수면확산의 네트워크적 특성에 대한 연구
Network Properties of Sleeper Diffusion
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인터넷 공간에서 대부분의 정보는 매우 빠르게 확산되고 소멸된다. 그러나 소수의 정보들은 이런 패턴을 따르지않고, 시간을 두고 천천히 확산된다. 본 연구는 이 두 가지 확산패턴 중에서 시간을 두고 천천히 확산되는, 수면확산(sleeper diffusion)을 하는 정보들이 어떤 이유로 발생하게 되는지를 네트워크 특성변수를 통해 설명하고자 하였다. 이를 위해 본 연구는 수면확산을 하는 정보 34개를 표집하여, 정보의 생산자, 조기수용자와 조기수용자의 이웃집단의 네트워크 특성을 측정하여, 수면확산이 일어나지 않은 정보 108개 아이템과 비교하였다. 연구 결과, 정보생산자의 연결정도의 직접효과와 조기수용자의 이웃집단의 정보수용을 촉진하는 조기수용자의 사회적 압력이 가지는 간접효과를 발견하였다. 다시 말해, 정보 생산자에 의한 Random Seeding과 조기수용자 집단의 네트워크 구조에 기반한 사회적 압력이 정보의 수면확산과 관계됨이 설명되었다. 이는 허브의 초기수용으로 설명되는 대부분의 급격한 정보 확산과정과는 다르다. 특히 본 연구는 기존 연구들이 관심대상으로 삼지 않던 느린 정보확산을 연구대상으로 함으로써 정보확산에 대한연구의 범위를 확장하였으며, 수면확산이 정보생산자의 연결정도, 조기수용자들의 사회적 압력의 간접적 효과로 발생함을 규명하였다는 점에서 시사점을 갖는다.
Abstract
Most of information on social network diffuses and disappears in a short time, while some information disperses slowly in the early stage until the diffusion takes off in the late stage. In this paper, authors focused on slowly dispersed information. We call these “sleeper diffusion”. In order to study the determinants of them, 34 of sleeper diffusion items are sampled and compared with 108 non-sleepers, in terms of senders, early adopters and neighbors’ of early adopters’ (hereafter ‘the neighbor group’) network properties. The average of 35.23 people adopted non-sleepers, and 70.46% of penetration rate had attained until 7th day. But that trend started to retard after 7th day. In short, the diffusion was concentrated in the early days. Contrary to non-sleepers, sleepers had attained only 3.06% penetration rate during the first 30 days by the average of 66.09 people. However, it was adopted by the average of 2,163 people because diffusions took off after a month from the posting (Table 1, Figure 1). We specified the path model using variables related with information adoption behavior and network properties of sender, early adopters and the neighbor group. Initially 9 variables, 2dependent variables and 7 independent variables, were measured, however, 3 variables were not included in path model due to multicollinearity or insignificant difference between information item groups. So the path model consisted with 6 variables as listed in Table 2. Dependent variables are the dummy variable for sleeper diffusion and the adoption rate of the neighbor group (level of information cascade by neighbor group). Independent variables, as below, are the network properties of three parties; sender, early adopters and the neighbor group. First, the degree of sender is measured as their influence on random seeding. Second, as network properties of early adopters, the mean and the skewness of their degree and network density among them are measured. However, only the mean of degree is included in path model due to multicollinearity among the variables. Third, three variables are measured for the neighbor group; the overlap ratio of neighbor group which shows the social pressure by early adopters (Equation 1), and the size and the mean of degree of the neighbor group. But the size of the neighbor group is excluded which have insignificant difference between information item groups. Consequently, the path model was specified using the variables in Table 2 (Figure 2). The model has adequate goodness-of-fits (χ2=23.8, df=8, p=.002, GFI=.947, CFI=.944). In the model, authors found the direct effect of the degree of sender and the indirect effect of the overlap ratio of neighbor group. In other words, random seeding by sender and social pressure by early adopters are accelerating the neighbor group’s information adoption. This diffusion pattern is very different from the most diffusions like non-sleepers which are led by early adoption of hubs. Effect of two determinants of sleeper diffusion were also verified through logistic regression model (Table 3). The degree of sender and the overlap ratio of the neighbor group have a significant effect on emergence of sleeper diffusion (χ2=10.36, df=2, p=.0056; 76.76%). In conclusion, this research focused on investigating slowly dispersed information. And we found two determinants of sleeper diffusion; the sender’s random seeding and the indirect effects of early adopters’ social pressure on their neighbors.
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