Predictive Analysis on Twitter: Techniques and Applications

By Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit P. Sheth, Ismailcem Budak Arpinar. 2018

19 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ugur Kursuncu
2. Manas Gaur
3. Amit P. Sheth
4. Krishnaprasad Thirunarayan
5. Ismailcem Budak Arpinar
6. Carlos Castillo
7. Valerie L. Shalin
8. Amanuel Alambo

In Information Systems

1. Ugur Kursuncu
2. Manas Gaur
3. Amit P. Sheth
4. Carlos Castillo

In Statistical and Nonlinear Physics

1. Ugur Kursuncu
2. Manas Gaur
3. Krishnaprasad Thirunarayan
4. Ismailcem Budak Arpinar
5. Amit P. Sheth
6. Carlos Castillo
7. Fernando Diaz
8. Muhammad Imran

Citations in the corpus

• Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “…demonstrate the limitation of attentional manipulation in a set of problems such as sentiment (mis)classification (Maurya 2018) and suicide risk (Corbitt-Hall et al. 2016), where feature presence is inherently ambiguous, just as in the online radicalization problem (Kursuncu et al. 2019a).”
• Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions (2019, background): “ specific domain. Word (or phrase, sentence, document) embedding models generate numerical vector representations of words (or phrases, sentences, documents) that can be used to represent the content [37]. We can use vector operations such as addition, multiplication, and concatenation to aggregate ”