Efficiency of Adversarial Timeline Competition in Online Social Networks

Targeted online advertising elicits a potential threat.A commercial agent has a chance to mitigate the visibility of hisopponents because their sales or services are of similar types. Inthis paper, we consider the competition for attention in popularonline social networks (OSNs) that usually employ a timeline-based homepage to sort messages chronologically in a limitedvisible region. A non-cooperative Tullock-like game model isformulated that consists of a finite amount of benign agents andone malicious agent. By paying to the OSN, each benign agentseeks to maximize his utility of visibility, while the maliciousone aims to reduce the utilities of benign agents. Our primarypurposes are to quantify how robust the overall performanceof benign agents is against the malicious action, and how theOSN’s revenue is influenced. We derive the upper and the lowerbounds of six fundamental measures with regard to the totalutility and the total net utility of benign agents and the OSN’srevenue under three different scenarios: with and without themalicious agent, and the maximum. They capture the worst andthe best performances of the benign agents as well as the OSN.Our study reveals two important insights: i) the performancebounds are very sensitive to the malicious agent’s willingness topay at certain ranges; ii) the OSN acquires more revenues fromthis malicious action.