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Modeling and Prediction of People's Needs (Vision Paper)

Published: 07 November 2017 Publication History

Abstract

Unrest in the society emerges when people's reasonable needs are ignored. Nowadays, the proliferation of data acquisition and data sharing technologies enables users all over the world to publicly express their needs. A new type of Interactive "Petition" Platforms (IPP) for compiling and organizing people's societal needs is experiencing fast growing but without much attention in the research community. In this paper we propose to explore and exploit the information from IPPs for social good in three stages: (i) Descriptive learning: characterize the petition process, using IPP data for automated storytelling, compiling comments into a concise article capturing the evolution of a story in space and time. (ii) Predictive learning: forecast the petition results, to predict which petitions will be successful, (iii) Prescriptive learning: auto-generate new petitions thus forecasting where and when the users will have a need under which topic. Breaking into this entirely new field of describing, predicting and prescribing human needs promises a plethora of application for the benefit of all.

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Cited By

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  • (2018)Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00069(537-546)Online publication date: Nov-2018

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cover image ACM Conferences
LENS'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News
November 2017
50 pages
ISBN:9781450355001
DOI:10.1145/3148044
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Publication History

Published: 07 November 2017

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Author Tags

  1. Automated News Generation
  2. Human Need Prediction
  3. Petition Data
  4. Petition Mining
  5. Spatio-Temporal Data Mining

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  • (2018)Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00069(537-546)Online publication date: Nov-2018

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