Identifying and tracking topic-level influencers in the microblog streams

Su, Sen; Wang, Yakun; Zhang, Zhongbao; Chang, Cheng; Zia, Muhammad Azam

doi:10.1007/s10994-017-5665-1

Cited by 14 publications

(15 citation statements)

References 28 publications

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“…The 50% cut-off reflected a previous online credibility study [20]. Follower count alone does not denote influencer status [33], and 'micro-influencers' with between 1000 and 100,000 followers are considered important as they tend to focus on a niche area [34,35]. To capture these topic-specific SMI, follower counts across Twitter, Facebook and Instagram were used, with the lower cut-off being 80,000 on one SM.…”

Section: Does the Influencer Always Make Clear Distinction Between Famentioning

confidence: 99%

Analysing Credibility of UK Social Media Influencers’ Weight-Management Blogs: A Pilot Study

Sabbagh

Boyland

Hankey

et al. 2020

IJERPH

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Social media influencers (SMI) are individuals with large follower engagement, who can shape the thoughts and dietary behaviours of their audience. Concerns exist surrounding the spread of dietary misinformation by SMI, which may impact negatively on public health, yet no standards currently exist to assess the credibility of their information. This study aimed to evaluate the credibility of key SMI weight management (WM) blogs (n = 9), piloting a pre-prepared credibility checklist. SMI were included if they had a blue-tick verification on ≥2 social media (SM) and an active WM blog. A sample of blog posts were systematically evaluated against thirteen credibility indicators under four themes: ‘transparency’, ‘use of other resources’, ‘trustworthiness and adherence to nutritional criteria’ and ‘bias’. Indicators were yes/no questions to determine an overall credibility percentage for each SMI. The ten most recent meal recipes from each blog were evaluated against Public Health England’s (PHE) calorie targets and the UK ‘traffic light’ food labelling scheme to assess nutritional quality. Percentages ranged from 23–85%, the highest gained by a Registered Nutritionist. SMI blogs may not be credible as WM resources. Given the popularity and impact of SM in the context of overweight, obesity and WM, this study may inform the methodological approach for future research.

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Section: Does the Influencer Always Make Clear Distinction Between Famentioning

confidence: 99%

Analysing Credibility of UK Social Media Influencers’ Weight-Management Blogs: A Pilot Study

Sabbagh

Boyland

Hankey

et al. 2020

IJERPH

View full text Add to dashboard Cite

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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%

“…Therefore, it is essential to analyze the user influence at the topic level and determine the topic-based influential users. This can be considered as a granular study of influential user detection problem, and a vast amount of interest has been shown towards inferring topic-based influential users in the literature [13,26,37,38,64,69,78,98,111,125].…”

Section: Introductionmentioning

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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“…Each of the aforementioned studies has not considered the importance of hot event topic information content and user preferences. Literature in the context of event detection demonstrates that the influence of users in the social network is related to the relationship between users and topic while the influence of the same user is dissimilar under different topics [30]. Furthermore, these studies in [11], [23], [31], [32] focus on the effect of topic influence on user activation probability and ignored utilizing the popularity of topics, links between posts, and diffusion power of the users.…”

Section: Related Workmentioning

confidence: 99%

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

Shi

Liu

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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Microblogging, networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carries opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a Human-centric Social Computing (HCSC) model for hot event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are pre-processed through Hypertext Induced Topic Search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a Latent Dirichlet Allocation (LDA) based multi-prototype user topic detection method is used for identifying users with high influence in the network. Further, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot event detection and information propagation.

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Identifying and tracking topic-level influencers in the microblog streams

Cited by 14 publications

References 28 publications

Analysing Credibility of UK Social Media Influencers’ Weight-Management Blogs: A Pilot Study

Analysing Credibility of UK Social Media Influencers’ Weight-Management Blogs: A Pilot Study

Topic-based influential user detection: a survey

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

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