scholarly journals Channel Optimization of Marketing Based on Users’ Social Network Information

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chaolin Peng
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Information Technologies ◽  
Information Dissemination ◽  
Product Information ◽  
Main Idea ◽  
Optimal Solution ◽  
User Interest ◽  
Nsga Ii ◽  
The Impact

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence (MOIM) model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions.

scholarly journals Influence Model of User Behavior Characteristics on Information Dissemination

2016 ◽  
Vol 11 (2) ◽  
pp. 209 ◽  
Author(s):  
Shao Chun Han ◽  
Yun Liu ◽  
Hui Ling Chen ◽  
Zhen Jiang Zhang
Keyword(s):  
Human Behavior ◽  
Information Diffusion ◽  
Statistical Physics ◽  
Information Dissemination ◽  
User Behavior ◽  
Activity Patterns ◽  
Propagation Model ◽  
Propagation Time ◽  
Behavior Characteristics ◽  
The Impact

Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals The Impact of User Behavior on Information Diffusion in D2D Communications: A Discrete Dynamical Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chenquan Gan ◽  
Xiaoke Li ◽  
Lisha Wang ◽  
Zufan Zhang
Keyword(s):  
Information Diffusion ◽  
User Behavior ◽  
Transition Process ◽  
Dynamical Model ◽  
Stability Factor ◽  
User Interest ◽  
User Influence ◽  
Proposed Model ◽  
Interaction Factor ◽  
The Impact

This paper aims to explore the impact of user behavior on information diffusion in D2D (Device-to-Device) communications. A discrete dynamical model, which combines network metrics and user behaviors, including social relationship, user influence, and interest, is proposed and analyzed. Specifically, combined with social tie and user interest, the success rate of data dissemination between D2D users is described, and the interaction factor, user influence, and stability factor are also defined. Furthermore, the state transition process of user is depicted by a discrete-time Markov chain, and global stability analysis of the proposed model is also performed. Finally, some experiments are examined to illustrate the main results and effectiveness of the proposed model.

Network Redundancy and Information Diffusion: The Impacts of Information Redundancy, Similarity, and Tie Strength

2016 ◽  
Vol 46 (2) ◽  
pp. 250-272 ◽  
Author(s):  
Hai Liang ◽  
King-wa Fu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Diffusion ◽  
Tie Strength ◽  
Information Redundancy ◽  
Ego Networks ◽  
Core Concepts ◽  
The Impact

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.

scholarly journals Maximizing the Influence of the Innovative Products Diffusion considering Advertisement and Promotion Strategies

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liang’an Huo ◽  
Jianbo Xu ◽  
Jianjia He ◽  
Tingting Lin
Keyword(s):  
Information Diffusion ◽  
Product Information ◽  
Decisive Role ◽  
Innovative Products ◽  
Level Information ◽  
Product Competition ◽  
Information Diffusion Model ◽  
Promotion Strategies ◽  
Promotional Strategies ◽  
The Impact

With the acceleration of product updates and the intensification of product competition, product market strategies become the primary consideration for the enterprises, and the advertisement and promotion strategies are considered the two important strategies implemented by enterprises. This paper considers the enterprises of similar products and substitute, their formation of a competition between traditional products and innovative products, and establishes a mixed node-level information diffusion model to describe the dynamic product diffusion process with complex network theories. We implement advertising strategies for potential buyers who have not obtained product information and implement promotional strategies for those who have obtained product information. In accordance with Pontryagin maximization principle, we seek the best strategy to maximize the impact of innovative products and use numerical calculations to simulate the diffusion state of products. We found that the advertisement strategies play a decisive role in the marketing of innovative products. If product promotion strategies are added, the spread of innovative products will be more effective and more influential.

scholarly journals Improvement of Daylight Factor Model for Window Size Optimization and Energy Efficient Building Envelope Designs

2021 ◽  
Vol 8 (2) ◽  
pp. 204-221
Author(s):  
Chahrazed Mebarki ◽  
◽  
Essaid Djakab ◽  
Abderrahmane Mejedoub Mokhtari ◽  
Youssef Amrane ◽  
...  
Keyword(s):  
Energy Demand ◽  
Research Work ◽  
Optimal Solution ◽  
Window Size ◽  
Building Envelope ◽  
Nsga Ii ◽  
New Approach ◽  
Building Model ◽  
Heating And Cooling ◽  
The Impact

Based on a new approach for the prediction of the Daylight Factor (DF), using existing empirical models, this research work presents an optimization of window size and daylight provided by the glazed apertures component for a building located in a hot and dry climate. The new approach aims to improve the DF model, considering new parameters for daylight prediction such as the orientation, sky conditions, daytime, and the geographic location of the building to fill in all the missing points that the standard DF, defined for an overcast sky, presents. The enhanced DF model is considered for the optimization of window size based on Non dominated Sorting Genetic Algorithm (NSGA II), for heating and cooling season, taking into account the impact of glazing type, space reflectance and artificial lighting installation. Results of heating and cooling demand are compared to a recommended building model for hot and dry climate with 10% Window to Wall Ratio (WWR) for single glazing. The optimal building model is then validated using a dynamic convective heat transfer simulation. As a result, a reduction of 48% in energy demand and 21.5% in CO2 emissions can be achieved. The present approach provides architects and engineers with a more accurate daylight prediction model considering the effect of several parameters simultaneously. The new proposed approach, via the improved DF model, gives an optimal solution for window design to minimize building energy demand while improving the indoor comfort parameters.

scholarly journals An Information Dissemination Influence Model for Mobile Social Network under Multi-Role View

2020 ◽  
pp. 2150001
Author(s):  
Jianfeng Li ◽  
Fangshuo Li ◽  
Wenxiang Wang ◽  
Jun Zhai
Keyword(s):  
Social Network ◽  
Business Strategy ◽  
Information Technologies ◽  
Information Dissemination ◽  
Multiple Roles ◽  
Mobile Social Network ◽  
Influence Model ◽  
Social Network Influence ◽  
New Business ◽  
Social Management

Mobile social networks are dominating in our society’s daily life because of fast advancements of information technologies. To further exploit benefits from the ubiquitous service, studying the influence of information dissemination in this kind of social network becomes a necessity. This paper proposes a mobile social network influence model with regard to multiple roles. In the model, the concept of group is adopted to analyze a user’s role in different contexts. Through the introduction of role’s level and group’s relativity, information dissemination can be investigated deeply, and then, with the Floyd-Warshall algorithm, information strength matrix is constructed to study each node’s influence and under-influence indexes in the network, in addition, the comprehensive influence under multi-role view is also expressed distinctly in the fuzzy form. The result of this research will help find out preferable information disseminators as a new business strategy in e-commerce. Furthermore, it is also useful for detecting gossips and controlling its dissemination in social management.

scholarly journals A Spread Willingness Computing-Based Information Dissemination Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Haojing Huang ◽  
Zhiming Cui ◽  
Shukui Zhang
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
Evolution Equations ◽  
Evolutionary Relationship ◽  
Node Degree ◽  
Large Area ◽  
Frequency Of Contact ◽  
Wide Range ◽  
The Impact ◽  
The Relationship

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user’s spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

scholarly journals Designing a bi-objective decision support model for the disaster management

2021 ◽  
Author(s):  
Sina Nayeri ◽  
Ebrahim Asadi-Gangraj ◽  
Saeed Emami ◽  
Javad Rezaeian
Keyword(s):  
Objective Function ◽  
Disaster Management ◽  
Performance Metrics ◽  
Pareto Frontier ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Test Problems ◽  
Decision Support Model ◽  
Nsga Ii ◽  
The Impact

This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a Bi-Objective Mixed Integer Programming (BOMIP) model is proposed to assign and schedule of the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents and the second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to more similar to the real-world, time-windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position on the corresponding relief team and fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the optimal solution in the reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of  the proposed algorithms are compared with respect to different performance metrics in large size test problems. Finally, in order to investigate the impact of some parameters on the Pareto frontier, the sensitivity analysis and the managerial suggestions are provided.

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