Discovering Information Propagation Patterns in Microblogging Services

Abstract

During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.

Roles of information propagation of Chinese microblogging users in epidemics: a crisis management perspective

“…Additionally, the average depth for different user categories and patterns ranges from 2 to 3, which shows that most of the microblogs will not be reposted after reaching two or three users along the information propagation path. This value is lower than the average depth found in other work (Yu et al , 2015). As new information appears quickly and new microblogs are posted frequently in a crisis situation, people focus on fresh information and stop reposting these microblogs after a short timeframe.…”

“…Such reposting actions propagate information further in the social network, and information propagation patterns can be derived based on these hops at which a single microblog has traveled. Modeling the information propagation in microblogging platforms may lead to more effective use of such platforms and provide insights into the underlying sociology (Yu et al , 2015). However, there are subtle differences in the implementations of reposting on Weibo and Twitter.…”

Fine-grained Emotion Role Detection Based on Retweet Information

“…Among them the retweet/repost behavior can be used to track the propagation of information. This kind of information propagation naturally forms a tree structure within which users correspond to nodes and retweeting/reposting behaviors correspond to edges [42]. As illustrated in Figure 1, we extract the repost structure in Weibo using the identifier "//@".…”

Time-Sync Video Tag Extraction Using Semantic Association Graph

“…(3) Herding effects. Herding effects are common in TSCs [17,61]. That means, latter TSCs may depend on the former ones and have a semantic association with the preceding ones.…”