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Embedding and predicting the event at early stage

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Abstract

Social media has become one of the most credible sources for delivering messages, breaking news, as well as events. Predicting the future dynamics of an event at a very early stage is significantly valuable, e.g, helping company anticipate marketing trends before the event becomes mature. However, this prediction is non-trivial because a) social events always stay with “noise” under the same topic and b) the information obtained at its early stage is too sparse and limited to support an accurate prediction. In order to overcome these two problems, in this paper, we design an event early embedding model (EEEM) that can 1) extract social events from noise, 2) find the previous similar events, and 3) predict future dynamics of a new event with very limited information. Specifically, a denoising approach is derived from the knowledge of signal analysis to eliminate social noise and extract events. Moreover, we propose a novel predicting scheme based on locally linear embedding algorithm to construct the volume of a new event from its k nearest neighbors. Compared to previous work only fitting the historical volume dynamics to make a prediction, our predictive model is based on both the volume information and content information of events. Extensive experiments conducted on a large-scale dataset of Twitter data demonstrate the capacity of our model on extract events and the promising performance of prediction by considering both volume information as well as content information. Compared with predicting with only the content or the volume feature, we find the best performance of considering they both with our proposed fusion method.

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Notes

  1. https://twitter.com/

  2. https://www.google.com/trends/

  3. In practice, we find that V∗ and Vg may have different lengthes. To make them comparable, we simply expand the shorter one with value 0 to meet the length of the longer one.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Project 61572108, Project 61632007 and Project 61502081.

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Authors and Affiliations

  1. Guizhou Provincial Key Laboratory of Public Big Data, GuiZhou University, Guizhou, Guiyang, 550025, China

    Zhiwei Liu

  2. Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Zhiwei Liu, Yang Yang, Zi Huang, Fumin Shen, Dongxiang Zhang & Heng Tao Shen

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  1. Zhiwei Liu
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  2. Yang Yang
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Correspondence to Yang Yang.

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This article belongs to the Topical Collection: Special Issue on Geo-Social Computing

Guest Editors: Guandong Xu, Wen-Chih Peng, Hongzhi Yin, Zi (Helen) Huang

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Liu, Z., Yang, Y., Huang, Z. et al. Embedding and predicting the event at early stage. World Wide Web 22, 1055–1074 (2019). https://doi.org/10.1007/s11280-018-0545-6

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