Veracity of data : from truth discovery computation algorithms to models of misinformation dynamics 1. Introduction to data veracity 1.1 The fourth V of big data 1.2 Main causes affecting data veracity 1.3 Classification of truth discovery approaches 1.4 Information extraction 1.5 Fact checking and trust computation 1.6 Misinformation dynamics in networked systems On the Web a massive amount of user generated content is available through various channels (e.g. texts tweets Web tables databases multimedia sharing platforms etc.). Conflicting information rumors erroneous and fake content can be easily spread across multiple sources making it hard to distinguish between what is true and what is not. This book gives an overview of fundamental issues and recent contributions for ascertaining the veracity of data in the era of Big Data. The text is organized into six chapters focusing on structured data extracted from texts. Chapter 1 introduces the problem of ascertaining the veracity of data in a multi source and evolving context. Issues related to information extraction are presented in Chapter 2. Current truth discovery computation algorithms are presented in details in Chapter 3. It is followed by practical techniques for evaluating data source reputation and authoritativeness in Chapter 4. The theoretical foundations and various approaches for modeling diffusion phenomenon of misinformation spreading in networked systems are studied in Chapter 5. Finally truth discovery computation from extracted data in a dynamic context of misinformation propagation raises interesting challenges that are explored in Chapter 6. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of fact checking truth discovery or rumor spreading 6. Transdisciplinary challenges of truth discovery 6.1 Introduction 6.2 From information to data 6.2.1 Big data vs. sparse facts 6.2.2 Decontextualization 6.2.3 Uncertain incomplete and biased observations 6.2.4 Data and information fusion across languages modalities and media 6.3 From multisource data to networks of sources and networks of content 6.3.1 Truth discovery in common multiplexes 6.3.2 Time dependent truth discovery 6.3.3 Complex interconnected networks of sources and multimedia content 6.4 Final remark Bibliography Authors biographies 5. Misinformation dynamics 5.1 Introduction 5.2 Theoretical foundations 5.2.1 Terminology in complex networks 5.2.2 Complex network descriptors 5.2.3 Network models 5.3 Disease and rumor propagation models 5.3.1 Epidemic spreading 5.3.2 Rumor propagation 5.3.3 Misinformation dynamics 5.4 Theory under test: empirical feedback 5.4.1 Source identification 5.4.2 Dynamical role of source and intermediate nodes 5.5 Misinformation containment and meme mutation in complex social systems 5.5.1 Influence limitation: averting malicious viral processes 5.5.2 Information mutation: meme tracking 4. Trust computation 4.1 Introduction 4.2 Definitions 4.3 Probabilistic trust computation 4.3.1 Direct trust models 4.3.2 Combining direct and indirect trust models 4.3.3 Evaluation of trust computing schemes 4.4 Trust propagation 4.4.1 Overriding trust propagation 4.4.2 Aggregation based propagation 4.5 Conclusion 3. Truth discovery computation 3.1 Introduction 3.2 Terminology 3.2.1 Notations and basic principle 3.2.2 Characterization of truth discovery methods 3.2.3 Modeling assumptions 3.3 Truth discovery methods 3.3.1 Agreement based methods 3.3.2 MAP estimation based methods 3.3.3 Analytical methods 3.3.4 Bayesian inference based methods 3.4 New developments 3.4.1 Evolving truth discovery 3.4.2 Zero to many truth 3.4.3 Long tail phenomenon 3.4.4 Truth discovery from crowdsourced data 3.5 Conclusion 2. Information extraction 2.1 Introduction 2.2 Information extraction pipeline 2.2.1 Tokenization and sentence segmentation 2.2.2 Normalization 2.2.3 Part of speech tagging 2.2.4 Named entity recognition 2.2.5 Mention detection and coreference resolution 2.3 Knowledge graph population 2.3.1 Entity discovery and linking 2.3.2 Relation extraction and inference 2.3.3 Slot filling 2.3.4 Slot filler validation 2.4 Contradiction detection 2.5 Conclusion