The Community for Technology Leaders
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) (2016)
Omaha, NE, USA
Oct. 13, 2016 to Oct. 16, 2016
ISBN: 978-1-5090-4470-2
pp: 161-168
ABSTRACT
Story identification from online user-generated content has recently raised increasing attention. Existing approaches fall into two categories. Approaches in the first category extract stories as cohesive substructures in a graph representing the strength of association between terms. The latter category includes approaches that analyze the temporal evolution of individual terms and identify stories by grouping terms with similar anomalous temporal behavior. Both categories have limitations. In this work we advance the literature on story identification by devising a novel method that profitably combines the peculiarities of the two main existing approaches, thus also addressing their weaknesses. Experiments on a dataset extracted from a real-world web-search log demonstrate the superiority of the proposed method over the state of the art.
INDEX TERMS
User-generated content, Correlation, Weight measurement, Web search, Data mining, Computational modeling, Time series analysis
CITATION

F. Bonchi, I. Bordino, F. Gullo and G. Stilo, "Identifying Buzzing Stories via Anomalous Temporal Subgraph Discovery," 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE, USA, 2016, pp. 161-168.
doi:10.1109/WI.2016.0032
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