. Sun& Bin (2018) described the occurrence and decision-making of emotion from the perspective of mathematical modeling, and established the mapping relationship between emotion and cognitive decision-making by using complex network, fuzzy mathematics and other mathematical methods. Putzer & Onken (2003) established the mapping function of emotion and cognition, and proposed the
human behavior cognitive model. Fum, Missier, & Stocco (2007) constructed the function of emotion influence on cognition based on probability theory, and established the human behavior model. Liang, Dong, & Wang (2012) proposed the emotion model based on fuzzy logic, and through the combination of the model and neural network, and then use emotion as the re-excitation signal, guide the user to update and learn the decision-making strategy adaptively.
With the increasingly close combination of social elements and various intelligent applications, human's behaviors and intentions are increasingly affected by social network. In order to further understand social network users, psychologists pay attention to and study the relationship between psychological characteristics and social network users' behavior. Robillard, Bouchard, Dumoulin et al. (2010) found that extroverts are more likely to use social networks to alleviate their life anxiety. Hamburger & Ben-Artzi (2000) obtained the behavior data of social network users by questionnaire. They analyzed the relationship between network behavior and different personalities. The results showed that introverts preferred to find a sense of belonging through online communication. Sun & Bin (2017) studied the relationship between social network topology and user behavior decision. These studies mainly focused on the correlation between personality characteristics and social network user behavior.
Because the behavior data and status of social network users are easier to record and obtain, more and more researchers used big data analysis to find the rules and construct personality analysis model and psychoanalysis model. Ding, Yan, Zhang et al. (2015) used the different n-grams set in social network log as the feature, took naive Bayes as the learning algorithm, and analyzed it on the dimension of four personal lattices. They found that binary classification and automatic feature selection can return the best behavior decision classification results. Morgan (1993) used linguistic and social features to predict the behavior decision scores of more than 2000 social network users.
USER BEHAVIOR DECISION MODELING BASED ON PSYCHOLOGICAL PROCESS
A DECISION-MAKING MODEL FOR SOCIAL NETWORK USERS BASED ON PSYCHOLOGICAL PROCESS 1372
in psychology is motivation theory. Famous psychologist Maslow studied motivation theory deeply and proposed a five-level need model (Maslow, 1943), which is shown as Figure 1.
The model classifies human needs from low level to high level as physiological needs, security needs, social needs, respect needs and self-fulfillment needs. The model considers that when the first level needs are met, a higher-level need will be generated, and when the needs are met, the motivation will disappear. For example, when people feel hungry, they have the physiological need to eat, and then they have the motivation to eat, and then they have the behavior of eating, and then the need to eat is met, and the motivation is calmed down.
Figure 1
.
Five-level need model
The motivation theory can explain the behavior decision-making process of users, but it is only from the psychological aspect, which is not conducive to constructing a comprehensive and in-depth user decision-making model. In order to further explore the internal mechanism of user behavior decision-making, in this paper we deeply analyzed each stage of user decision-making, and proposed a user decision-making model framework based on psychological process.
User decision is the whole process from external information input to user behavior output, and then to behavior feedback. Therefore, we regard user decision-making as a psychological process, which can be divided into the following stages: cognitive stage, decision-making stage, action stage, in addition to the external environment and internal user beliefs, they constitute the whole decision-making system. The decision model framework is shown
in Figure 2.
The cognitive stage is the process of information receiving and information processing, including external perception and internal perception. External perception is the process of people obtaining external environment information through vision, hearing, touch, taste, smell, intuition, etc., while internal perception is the process of obtaining their own preferences, mood and needs through internal feelings and self-awareness.
The decision-making stage is the process of further processing and forming behavioral intention for the perceived relevant information. Decision-making is a series of extremely complex thinking process, which is generally considered as a kind of thinking behavior including reasoning, judgment, synthesis, analysis and induction, etc.
Figure 2
.
The framework of decision-making
model
Based on the framework of decision-making, we start from the whole internal psychological process of individual decision-making, a social network user decision model based on extended closed-loop psychological process is proposed.
The model considers that user behavior is not spontaneous, but the change of behavior caused by incentive. Incentive can be measured by incentive intensity 𝑆:
𝑆 = 𝛿1× 𝐼 + 𝛿2× 𝐸 + 𝛿3× 𝑈
where, 𝐼 represents external event incentive, 𝐸 represents environmental incentive, U represents user internal incentive, 𝛿1, 𝛿2 and
𝛿3 are different weights.
When the incentive is perceived by the user through the perception module, the user will generate the corresponding emotion intensity 𝑃
BIN,SUN 1373
according to the corresponding incentive intensity in combination with their own attributes.
𝑃 = 𝐾 × log(1 + 𝑆)
where, 𝐾 represents emotion intensity coefficient, it is related to the user's own attributes. The relationship between emotion intensity and incentive intensity is nonlinear, and the logarithm of emotion intensity and incentive intensity is in direct proportion.
There are two channels in human decision-making, one is deliberation mechanism, the other is reaction mechanism. Deliberation mechanism obtains behavior intention through rational reasoning, while reaction mechanism produces behavior rapidly. The two-channel decision mechanism can be defined as Figure 3.
When the emotion intensity of the output of the perception module reaches the threshold that can trigger the user to make a quick response decision, user would have decision mode conversion. There are two values in the decision-making mode: 0 indicates that the user makes decisions by using the reaction mechanism, and 1 indicates that the user uses deliberation mechanism to make decisions.
Figure 3
.
The mechanism of two-channel
decision-making
In the behavior decision-making model of deliberation mechanism, we use the rational decision-making model for reference. In the rational decision-making model, the definition of user 𝑢 selects a behavior 𝑏𝑖 in behavior set
𝑆𝑏 to maximize the expected utility of the
behavior, 𝑏𝑖∈ 𝑆𝑏, 𝑆𝑏 contains n elements. So
the expected utility function of actor can be
defined as follows:
𝑉(𝑢) = ∑ 𝑝𝑖(𝑢)𝑣𝑖(𝑢) 𝑛
𝑖=1
where, 𝑝𝑖(𝑢) represents the probability that
the user chooses a certain behavior, 𝑣𝑖(𝑢)
represents utility value after selecting an action.
𝑝𝑖(𝑢) = 𝑝(𝑎𝑖) =
𝑒𝑤𝑣𝑖 ∑𝑛 𝑒𝑤𝑣𝑖
𝑖=1
where, 𝑤 represents the degree of rationality in choosing an action, when 𝑤 = 0, it shows that individual decision-making is completely irrational, 𝑝(𝑎𝑖) represents that individual
decision-making is random and does not consider the utility of selecting a certain behavior at all. When 𝑝(𝑎𝑖) = 1 𝑛⁄ , then
𝑉(𝑢) =∑ 𝑣𝑖(𝑢)
𝑛 𝑖=1
𝑛
The general formal description of the decision is defined as follows: There is a set of user decision schemes D = {𝐷1, 𝐷2, … , 𝐷𝑚}, each
scheme contains multiple attribute sets A = {𝐴1, 𝐴2, … , 𝐴𝑛}, weight set of each attribute is
w = {𝑤1, 𝑤2, … , 𝑤𝑛} , 𝑤1+ 𝑤2+ ⋯ + 𝑤𝑛= 1 .
Users use decision matrix to make decisions, and the decision matrix is constructed as follows:
𝑅 = [
𝑥11 ⋯ 𝑥1𝑛
⋮ ⋱ ⋮
𝑥𝑚1 ⋯ 𝑥𝑚𝑛
]
where, 𝑥𝑖𝑗 represents the value of attribute 𝐴𝑗
in scheme 𝐷𝑖.
In decision-making, firstly, the attribute value 𝑥𝑖𝑗 is normalized to get the normalized attribute
evaluation 𝑢𝑖𝑗. According to the utility value 𝑈𝑖,
the scheme is sorted and selected:
𝑈𝑖= 𝑈(𝐷𝑖) = ∑ 𝑤𝑗𝑢𝑖𝑗 𝑛
𝑗=1
As an important form of irrational decision-making, reaction mechanism often plays a supplementary role in deliberate decision-making. In the reaction mechanism, if the emotion intensity of a behavior scheme triggers the reaction mechanism, the user is considered to choose the scheme unconsciously and
A DECISION-MAKING MODEL FOR SOCIAL NETWORK USERS BASED ON PSYCHOLOGICAL PROCESS 1374
intuitively. In user decision schemes set D = {𝐷1, 𝐷2, … , 𝐷𝑚}, if there are 𝑚0 schemes, the
emotion intensity exceeds the trigger threshold, in this case, user will make the scheme selection with equal probability.
EXPERIMENT AND ANALYSIS
In this section, we aim to verify the correctness and validity of the decision-making model based on the extended closed-loop psychological process proposed in this paper. In the experiment, we use the real social network data, and perform the comparison of four experimental results based on different psychological processes with the measured data to illustrate the correctness of the model, and through the comparison with the traditional user decision-making model, to show the validity of the model
The experimental data comes from an organized tour platform in a social network. The platform regularly publishes organized tour related activities, and has held nearly 100 organized tour activities at present. Organized activities usually include two categories: one is short-distance activities, which are mainly around the city, usually held by weekend holidays, and the other is long-distance activities, which are usually held in winter and summer, and need long-term planning and activity preparation. We collected relevant experimental data, including user data (gender, age, occupation, etc.), activity participation data (number of participants in each activity, travel expenses, etc.), social topic data (interaction of chat group, number of social platform posts, etc.).
In this paper, four experiments are carried out on the collected data set. Different micro decision-making modeling methods are used to model and predict the user's activity participation behavior. Different prediction results are obtained by constantly introducing and enriching various psychological processes of user decision-making, so as to compare and analyze with the actual value.
In the first experiment, the general rational behavior decision-making model of users is established, it regards the participation of users in organized tour activities as a result of rational choice of maximizing revenue. The prediction result of the model is recorded as 𝑃1. In the second experiment, a two-channel decision
model was used, it considered the reaction mechanism and deliberation mechanism in the process of user decision-making. The prediction result of the model is recorded as 𝑃2. In the third experiment, on the basis of the second experiment, the user intention fluctuation model is introduced, and the intention fluctuation model is used to modify the decision model to reflect the time dynamics of the decision. The prediction result of the model is recorded as 𝑃3 . The fourth experiment integrated the previous experiments, and further introduced experience feedback model to construct a complete psychological closed-loop process of user decision-making, it considers the influence of past psychological experience on current decision-making. The prediction result of the model is recorded as 𝑃4. The results of the four experiments are shown in Figure 4.
Because cosine similarity can be used to compare the similarity between two groups of data, and it is easy to understand and calculate, in this paper we use cosine similarity calculation formula to calculate and compare the data.
The similarity between the four groups of experiments and the actual data are calculated, and the results are shown in Table 1.
Figure 4
.
Comparison of actual values and
experimental
results
by
introducing
different psychological processes
Table 1.
Similarity between four groups of
experimental data and actual data
Experimental results Similarity
𝑃1 86.21%
𝑃2 87.73%
𝑃3 89.62%
BIN,SUN 1375
As shown in Table 1, if only from the perspective of rational decision-making, the similarity between the predicted result and the actual value is only 86.21%, through enriching the psychological mechanism of user decision-making, the similarity between the predicted value and the measured value of behavior is increasing greatly. It shows that the decision-making process is not only a result of rational judgment, sometimes the user's emotional preference will also affect the decision-making behavior.
In order to verify the validity of the closed-loop decision-making model of psychological process, in this paper the classical expected utility model (EU model) and the subjective expected utility model (SEU model) are compared.
The experimental settings are as follows: It is known that the total number of participants in an activity is N, the set of users participating in the activity is 𝑈 = {𝑢1, 𝑢2, … , 𝑢𝑁}, only these N
users are kept in social network. Three decision models are performed for these users to judge their participation in activities, 𝑋(𝑡) represents the cumulative number of participants output by the model at time t, then 𝑋(t) 𝑁⁄ can be used to evaluate the prediction of three models at time t.
In the selection of activities, this experiment chooses short-distance activities and long-distance activities for comparative analysis. The experiment comparative results are shown in Figure 5 and Figure 6, respectively.
Figure 5
.
Comparison of three models in
short-distance activities
As shown in Figure 5, three decision models are performed in short-distance activities. From the view of running speed, our model converges when t = 11, however, EU model and SEU model do not converge. It means that our model has a better operation efficiency. EU and SEU models are two typical deliberate decision models in uncertain environments. The two models continuously evaluate the risks and benefits of behaviors and select the optimal (maximized) behavior results. In our model, the two-channel decision-making mechanism is introduced into the decision-making process, so that some users can make quick decisions directly through the reaction mechanism without repeated rational decision-making, which improves the operation efficiency of the model compared with other models.
In addition, the final accuracy of our model is 87%, EU model and SEU model are 77% and 82%, respectively. It can be seen that the output results of our model have better accuracy. It is because our model comprehensively considers the psychological process in decision-making, which makes the output of the decision-making model closer to the decision-making and selection of natural person, and improves the accuracy of our model.
Figure 6
.
Comparison of three models in
long-distance activities
As shown in Figure 6, it can be seen from the data of long-distance activities that our model is more effective and accurate than other two models. However, through further analysis of the accuracy, we can find that the accuracy of
A DECISION-MAKING MODEL FOR SOCIAL NETWORK USERS BASED ON PSYCHOLOGICAL PROCESS 1376
our model is higher in long-distance activities than in short-distance activities. The reason may be that the long-distance activity has a long preparation period, and the final participants of the activity are variable. Compared with other models, the advantage of our model is that it introduces the intention fluctuation process to simulate various psychological phenomena of participants.
CONCLUSIONS
In this paper, we used psychological method to study the user modeling problem in social network. We took the user in social network as the modeling subject, extracted the psychological needs of user modeling problem, and constructed the user decision-making model based on the closed-loop psychological process. The validity and correctness of our model are verified by the actual data of social network, and the superiority of our model is verified by comparing with other classical models.
Acknowledgments
This research was funded by Shandong Provincial Natural Science Foundation, China, grant number ZR2017MG011. This research is also supported by the Humanity and Social Science Youth foundation of Ministry of Education of China (grant no. 15YJC860001), National Statistical Science Research Project (grant no. 2017LZ38).
REFERENCES
Adams, E. (1962). Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. The Journal of Philosophy, 59(7), 177-182. Ding, Y., Yan, S., Zhang, Y. B., Dai, W., & Dong, L.
(2015). Predicting the attributes of social network users using a graph-based machine learning method. Computer Communications,
73, 3-11.
Fum, D., Missier, F. D., & Stocco, A. (2007). The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words. Cognitive Systems Research, 8(3), 135-142.
Gutnik, L. A., Hakimzada, A. F., Yoskowitz, N. A.,
& Patel, V. L. (2006). The role of emotion in decision-making: a cognitive neuroeconomic approach towards understanding sexual risk behavior. Journal of Biomedical Informatics,
39(6), 720-736.
Hamburger, Y. A., & Ben-Artzi, E. (2000). The relationship between extraversion and neuroticism and the different uses of the internet. Computers in Human Behaviour,
16(4), 441-449.
Harcum, E. R. (1988). A classroom demonstration of determinism, prediction, and control of human behavior. Psychology A Journal of Human Behavior, 25, 41-43.
Hollings, J. (2013). Let the story go: the role of emotion in the decision-making process of the reluctant, vulnerable witness or whistle-blower. Journal of Business Ethics, 114(3), 501-512.
Jatupaiboon, N., Pan-Ngum, S., & Israsena, P. (2015). Subject-dependent and subject-independent emotion classification using unimodal and multimodal physiological signals. Journal of Medical Imaging and Health Informatics, 5(5), 1020-1027.
Kuder, J. M. (1995). The current environment for medical decision making: alternative efficiency concepts and decision motivation.
Motivation & Emotion, 19(3), 221-236. Li, Y., Ashkanasy, N. M., & Ahlstrom, D. (2014).
The rationality of emotions: a hybrid process model of decision-making under uncertainty.
Asia Pacific Journal of Management, 31(1), 293-308.
Liang, J., Dong, D., & Wang, X. (2012). An artificial endocrine-emotion model based on fuzzy logic. Information Engineering & Applications, 154, 307-314.
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370-396.
Morgan, P. D. (1993). Simulation of an adaptive behavior mechanism in an expert decision-maker. IEEE Transactions on Systems, Man and Cybernetics, 23(1), 65-76.
Putzer, H., & Onken, R. (2003). Cosa-a generic cognitive system architecture based on a cognitive model of human behavior.
Cognition, Technology & Work, 5(2), 140-151. Robillard, G., Bouchard, S., Dumoulin, S., Guitard, T., & Klinger, E. (2010). Using virtual humans to alleviate social anxiety: preliminary report from a comparative outcome study. Stud Health Technol Inform,
BIN,SUN 1377
154, 57-60.
Sun, G., & Bin, S. (2017). Router-Level Internet Topology Evolution Model based on Multi-Subnet Composited Complex Network Model.
Journal of Internet Technology, 18(6), 1275-1283.
Sun, G., & Bin, S. (2018). A new opinion leaders
detecting algorithm in multi-relationship online social networks. Multimedia Tools and Applications, 77(4), 4295-4307.
Turnbull, O. (2002). Decision-making, emotion, and cognitive neuropsychiatry.
