A Noise-Filtering Approach for Spatio-temporal Event Detection in Social Media Conference Paper uri icon

abstract

  • Springer International Publishing Switzerland 2015. We propose an iterative spatial-temporal mining algorithm for identifying and extracting events from social media. One of the key aspects of the proposed algorithm is a signal processing-inspired approach for viewing spatial-temporal term occurrences as signals, analyzing the noise contained in the signals, and applying noise filters to improve the quality of event extraction from these signals. The iterative event mining algorithm alternately clusters terms and then generates new filters based on the results of clustering. Through experiments on ten Twitter data sets, we find improved event retrieval compared to two baselines.

name of conference

  • Advances in Information Retrieval - 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29 - April 2, 2015. Proceedings

published proceedings

  • Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

author list (cited authors)

  • Liang, Y., Caverlee, J., & Cao, C.

citation count

  • 10

complete list of authors

  • Liang, Yuan||Caverlee, James||Cao, Cheng

editor list (cited editors)

  • Hanbury, A., Kazai, G., Rauber, A., & Fuhr, N.

publication date

  • January 2015