Predictive Analysis on Twitter: Techniques and Applications

TitlePredictive Analysis on Twitter: Techniques and Applications
Publication TypeBook Chapter
Year of Publication2018
AuthorsUgur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, Ismailcem Budak Arpinar
Book TitleEmerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining
Chapter3
Pagination39-79
Date Published08/2018
PublisherSpringer
CityDayton
KeywordsCitizen sensing, Community evolution, Demographic prediction, Drug trends, Election prediction, event analysis, Harassment detection, machine learning, Mental Health, Semantic Social Computing, Sentiment-Emotion-Intent Analysis, social media analysis, Spatio-temporalthematic analysis, Stock Market prediction
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.

DOI10.1007/978-3-319-94105-9_4
Full Text

Citation:
Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth and I. Budak Arpinar. "Predictive Analysis on Twitter: Techniques and Applications". Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2018.

Projects: 
Context-Aware Harassment Detection on Social Media
eDrugTrends
Modeling Social Behavior for Healthcare Utilization in Depression