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Predicting Democratic Protests Paradigm from Twitter : Using Deep learning BiLSTM Model
  • Rhea Mahajan
Rhea Mahajan
UNiversity of Jammu, UNiversity of Jammu

Corresponding Author:[email protected]

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Twitter allows its users to share their concerns, ideas and daily activities online. This shared content by individuals provides a rich source of natural occurring data. The rapidity and flexibility of tweets have motivated its use as a tool for mobilizing masses for organizing strikes, protests, and other demonstrations. In this paper, we hypothesized that unrestricted data available on Twitter has potential power to predict day and location of major protests when deep learning and data mining is applied to its unstructured data. The prediction model used in the study is Bidirectional Long Short Term Memory (BiLSTM) Model. The results of our experiment shows that our model performs better than existing baselines models achieving an accuracy of 82.02% on testing data and 95.28% on training data. Overall, the aim of the paper is to study the efficacy of data mining techniques on publicly available data on Twitter in predicting mass protests and demonstrations.