Influence maximization diffusion models based on engagement and activeness on instagram
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TL;DR: Wang et al. as discussed by the authors proposed three new realistic diffusion models, based on the Independent Cascade and Linear Threshold models, namely IC-u, LT-u and UAD models.
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About: This article is published in Journal of King Saud University - Computer and Information Sciences. The article was published on 01 Jun 2022. and is currently open access. The article focuses on the topics: Influencer marketing & Maximization.
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Citations
InfOnto : An ontology for fashion influencer marketing based on Instagram
17 Nov 2022
TL;DR: In this article , an ontology of fashion influencer marketing domain based on fashion marketing resources in Iran that were available during the years 2014-2021 was created with 1 conceptual core, 3 main concepts, 81 concepts, 8 categories, 2373 axioms, 1196 logical axiom, 61 object properties, 72 data properties, and 9 annotative properties.
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References
•Proceedings Article
Measuring User Influence in Twitter: The Million Follower Fallacy
Meeyoung Cha,Hamed Haddadi,Fabrício Benevenuto,Krishna P. Gummadi +3 more
- 16 May 2010
TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Influence Maximization in Near-Linear Time: A Martingale Approach
Youze Tang,Yanchen Shi,Xiaokui Xiao +2 more
- 27 May 2015
TL;DR: The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
877
Influence maximization: near-optimal time complexity meets practical efficiency
Youze Tang,Xiaokui Xiao,Yanchen Shi +2 more
- 18 Jun 2014
TL;DR: TIM as discussed by the authors is an algorithm for influence maximization that runs in O((k+ l) (n+m) log n/e2) expected time and returns a (1-1/e-e)-approximate solution with at least 1 - n-l probability.
654
Social influence and the collective dynamics of opinion formation.
TL;DR: An influence map is drawn that describes the strength of peer influence during interactions and identifies two major attractors of opinion: the expert effect, induced by the presence of a highly confident individual in the group, and the majority effect, caused by a critical mass of laypeople sharing similar opinions.
Foundations of Social Media Marketing
TL;DR: In this paper, the authors outline the nature, effects and present status of the Social Media, underlying their role as customer empowerment agents and propose two possible Social Media marketing strategies: a passive approach focusing on utilizing the social media domain as source of customer voice and market intelligence, and an active approach i.e. engaging the social Media as direct marketing and PR channels, as channels of customer influence, as tools of personalizing products and last but not least develop them as platforms of co-operation and customer-generated innovation.