Analyzing the Co-evolution of Network Structure and Content Generation in Online Social Networks

With the rapid growth of online social network sites (SNS), it has become imperative to investi-gate what drives content production on these platforms. We posit that the content producing behavior of users is influenced not just by their personal attributes like age and gender, but also by their social network structure. However, it is empirically challenging to estimate network structure and behavior through traditional approaches as the social network structure and the content production behavior influence the evolution of each other. In the current study, we adapt an actor-based continuous-time model to jointly estimate the co-evolution of the users\u27 social network structure and their content production behavior using a Markov Chain Monte Carlo (MCMC) based simulation approach. We apply our model to an online social network of university students and uncover strong evidence for both social influence and homophilous friend selection. Interestingly, we find that individuals befriend others who are similar in content production during the friendship formation stage, but gradually diverge in their content production behavior from these similar others over time. We offer potential explanations for this phenomenon and emphasize the importance of these findings for platform owners and product marketers

Modelling Heterogeneous Effects in Network Contagion: Evidence from the Steam Community

This study considers heterogeneous effects of reviews and social interactions on diffusion or contagion of new products in a networked setting, using a sample of interconnected public user profiles from the Steam Community. Ownership and reviews of two cult hit independent games – The Binding of Isaac: Rebirth, and To the Moon – are analyzed over a period of four years. This data was fit with a Hawkes Process Hazard Regression Model with exponential decay kernels for each game, yielding estimates of scale and duration of incremental heterogeneous actions within the network. This analysis finds strong, short term, additive, and marginally decreasing, social contagion effects from other users buying games, with much smaller, but also far more durable and highly significant, effects from review posting behavior in the network, independent of review quality. This seems to suggest that review influence, while still distinguishable from network homophily, is unlikely to lead to cascade effects

잠재 심리학 변수 및 사회적 상호작용을 고려한 새로운 일반화된 이질적 데이터 모형

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 이종수.사회적 상호작용은 개인의 태도 및 의견, 개인의 선호, 그리고 개인의 행동에 중요한 영향을 미친다. 따라서 사회적 상호작용은 사회과학 분야에 매우 중요한 연구 대상이다. 사회적 상호작용은 크게 구전 효과와 관찰된 학습 두가지로 나눌 수 있다. 기존 경제학 분야의 선택 모형에서는 주로 관찰된 학습을 사회적 상호작용으로 간주하였다. 구전 효과는 태도나 행위의 경향성 분석에 많이 연구되어 왔지만 선택 모형에 고려하는 것은 어려움이 많았다. 그리고, 많은 선택모형에서 개인의 의사결정 과정을 더 잘 이해하고 모형의 예측력을 향상시키기 위해 개인의 잠재적 심리학 변수를 추가하고자 하였다. 사회적 상호작용이 개인의 심리학적 특성에 많은 영향을 미친다는 것은 이미 많은 연구를 통해서 입증되어 왔다. 하지만 사회적 상호작용이 개인의 잠재적 심리학 변수에 미치는 영향을 다룬 연구는 매우 드물었다. 또한, 개인의 행동 사이에 존재한 상관관계를 선택모형에 동시에 다루지 못하면 편향된 추정결과를 얻을 수 있다. 혼합형 종속변수를 동시에 추정하는 모형은 꾸준히 개발되어 왔지만 여기에 사회적 상호작용을 고려한 연구는 찾아보기 힘들다. 본 연구에서는 두가지 사회적 상호작용을 모두고려하는 다중 선택 모형을 제안하였다. 제안한 선택 모형은 사회적 상호작용을 고려할 수 있을 뿐만 아니라 내생적 다중 선택을 다룰 수 있으며 혼합적 종속변수까지 포함할 수 있다. 본 연구에서 먼저 시뮬레이션 분석을 통해서 모델의 유용성을 입증한 다음에 실증분석으로 기존 모델에서 확인할 수 없었던 사회적 상호작용이 개인의 행위에 미치는 영향을 분석하였다. 사회적 상호작용을 고려하지 않으면 추정된 결과가 편향될 수 있으며 변수의 계수를 과대 추정할 가능성이 있다는 것을 실증연구를 통해서 증명했다.Social interaction has enormous effect on individuals attitude and opinion, preferences, and behaviors. Social interaction is one of the most important study regions within social sciences. There are generally two types of social interaction: word-of-mouth and observed learning. Observed learning is considered as social interaction is majority of choice models in economic studies. However, word-of-mouth is largely studied in attitude and behavior propensity related studies and hardly ever been incorporated in choice models due to the characteristics of the word-of-mouth. On the other hand, choice models have tried to incorporate individuals psychological variables in order to get better understanding of individual decision process and to improve the forecasting ability of the models. However, there are limited studies have considered the effect of social interaction on the individuals psychological variables which is one of the major mechanisms of social interaction. Moreover, since individuals behaviors endogenously correlated themselves, consideration of endogenously correlated outcomes simultaneously is necessary for many choice situations. Though there are some studies derived handful model for multiple choices, only a few models have incorporated the social interaction into the model. This study proposes a new multiple endogenous choice model incorporating both types of social interaction. Furthermore, the proposed model is capable of dealing with multiple endogenous heterogenous dependent variables. The dissertation conducts a simulation study to confirm the performance of proposed model and an empirical study to provide evidence of how social interaction effect on individuals choice and proof that ignoring the effect of social interactions may leads to inconsistent estimation and may over-estimated the variable effects.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Object 4 1.3 Research Outline 8 Chapter 2. Literature Review 10 2.1 Theoretical Insights 10 2.1.1 Studies on Human Behavior 11 2.1.2 Studies on Word-of-Mouth 14 2.2 Choice Models 20 2.2.1 Choice Models with Psychological Factors 20 2.2.2 Choice Models with Spatial/Social Dependence 32 2.2.3 Models of Mixed Data 41 2.3 Limitations of Previous Research and Research Motivation 46 Chapter 3. Model Specification 48 3.1 Latent Psychological Variable Structural Equation Model 49 3.2 Latent Variable Measurement Equation Model 50 3.2.1 Single Dependent Variable 50 3.2.2 Multiple Dependent Variables 52 3.3 Estimation Methodology 60 3.4 Simulation Study 62 3.4.1 Simulation Design 62 3.4.2 Simulation Results 65 Chapter 4. Empirical Study 73 4.1 Empirical Study Background and Specification 73 4.1.1 Latent Psychological Variables 75 4.1.2 Endogenous Outcomes 78 4.2 Data Description 80 4.3 Estimation Results 87 4.3.1 Structural Equation Model for Latent Psychological Variables 87 4.3.2 Effect of Latent Psychological Variables on Endogenous Outcomes 90 4.3.3 Comparison of the GHDM models 96 Chapter 5. Conclusion 100 5.1 Concluding Remarks and Contribution 100 5.2 Limitations and Future Studies 103Docto

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure

Understanding the Formation of Information Security Climate Perceptions: A Longitudinal Social Network Analysis

Business process capture is a first step in the larger endeavour of business process management. In this paper we view business process capture as a knowledge conversion process. We explore the conversion of knowledge when business analysts capture information about business processes from domain experts. We identify seven process capture activities in a thematic analysis of comments made by business analysts in response to open-ended questions in an online survey. The seven activities are involving, simplifying, tailoring, training, combining, confirming, and engaging soft skills. We show how these activities involve the transfer of tacit and explicit knowledge between the business analyst and the domain expert and how the transfer conforms to the SECI modes of knowledge conversion, well known in the research domain of knowledge management. The paper contributes a SECI-based knowledge conversion model of business process capture and insight for business analysts about business process capture activities

Identifying communicator roles in Twitter

Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge