On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream

Authors:
Saptarshi Ghosh;Muhammad Bilal Zafar;Parantapa Bhattacharya;Naveen Sharma;Niloy Ganguly;Krishna Gummadi
Affiliations:
IIT Kharagpur & MPI-SWS, Kharagpur, India;MPI-SWS, Kaiserslautern, Saarbruecken, Germany;IIT Kharagpur & MPI-SWS, Kharagpur, India;University of Washington, Washington, WA, USA;IIT Kharagpur, Kharagpur, India;MPI-SWS, Kaiserslautern, Saarbruecken, Germany
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

Citing 5
Cited 0

TwitterStand: news in tweets

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
@spam: the underground on 140 characters or less

Proceedings of the 17th ACM conference on Computer and communications security
Smoothing techniques for adaptive online language models: topic tracking in tweet streams

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring who-is-who in the Twitter social network

Proceedings of the 2012 ACM workshop on Workshop on online social networks
Cognos: crowdsourcing search for topic experts in microblogs

SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several applications today rely upon content streams crowd-sourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the crucial question of how to sample the data generated by users in social networks. The traditional method is to randomly sample all the data. We analyze a different sampling methodology, where content is gathered only from a relatively small subset (