Topic Influence Analysis Based on User Intimacy and Social Circle Difference

Lu, Meilian; Wang, Zi; Ye, Danna

doi:10.1109/access.2019.2931146

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…A topic-based Social Measurement has been proposed by Hamzehei et al ( 2016 ) on the basis of network structure, user-generated content and users interactions. Lu et al ( 2019 ) measured topical users influence in OSN by combining users’ social relationships, posting and forwarding behaviors and posts content. A topic-aware influence maximization problem has been investigated by Chen et al ( 2015 ) focusing on efficiency of algorithms keeping a high influence spread.…”

Section: The Role Of Variety In Snmentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).

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Section: The Role Of Variety In Snmentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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“…While very few approaches have used manually handcrafted keywords to define multiple topics, [14,131], various other techniques, such as topic modeling [7,9,13,20,23,28,37,39,47,60,64,67,82,84,96,98,104,108,109,111,115,117,117,121,125,130,132], machine learning [85,108], and platform structures [50,126] are used to infer multiple topics in the literature. The way content is organized on a platform can be considered as a representation of a topic based on platform structures; e.g., a board on the Pinterest platform discusses a single topic.…”

Section: Topic Detectionmentioning

confidence: 99%

“…As discussed in the preliminaries (Section 2.2), LDA [15] is a traditional topic-modeling approach to identify the topics automatically from a larger collection of documents, where each topic is defined as a probability distribution over the vocabulary of words from a document collection. This flexibility led to many topic-based IUD approaches in the literature using LDA [39,67,84,96,111] or extension of LDA [23] to first infer the topics, then grouping the posts based on the topic dominantly discussed in the post and inferring influential users from topic related posts using different IUD techniques.…”

Section: Topic Modeling Based Topic Detectionmentioning

confidence: 99%

Topic-based influential user detection: a survey

Panchendrarajan

Saxena

2022

Appl Intell

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Online Social networks have become an easy means of communication for users to share their opinion on various topics, including breaking news, public events, and products. The content posted by a user can influence or affect other users, and the users who could influence or affect a high number of users are called influential users. Identifying such influential users has a wide range of applications in the field of marketing, including product advertisement, recommendation, and brand evaluation. However, the users’ influence varies in different topics, and hence a tremendous interest has been shown towards identifying topic-based influential users over the past few years. Topic-level information in the content posted by the users can be used in various stages of the topic-based influential user detection (IUD) problem, including data gathering, construction of influence network, quantifying the influence between two users, and analyzing the impact of the detected influential user. This has opened up a wide range of opportunities to utilize the existing techniques to model and analyze the topic-level influence in online social networks. In this paper, we perform a comprehensive study of existing techniques used to infer the topic-based influential users in online social networks. We present a detailed review of these approaches in a taxonomy while highlighting the challenges and limitations associated with each technique. Moreover, we perform a detailed study of different evaluation techniques used in the literature to overcome the challenges that arise in evaluating topic-based IUD approaches. Furthermore, closely related research topics and open research questions in topic-based IUD are discussed to provide a deep understanding of the literature and future directions.

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“…(1) User's activity A u . The more microblogs users post, the bigger influence they have on others [18]. Therefore, user's activity could be measured by the number of original microblogs and the retweet microblogs related to an event.…”

Section: A User Popularitymentioning

confidence: 99%

“…In social media, users influence their followers by their posted or retweeted microblogs, and attract them to participate the event discussion. Hence, the number of followers is regarded as one of the most intuitive measurements for the user popularity [18]. And, the number of posted microblogs by users also is a measurement to show the infectious ability of the user.…”

Section: A User Popularitymentioning

confidence: 99%

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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Topic Influence Analysis Based on User Intimacy and Social Circle Difference

Cited by 6 publications

References 32 publications

A survey of Big Data dimensions vs Social Networks analysis

A survey of Big Data dimensions vs Social Networks analysis

Topic-based influential user detection: a survey

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

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