Topic Oriented User Influence Analysis in Social Networks

Wang, Jing; Liu, Zhijing; Zhao, Hui

doi:10.1109/wi-iat.2015.202

Cited by 4 publications

(8 citation statements)

References 11 publications

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“…Single topic-based IUD approaches aim to infer the influential user of a known topic. The known topic is represented using a set of keywords [16,18,24,26,29,53,63,68,75,76,81,88,93,106,107,124,128,129], a query [2,7,99,115] or a set of users [65], which enable them to filter the posts that contains keywords or query words, or posted by representative users, respectively. Finally, influential users are inferred from posts and activities related to the topic of interest.…”

Section: Topic Detectionmentioning

confidence: 99%

“…In the literature, different techniques, such as statistical measures [18,26,29,53,63,81,93,99,106,107], random-walk approaches [2, 7, 7, 14, 16, 23, 24, 37, 39, 65, 67-69, 75, 76, 84, 88, 96, 108, 111, 115, 124, 126, 128], propagation algorithms [28,85,108,115], machine learning [25,50,[129][130][131], topic modeling [9,13,20,64,82,98,104,109,117,121], similarity measures [47,117,125], and optimization algorithms [113] are used for topic-based influential user detection. The Pagerank algorithm, i.e.…”

Section: Influential User Detectionmentioning

confidence: 99%

“…Statistical measures observed in an OSN, such as the number of followers for a user who often discusses a topic, number of posts published about a topic, number of reactions to a post that discusses a topic, are good indicators of influence made on a specific topic. These statistical measures can be directly quantified [18,26,29,53,63,81,93,106,107] to define a topic-based influence score for a user.…”

Section: Statistical Measures For Influence Detectionmentioning

confidence: 99%

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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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Section: Topic Detectionmentioning

confidence: 99%

Section: Influential User Detectionmentioning

confidence: 99%

Section: Statistical Measures For Influence Detectionmentioning

confidence: 99%

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Topic-based influential user detection: a survey

Panchendrarajan

Saxena

2022

Appl Intell

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“…Their experimental results showed that different influence roles may have stronger influence in their own role level. Wang et al [15] calculated user influence with four features, i.e., Expert, Leader, Social, and Similar, and then applied user influence to group recommendations. Wei et al [16] took users' opinion and topic relevance into consideration, and then predicted user influence according to the latent factors resulting from the tensor factorization.…”

Section: Introductionmentioning

confidence: 99%

“…However, most studies for user influence analysis on topic level only consider users' explicit features which can be obtained from users' profile directly [14,15]. In particular, these existing works neglect the temporal characteristic which can be obtained from the interactions [17].…”

Section: Introductionmentioning

confidence: 99%

Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

Liao

Zhang

Wei

et al. 2018

Wireless Communications and Mobile Computing

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User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user's temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user's opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

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