Hot Topic Propagation Model and Opinion Leader Identifying Model in Microblog Network

Abstract

As the network technique is fast developing, the microblog has been a significant carrier representing the social public opinions. Therefore, it is important to investigate the propagation characteristics of the topics and to unearth the opinion leaders in Micro-blog network. The propagation status of the hot topics in the Micro-blog is influenced by the authority of the participating individuals. We build a time-varying model with the variational external field strength to simulate the topic propagation process. This model also fits for the multimodal events. The opinion leaders are important individuals who remarkably influence the topic discussions in its propagation process. They can help to guide the healthy development of public opinion. We build an AHP model based on the influence, the support, and the activity of a node, as well as a microblog-rank algorithm based on the weighted undirected network, to unearth and analyze the opinion leaders’ characteristics. The experiments in the data, collected from the Sina Micro-blog from October 2012 to November 2012 and from January 2013 to February 2013, show that our models predict the trend of hot topic efficiently and the opinion leaders we found are reasonable.

Opinion leaders’ prediction for monitoring the product reputation

“…These blogs were selected (filtered) according to the similarity rate (ontology-based) to a specific topic. In the same context, authors in Lin et al (2013) have built an AHP model (analytical hierarchy process) based on three main criteria of each node (influence, support, activity) in addition to performing a microblog-rank algorithm based on the weighted undirected network.…”

“…For example, an ordinary user can become an opinion leader of a given hot topic during a limited timeline (elections, a natural disaster, etc.) (Lin et al, 2013). Consequently, the opinion leader score has to be computed periodically by following the steps below:…”

“…The majority of the previous works have only focus on the profiles information and timeline to evaluate whether they are close enough to the opinion leaders' prototype (the more connected, the highly central and the most able to make others react to their messages). In Lin et al (2013), a study involving Sina Weibo micro-blog has been made where opinion leaders have identified using the profiles information such as forward amount, comments amount, friends and fans amount, etc. Another research has used 11 parameters of MySpace.…”

Computational modeling of Weibo user influence based on information interactive network

“…Xianhui et al (2015) developed the TopicLeaderRank algorithm, which computed the user influence based on a constructed user behavior network containing Weibo users’ content attributes and social attributes. In addition, there are also the forum reply emotional polarity based LeaderRank algorithm (Yu et al , 2010), the microblog novelty and inter-microblog linking relation-based InfluenceRank algorithm (Song et al , 2007), the user comments relation-based Microblog-Rank algorithm (Lin et al , 2013), and others. Pal and Counts (2011) computed individual spreading influence, forwarding influence and mentioning influence based on individual number of fans, post, reply, forwarding and mentioning from Twitter’s database, respectively.…”