Stories play an important role in human perception of the world and therefore the computational analysis of narrative structure is a key area in natural language processing. The focus of this thesis is to develop and evaluate computational models for two main elements of the narrative structure: Events and Desires. Our work first aims to test a theory that proposes a linear structure of narratives and identifies different parts of a story based on their function. Unlike most of the previous work that use the news articles or other simpler and more conventional genres, we use a corpus of personal stories from social media that have a wider range of topical content and variations of discourse relations. We present an unsupervised method for modeling narrative events, focusing on specific event relations based on the Penn Discourse Treebank's definition of contingency. We use a weakly supervised approach to extract the key events from stories and create a topic-sorted corpus of personal narratives using a bootstrapping method. We additionally propose new evaluation methods for testing the contingent event pairs. Our results show that most of the relations we learn from blog stories are not found in the existing event collections. In our final contribution, we develop supervised methods for modeling the protagonist's goals and their outcome in personal narratives, as a sub-problem of modeling affects. Our studies show that both prior and post context are useful for modeling desire fulfillment. In addition, we show that exploiting narrative structure is helpful, both directly in terms of the utility of discourse relation features and indirectly by using a sequential model. We further examine our analysis of the human desires by identifying and studying the expressions of unfulfilled goals.

With creative designs, this book contains important contributions to our understanding of social media news’s effects on political engagement, political knowledge, willingness to engage in self-censorship, and political disaffection. In recent years, social media has emerged as a major source of news and other information. The unique nature of social media and the variety of platforms available to individuals present challenges for those who want to study and understand its psychological impact. Fortunately, many innovative studies on this subject have appeared in publications in the last few years. This edited volume features a collection of recently published studies focusing on the effects of social media news as well as the framing of social issues on these platforms. The authors of these studies used surveys, experiments, and content analysis to explore their research questions. Each chapter provides valuable insights on the growing influence of social media news. The chapters in this book were originally published in the journal Mass Communication and Society.

This book constitutes the refereed proceedings of the 10th International Conference on Social, Cultural, and Behavioral Modeling & Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2017, held in Washington, DC, USA, in July 2017. The 16 full papers and 27 short papers presented were carefully reviewed and selected from 79 submissions. Owing to its strong multi-disciplinary heritage, the papers represent a large range of disciplines including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science and use numerous types of computational methods such as machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics. They are organized in the following topical sections: behavioral and social sciences; cyber and intelligence applications; information, systems, and network sciences; and methodology.

Publisher Description

Introduces the topic gently and intuitively with ample famous examples and case studies Develops and explains intuitively the information flow models, and thereafter builds the control theory for information management and propagation Includes mathematical treatment of information spread and fake news epidemics and step by step development of modeling framework Discusses Control methods and application examples Borrows from multiple disciplines and sub-disciplines and tries to create a new unified structure for digital information spread and control

This two-volume set constitutes the proceedings of the 22nd International Conference on Web Information Systems Engineering, WISE 2021, held in Melbourne, VIC, Australia, in October 2021. The 55 full, 29 short and 5 demo papers, plus 2 tutorials were carefully reviewed and selected from 229 submissions. The papers are organized in the following topical sections: Part I: BlockChain and Crowdsourcing; Database System and Workflow; Data Mining and Applications; Knowledge Graph and Entity Linking; Graph Neural Network; Graph Query; Social Network; Spatial and Temporal Data Analysis. Part II: Deep Learning (1), Deep Learning (2), Recommender Systems (1), Recommender Systems (2), Text Mining (1), Text Mining (2), Service Computing and Cloud Computing (1), Service Computing and Cloud Computing (2), Tutorial and Demo.

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

A landmark insider’s tour of how social media affects our decision-making and shapes our world in ways both useful and dangerous, with critical insights into the social media trends of the 2020 election and beyond “The book might be described as prophetic. . . . At least two of Aral’s three predictions have come to fruition.”—New York NAMED ONE OF THE BEST BOOKS OF THE YEAR BY WIRED • LONGLISTED FOR THE PORCHLIGHT BUSINESS BOOK AWARD Social media connected the world—and gave rise to fake news and increasing polarization. It is paramount, MIT professor Sinan Aral says, that we recognize the outsize effect social media has on us—on our politics, our economy, and even our personal health—in order to steer today’s social technology toward its great promise while avoiding the ways it can pull us apart. Drawing on decades of his own research and business experience, Aral goes under the hood of the most powerful social networks to tackle the critical question of just how much social media actually shapes our choices, for better or worse. He shows how the tech behind social media offers the same set of behavior influencing levers to everyone who hopes to change the way we think and act—from Russian hackers to brand marketers—which is why its consequences affect everything from elections to business, dating to health. Along the way, he covers a wide array of topics, including how network effects fuel Twitter’s and Facebook’s massive growth, the neuroscience of how social media affects our brains, the real consequences of fake news, the power of social ratings, and the impact of social media on our kids. In mapping out strategies for being more thoughtful consumers of social media, The Hype Machine offers the definitive guide to understanding and harnessing for good the technology that has redefined our world overnight.

Many fundamental technological and managerial issues surrounding the development and implementation of intelligent analytics within multi-industry applications remain unsolved. There are still questions surrounding the foundation of intelligent analytics, the elements, the big characteristics, and the effects on business, management, technology, and society. Research is devoted to answering these questions and understanding how intelligent analytics can improve healthcare, mobile commerce, web services, cloud services, blockchain, 5G development, digital transformation, and more. Intelligent Analytics With Advanced Multi-Industry Applications is a critical reference source that explores cutting-edge theories, technologies, and methodologies of intelligent analytics with multi-industry applications and emphasizes the integration of artificial intelligence, business intelligence, big data, and analytics from a perspective of computing, service, and management. This book also provides real-world applications of the proposed concept of intelligent analytics to e-SMACS (electronic, social, mobile, analytics, cloud, and service) commerce and services, healthcare, the internet of things, the sharing economy, cloud computing, blockchain, and Industry 4.0. This book is ideal for scientists, engineers, educators, university students, service and management professionals, policymakers, decision makers, practitioners, stakeholders, researchers, and others who have an interest in how intelligent analytics are being implemented and utilized in diverse industries.