Retrieval of Trending Keywords in a Peer-to-Peer Micro-blogging OSN

We investigate the problem of identifying trending information in a peer-to-peer micro-blogging online social network. In a distributed decentralized environment, the participating nodes do not have access to global statistics such as the frequencies of the keywords and the information creation rate. We propose a two step solution. First, nodes make a local estimate of the frequency of keywords in the network based on their local information. At each iteration a subset of nodes collect this information from a small subset of random nodes in the network and aggregate the results. The most frequently occurring keywords are identified. In the second step, a node requests another small random subset of nodes to identify when, in the recent past, the more frequently occurring keywords were seen in micro-blogs. Once again this information is aggregated the fraction of time within a consecutive period that keywords were encountered is calculated. If this fraction, referred to as the trending fraction, is close to 1, then the keyword is predicted to be trending. A simulation on a network of 10,000 nodes shows that the solution is capable of detecting multiple trending keywords with a moderate increase in bandwidth.