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Predictive Analysis on Twitter: Techniques and Applications

  • Ugur Kursuncu
  • Manas Gaur
  • Usha Lokala
  • Krishnaprasad Thirunarayan
  • Amit Sheth
  • I. Budak Arpinar
Chapter
First Online:
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches, and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.

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Notes

Acknowledgements

We are grateful to Amelie Gyrard, Mustafa Nural, Sanjaya Wijeratne, Shreyansh Bhatt, and Ankita Saxena for their assistance with their reviews and comments that greatly improved this book chapter.

We acknowledge partial support from the National Science Foundation (NSF) award CNS-1513721: “Context-Aware Harassment Detection on Social Media,” National Institutes of Health (NIH) award: MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression,” NSF award EAR- 1520870 ‘Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response’, Community in Social Media: This work was supported by Army Research Office Grant No. W911NF-16-1-0300, National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02 Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use. Any opinions, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, NIH, NIDA, or Army Research Office.

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ugur Kursuncu
    • 1
    • 2
  • Manas Gaur
    • 1
  • Usha Lokala
    • 1
  • Krishnaprasad Thirunarayan
    • 1
  • Amit Sheth
    • 1
  • I. Budak Arpinar
    • 2
  1. 1.Kno.e.sis CenterWright State UniversityDaytonUSA
  2. 2.Department of Computer ScienceThe University of GeorgiaAthensUSA

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