Sharing economy services users’ digital profiles in Russia: topic modeling approach

2021 ◽  
pp. 183-206
Author(s):  
Vera Rebiazina ◽  
Eduard Tunkevichus
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Consumer Behavior ◽  
Topic Modeling ◽  
Business Models ◽  
User Behavior ◽  
Digital Environment ◽  
Sources Of Information ◽  
Digital Platforms ◽  
Wide Range

The transformation of society and the development of digital technologies have significantly affected consumer behavior: consumer identity is now spreading to digital environment, with a new segment of digital consumers being developed. As a result of digitalization, new business models are emerging, for example, commercial sharing systems, the full functioning of which is impossible without the existence of digital platforms and the Internet. Despite the popularity of the topic of commercial sharing systems in the research environment and a wide range of tools used in research, at the moment no attempts have been made to study a digital profile of commercial sharing services users based on the analysis of their social networks profiles. Social network data are one of the most extensive sources of information about consumers: the ability to analyze consumer behavior in social networks can become a significant competitive advantage for companies, as it allows them to quickly extract objective information about the users. The objective of the study is to develop digital profiles of commercial sharing systems’ users based on their digital footprint data. The empirical basis of the study is the publications (posts) of commercial sharing communities’ subscribers on a popular Russian social network VKontakte. The information posted by users in social networks was collected using Python (the API, Application Programming Interface are used), the sample size comprises 24,000 profiles. The collected data have been processed and analyzed using the topic modeling method, as a result of the analysis, 12 main topics are identified characterizing users’ interests. Based on individual topic profiles, topic profiles of communities are formed, furthermore, differences in the digital behavior commercial sharing systems profiles were identified. The application of data on user behavior in digital environment creates new opportunities for digital companies and can become the basis for improving the performance of personalization services, timely adaptation of product offers and approaches to interaction with customers, as well as become the basis for the development of ecosystems.

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.

Developing Marketing Strategy on Social Networks

2018 ◽  
pp. 66-82
Author(s):  
Boris Milović
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Nonprofit Organizations ◽  
Public Awareness ◽  
Winning Strategy ◽  
Marketing Performance ◽  
Wide Range ◽  
And Training

Social networks have proven to be very convenient and effective medium for the spreading of marketing messages, advertising, branding and promotion of products and services. Social networks offer companies, nonprofit organizations, political parties etc. sending certain messages for free. In addition, they allow companies access to a wide range of characteristics of their users. Developing appropriate, the winning strategy for marketing in social media is a comprehensive, time-intensive process therefore it is important to know to manage their content. Social networks transform certain classical approaches to marketing. They provide creative and relatively easy way to increase public awareness of the company and its products, and facilitate obtaining feedback and decision making. These are sources of different information about users and groups that they've joined. The success itself of marketing performance on a social network depends on the readiness and training of organizations to perform on them.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

scholarly journals Development of technology for controlling access to digital portals and platforms based on estimates of user reaction time built into the interface

2020 ◽  
Vol 8 (6) ◽  
pp. 34-46
Author(s):  
S. G. Magomedov ◽  
P. V. Kolyasnikov ◽  
E. V. Nikulchev
Keyword(s):  
Reaction Time ◽  
Information Security ◽  
User Behavior ◽  
Personal Characteristics ◽  
Mass Scale ◽  
Event Management ◽  
Time Data ◽  
User Behavior Analysis ◽  
Digital Platforms ◽  
Wide Range

The paper addresses the development of technology for controlling access to digital portals and platforms based on assessments of personal characteristics of user behavior built into the interface. In distributed digital platforms and portals using personal data, big data is collected and processed using specialized applications using computer networks. In accordance with the law, the data is stored on internal corporate servers and data centers. Special attention is paid to the tasks of differentiation and control of access in modern information systems. Wide availability and mass scale of services should be accompanied by more careful control and user verification. Access control to such systems cannot be ensured only through technologies and information security tools; efficiency can be increased through software and hardware architectural solutions. The paper proposes to expand the currently developing SIEM technology (Security information and event management), which combines the concept of security event management and information security management, with blocks of user behavior analysis. As a characteristic that can be measured without overloading communication channels and is independent of the type of device used, the psychomotor reaction time is proposed, measured as the performance of actions with the interface. A technological solution has been developed for implementation in a wide range of digital platforms: banking, medical, educational, etc. The results of experimental research using a digital platform of mass psychological research are presented. For the research, data from a mass survey were used when answering (in the form of a choice from the available options) to questions about the level of education. Analysis of the reaction time data showed the possibility of standardization and the same indicators of specific users when answering different questions.

Application of clustering algorithm in social network user scenario prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 4971-4979
Author(s):  
Xiaoxian Wen ◽  
Yunhui Ma ◽  
Jiaxin Fu ◽  
Jing Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Characteristic Analysis ◽  
User Behavior Analysis ◽  
Multimedia Social Networks ◽  
Scenario Prediction ◽  
User Scenario

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

scholarly journals Election Control in Social Networks via Edge Addition or Removal

2020 ◽  
Vol 34 (02) ◽  
pp. 1878-1885
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Result ◽  
Optimization Problem ◽  
Optimal Solution ◽  
The Social ◽  
Wide Range ◽  
Local Modification ◽  
Edge Addition ◽  
Over Time

We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).

Marketing based on user behavior on Facebook social network through recommender system design

2015 ◽  
Vol 3 ◽  
pp. 27 ◽  
Author(s):  
Bahareh Shadi Shams Zamenjani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
User Behavior ◽  
Real Data ◽  
User Interests ◽  
Second Stage ◽  
Commercial Use ◽  
High Level

t— the influence of social networks among people and at the same time inevitable spread of commercial use of them. Accordingly, in order to sell products, recommender systems designed based on user behavior on social networks, providing a variety of commercial offers tailored to the user. The accuracy of recommender systems that make recommendations to users, and how many of the proposals are accepted by the users is important. In this paper, a recommender system is designed based on user behavior in social network Facebook in two acts and suggests that users purchase their favorite products. The first step is to examine user behavior based on user interests will be given an offer to buy products. In the second stage recommender system uses data mining techniques and suggestions to the user that is associated with their previous purchases. This is real data and the real results of it and it is valid, as well as the results show a high level of accuracy recommender system is designed to offer suggestions to users.

scholarly journals Introduction: the dynamics of digital communication in the Philippines: legacies and potentials

2021 ◽  
Vol 179 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Jozon A Lorenzana ◽  
Cheryll Ruth R Soriano
Keyword(s):  
Business Models ◽  
Digital Communication ◽  
The Philippines ◽  
Special Issue ◽  
Digital Platforms ◽  
Digital Capitalism ◽  
Wide Range ◽  
New Business ◽  
High Level ◽  
Networked Publics

This special issue brings together six research articles that speak to the dynamics of digital communication in the Philippines, a country firmly located in the global geography of the digital economy and an early adopter and innovator in mobile communication. Increasingly, the rise of digital platforms is spurring on new business models and applications that find a wide range of appropriations in a developing economy with a high level of communication skills and a high level of inequality. These dynamics have, in turn, fuelled the popularity of social media and the populism that has gained international attention and, more critically, taken the country into uncharted political terrain. We introduce this Special Issue by taking stock of the legacies and potentials of digital communication in the country and highlighting how the articles sustain and extend past conversations. Drawing from the articles that cover a range of topics (entertainment, intimacy, labour, journalism and politics, scandals and pornography), we identify three overlapping themes that capture the socio-technical dynamics of digital communication in the Philippines: (1) how digital communication is emplaced in material, social and structural conditions; (2) the potentials of networked publics and communication; and (3) the convertibility of capitals and emergence of new competencies. These dynamics and potentials point to the contradictions, continuities and changes that relate to Philippine modernity in the context of global digital capitalism.

scholarly journals Tools for advancing research into social networks and cognitive function in older adults

2013 ◽  
Vol 26 (4) ◽  
pp. 533-539 ◽  
Author(s):  
Hiroko H. Dodge ◽  
Oscar Ybarra ◽  
Jeffrey A. Kaye
Keyword(s):  
Older Adults ◽  
Social Networks ◽  
Social Network ◽  
Cognitive Function ◽  
Health Outcomes ◽  
Social Engagement ◽  
Brain Aging ◽  
Epidemiological Studies ◽  
Dementia Risk ◽  
Wide Range

People are good for your brain. Decades of research have shown that individuals who have a larger number of people in their social network or higher quality ties with individuals within their network have lower rates of morbidity and mortality across a wide range of health outcomes. Among these outcomes, cognitive function, especially in the context of brain aging, has been one area of particular interest with regard to social engagement, or more broadly, socially integrated lifestyles. Many studies have observed an association between the size of a person's social network or levels of social engagement and the risk for cognitive decline or dementia (e.g. see review by Fratiglioni et al., 2004). The dementia risk reduction associated with a larger social network or social engagement shown by some epidemiological studies is fairly large. The population effect size of increasing social engagement on delaying dementia disease progression could exceed that of current FDA approved medications for Alzheimer's disease.

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