Crisis Assessment Oriented Influence Maximization in Social Networks

Niu, Weinan; Tan, Wenan; Wei, Jia; Zhao, Lu; Xie, Na

doi:10.1109/tcss.2022.3166182

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…By permitting disproportionate influence operations without oversight, such asymmetric information flows weaken informed discourse and erode autonomy, both of which are essential components of democracy. In addition to outright bans on discrimination, some suggested solutions include creating decentralized recommendation ecosystems that are less susceptible to large-scale centralized control and manipulation, and enforcing strict transparency rules around targeting methodologies [11].…”

Section: Vulnerabilities To Malicious Attacksmentioning

confidence: 99%

Preserving User Privacy in an Era of Third-Party Tracking

Mehrotra

2024

IJRASET

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Third-party cookies are essential for personalizing user experiences as online tracking and targeted advertising continue to advance quickly. But large-scale data collection by outside services also presents serious privacy risks that need to be looked into [1]. The present study examines various aspects of privacy degradation resulting from the widespread use of thirdparty cookies, which retain user data without explicit consent [2] We specifically draw attention to new issues with cross-site tracking, security threats from combined user profiles [3], manipulating user perception through persuasion, and conflicts of interest resulting from ambiguous data sharing contracts between platforms and advertisers.

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Section: Vulnerabilities To Malicious Attacksmentioning

confidence: 99%

Preserving User Privacy in an Era of Third-Party Tracking

Mehrotra

2024

IJRASET

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“…A novel matrix-factorizationbased trust prediction model is presented in [33] to address issues related to the sparse social interaction and low trust utilization in OSNs via integrating the social trust graph and recommendation system data to improve the predictive performance of OSN users. A crisis-assessment-oriented and topic-based influence maximization framework is proposed in [34] to address the risk of negative and untrusted information in OSNs [35]. A qualitative model of swift trust formation in global virtual teams is presented in [36] offering insights into how these teams develop trust behaviors among each other for effective virtual collaboration and international management.…”

Section: Online Advertisementmentioning

confidence: 99%

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

Adesokan,

Rahman,

Tsiropoulou

2024

Future Internet

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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.

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