Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of the 22nd ACM international conference on Conference on information knowledge management |
| Number of pages | 4 |
| Publication date | 2013 |
| Pages | 1229-1232 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | ACM International Conference on Information & Knowledge Management - San Francisco, United States Duration: 27 Oct 2013 → 1 Nov 2013 Conference number: 22 |
Conference
| Conference | ACM International Conference on Information & Knowledge Management |
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| Number | 22 |
| Country/Territory | United States |
| City | San Francisco |
| Period | 27/10/2013 → 01/11/2013 |