A new evaluation algorithm for the influence of user in social network

Wei, Jiang; Gao, Mengdi; Wang, Xiaoxi; Wu, Xianda

doi:10.1109/cc.2016.7405737

Cited by 11 publications

(7 citation statements)

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“…Many scholars have put forward their own understanding of the user popularity. Wei et al divided the user popularity into the influence of followers, comments, and retweets [15]. Francalanci et al thought that the user popularity refers to the extent to which users share text, pictures and other content on other users [17].…”

Section: A User Popularitymentioning

confidence: 99%

“…The more words there in the microblog text are, the more likely microblogs to be retweeted, shown in Figure 1 On the Sina Weibo, the number of new microblogs begin to rise significantly from 8:00 to 9:00, and since then, fluctuates at a high level. It was shown that the greater of the number of retweets x iz and the number of comments x ip , the greater the attention of Weibo events [15]. It seemed that Weibo event attention is affected by the release time of the related microblogs.…”

Section: B the Event Attention Degreementioning

confidence: 99%

“…Such as the Twitter users were measured by the properties of their posts, retweets and the number of mentions [13], or by the retweet relationship and the retweet distance [14]. The time for the released information of event, the number of users' followers, the influence of followers, and the degree of attention of followers were used to measure the user popularity [15]. However, most of the existing researches on the quantification of user popularity are based on user attributes or text content attributes, and few researches can integrate these two dimensions of attributes.…”

Section: Introductionmentioning

confidence: 99%

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Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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Sina Weibo has significantly impact on the information diffusion processes in many realworld social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events' influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and nonredundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.

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Section: A User Popularitymentioning

confidence: 99%

Section: B the Event Attention Degreementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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“…The influence score for a given URL post is calculated by tracking the diffusion of the URL from its source node until the diffusion event is terminated. The work is similar to the one described in [77] where the influence measurement is related with the Influence diffusion model which provides the influence of topical spread. However, it differs from [22] in that the diffused influence is studied in terms of activity or passivity of Twitter users solely based on the user's retweeting behavior.…”

Section: Diffusion-oriented Approachesmentioning

confidence: 99%

“…A measure of alpha centrality is employed, which incorporates both directionality of network connections and a measure of external importance. As already mentioned in [21] and [77], influencers are discovered by applying PageRank and newly proposed link-analysis algorithms which are exploiting the topology and properties of the network, including posting, retweeting and mentioning relationships among users. In [78] influence is measured by applying a hybrid framework that integrates both users' structural location and attributes.…”

Section: Network/graph Propertiesmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content. In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

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