Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEmpirical Analysis of the Online Rating Systems
186
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
This paper is to analyze the properties of evolving bipartite networks from four aspects, the growth of networks, the degree distribution, the popularity of objects and the diversity of user behaviours, leading a deep understanding on the empirical data. By empirical studies of data from the online bookstore Amazon and a question and answer site Stack Overflow, which are both rating bipartite networks, we could reveal the rules for the evolution of bipartite networks. These rules have significant meanings in practice for maintaining the operation of real systems and preparing for their future development. We find that the degree distribution of users follows a power law with an exponential cutoff. Also, according to the evolution of popularity for objects, we find that the large-degree objects tend to receive more new ratings than expected depending on their current degrees while the small-degree objects receive less ratings in terms of their degrees. Moreover, the user behaviours show such a trend that the larger degree the users have, the stronger purposes are with their behaviours except the initial periods when users choose a diversity of products to learn about what they want. Finally, we conclude with a discussion on how the bipartite network evolves, which provides guideline for meeting challenges brought by the growth of network.
rate research
Read More
We evaluate the rating system of Heroes of Newerth (HoN), a multiplayer online action role-playing game, by using statistical analysis and comparison of a players in-game performance metrics and the player rating assigned by the rating system. The datasets for the analysis have been extracted from the web sites that record the players ratings and a number of empirical metrics. Results suggest that the HoNs Matchmaking rating algorithm, while generally capturing the skill level of the player well, also has weaknesses, which have been exploited by players to achieve a higher placement on the ranking ladder than deserved by actual skill. In addition, we also illustrate the effects of the choice of the business model (from pay-to-play to free-to-play) on player population.
The mechanism of the online user preference evolution is of great significance for understanding the online user behaviors and improving the quality of online services. Since users are allowed to rate on objects in many online systems, ratings can well reflect the users preference. With two benchmark datasets from online systems, we uncover the memory effect in users selecting behavior which is the sequence of qualities of selected objects and the rating behavior which is the sequence of ratings delivered by each user. Furthermore, the memory duration is presented to describe the length of a memory, which exhibits the power-law distribution, i.e., the probability of the occurring of long-duration memory is much higher than that of the random case which follows the exponential distribution. We present a preference model in which a Markovian process is utilized to describe the users selecting behavior, and the rating behavior depends on the selecting behavior. With only one parameter for each of the users selecting and rating behavior, the preference model could regenerate any duration distribution ranging from the power-law form (strong memory) to the exponential form (weak memory).
How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the death of distance, physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resembling to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected.
Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems and could inform design of efficient propagation strategies based on users behaviors.
Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary credibility, but usually used the number of edits as the unit of time, without consideration of real time. In this work, we probe the formation of such collective intelligence through a systematic analysis using the entire history of 34,534,110 English Wikipedia articles, between 2001 and 2014. From this massive data set, we observe the universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, we find the existence of distinct growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, we present a mechanistic model that adopts the article editing dynamics based on both editor-editor and editor-article interactions. The model successfully generates the key properties of real Wikipedia articles such as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. We suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
