How to become an Influencer in Social Networks

Adesokan, Adedamola; Siraj, Md Sadman; Rahman, Aisha B; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon

doi:10.1109/icc45041.2023.10279644

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A novel crowdsourcing network architecture addressing location privacy concerns during task allocation is proposed in [23] by implementing a privacy-preserving, decentralized dispute arbitration protocol to handle payment disputes without revealing users' private information, showcasing resistance to forgery attacks and efficient perfor-mance. A game-theoretic model is introduced in [24] to analyze the dissemination of user personal information in online social networks, considering factors like intimacy and subject popularity [25]. The authors in [26] focused on user privacy and data trustworthiness and proposed game-theoretic solutions, specifically revisiting coalitional game formation and subgame perfect equilibrium-based concepts.…”

Section: Privacy and Trustworthinessmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

Self Cite

View full text Add to dashboard Cite

This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

show abstract

Section: Privacy and Trustworthinessmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

Self Cite

View full text Add to dashboard Cite

show abstract

“…As OSNs, like Instagram, Facebook, Twitter, and TikTok, gain increasing popularity, viral marketing emerges as a supremely efficient strategy for promoting products and services to customers by leveraging the power of word-of-mouth reputation [5]. A multiobjective optimization-based framework for maximizing the influence spread and minimizing the promotion costs in geo-social networks is presented in [6].…”

Section: Viral Marketingmentioning

confidence: 99%

INFLUTRUST: Trust-Based Influencer Marketing Campaigns in Online Social Networks

Adesokan,

Rahman,

Tsiropoulou

2024

Future Internet

View full text Add to dashboard Cite

This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms.

show abstract