| Résumé | Detecting events from social media requires to deal with the noisy sequences of user gen-erated text. Previous work typically focuses either on semantic patterns, using e.g.Topic models, or on temporal patterns of word usage, e.g. using wavelet analysis. In our study, we propose a novel method to capture the temporal patterns of word usage on social media, by transforming time series of word oc-currence frequency into images, and clustering images using features extracted from the images using the convolutional neural network ResNet. These clusters are then ranked by burstiness, identifying the top ranked clusters as detected events. Words in the clusters are also filtered using co-occurrence similarity, in order to identify the most representative words describing the event. We test our approach on one Instagram and one Twitter datasets, and obtain performance of up to 80% precision from the top five detected events on both datasets. |
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