Veracity of Big Data

Chapter 1: Introduction Chapter Goal: Introduce the readers to the manifestations of falsehood in Big Data and its ramifications.No of pages 30Sub -Topics1. The Big Data Phenomenon2. The Four V's3. Veracity - the fourth `V'4. Tracing truth in human endeavors5. Veracity in the context of the WebChapter 2: Mathematical AbstractionChapter Goal: Present the math behind the method and develop a mathematical framework within which the problem and its solution can be discussed.No of pages: 30Sub - Topics 1. A fruit vendor example2. Building the abstraction3. Twitter Example - Sentiment Analysis4. Solution SpaceChapter 3: Tools and TechniquesChapter Goal: Introduce the Machine Learning and mathematical tools to solve the problem. No of pages : 30Sub - Topics: 1. Machine Learning Algorithms - a quick primer2. Kalman Filter3. Statistical Techniques Chapter 4: Veracity of Web InformationChapter Goal: Use the concepts, tools, and techniques described in chapter 3 to examine the truthfulness of microblogsNo of pages: 50Sub - Topics: 1. Machine Learning the truthfulness of twitter data2. Statistical approaches to detect veiled attacks3. Applying Kalman Filter to analyze sentiment fluctuations Chapter 5: Future DirectionsChapter Goal: Explore ideas that the readers can consider for further delving into the topic, given that this is a niche area.1. Natural Language Processing methods2. Knowledge Representation Techniques3. Ensemble Methods