Lian Jian

Why share in peer-to-peer networks?

Prior theory and empirical work emphasize the enormous free-riding problem facing peer-to-peer (P2P) sharing networks. Nonetheless, many P2P networks thrive. We explore two possible explanations that do not rely on altruism or explicit mechanisms imposed on the network: direct and indirect private incentives for the provision of public goods. The direct incentive is a traffic redistribution effect that advantages the sharing peer. We find this incentive is likely insufficient to motivate equilibrium content sharing in large networks. We then approach P2P networks as a graph-theoretic problem and present sufficient conditions for sharing and free-riding to co-exist due to indirect incentives we call generalized reciprocity.

Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction

Social media can be a double-edged sword for political misinformation, either a conduit propagating false rumors through a large population or an effective tool to challenge misinformation. To understand this phenomenon, we tracked a comprehensive collection of political rumors on Twitter during the 2012 US presidential election campaign, analyzing a large set of rumor tweets (n = 330,538). We found that Twitter helped rumor spreaders circulate false information within homophilous follower networks, but seldom functioned as a self-correcting marketplace of ideas. Rumor spreaders formed strong partisan structures in which core groups of users selectively transmitted negative rumors about opposing candidates. Yet, rumor rejecters neither formed a sizable community nor exhibited a partisan structure. While in general rumors resisted debunking by professional fact-checking sites (e.g. Snopes), this was less true of rumors originating with satirical sources.

Performance analysis of a modified CDMA/PRMA MAC protocol

This paper analyzes the performance of a protocol proposed to improve the performance of Joint CDMA/PRMA protocol under heavy data traffic load condition. The proposed protocol features a demand-based assignment scheme for data transmission. Its performance is evaluated using two analysis methods: a Traditional Markov Analysis and a Transient Fluid Analysis. Simulation results are also given to validate the assumptions made in the mathematical models developed. Our work shows that demand-based assignment scheme is suitable for random data traffic transmission, especially when there are a large number of active data terminals with short random messages.

The diffusion of misinformation on social media

This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temporal pattern, content mutation, and sources of misinformation. We traced the lifecycle of 17 popular political rumors that circulated on Twitter over 13 months during the 2012 U.S. presidential election. Using text analysis based on time series, we found that while false rumors (misinformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension around the target dissolves. We observed that rumors resurface by partisan news websites that repackage the old rumor into news and, gain visibility by influential Twitter users who introduce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mechanism by which these messages evolve over time. False political rumors tend to resurface multiple times after the initial publication.False political rumors often turn into a more intense and extreme version over time.Resurged old political rumors tend to be presented as news.None-traditional partisan media are often behind the constant generation of false news.

Generalized Reciprocity in Peer-to-Peer Networks

Written with Lian Jian. Prior theory and empirical work emphasize the enormous free-riding problem facing peer-to-peer (P2P) sharing networks. Nonetheless, many P2P networks thrive. We explore two possible explanations: private provision of public goods and generalized reciprocity. We investigate a particular form of private incentives to share content: redistributing traffic in the network to the advantage of the sharing peer. Our preliminary model suggests that this incentive is likely insufficient to motivate equilibrium content sharing in large networks. We then approach P2P networks as a graph-theoretic problem and derive sufficient conditions for sharing and free-riding to co-exist in the absence of direct sharing benefits or an explicit incentive mechanism.