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The Online Effect: Decoding X to predict election outcomes/ Sanjeev Singh

By: Material type: TextTextPublication details: New Delhi: Bloomsbury, 2024.Description: xv, 229p. softbound 13*21 cmISBN:
  • 978-93-5640-625-4
DDC classification:
  • 23 004.678 SIN
Contents:
Here is the real-life story of how an Indian journalist used latest data tools to accomplish something no one had ever achieved before: the ability to predict the winning party of any election even before the votes are counted. The tale of how Sanjeev Singh and data scientist Rishabh Srivastava converged their expertise to build the Singh-Srivastava Model is told in everyday, easy-to-understand language. Singh takes us step by step through the process of putting the model together, from the first hint that the idea would actually work to the final elation of victory and beyond. The Online Effect will educate and entertain not just psephologists and others who can use the model for their own work but also general voters and users of X (formerly Twitter) whose support of political parties and tweets respectively provide the foundation of the model.
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Item type Current library Collection Call number Status Date due Barcode
Books Books Tetso College Library Science and Technology Non-fiction 004.678 SIN (Browse shelf(Opens below)) Available 13768

Here is the real-life story of how an Indian journalist used latest data tools to accomplish something no one had ever achieved before: the ability to predict the winning party of any election even before the votes are counted. The tale of how Sanjeev Singh and data scientist Rishabh Srivastava converged their expertise to build the Singh-Srivastava Model is told in everyday, easy-to-understand language. Singh takes us step by step through the process of putting the model together, from the first hint that the idea would actually work to the final elation of victory and beyond.

The Online Effect will educate and entertain not just psephologists and others who can use the model for their own work but also general voters and users of X (formerly Twitter) whose support of political parties and tweets respectively provide the foundation of the model.

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