NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS by Richard H. Thaler and Cass R. Sunstein Penguin Books, 2009, 312 pp, ISBN 978-0-14-311526-7This book is best described formally as a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications. Informally, it is about how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society. It is paternalism in the sense that "it is legitimate for choice architects to try to influence people's behavior in order to make their lives longer, healthier, and better", (p. 5) It is libertarian in that "people should be free to do what they like - and to opt out of undesirable arrangements if they want to do so", (p. 5) The built-in possibility of opting out or making a different choice preserves freedom of choice even though people's behavior has been influenced by the nature of the presentation of the information or by the structure of the decisionmaking system. I had never heard of libertarian paternalism before reading this book, and I now find it fascinating.Written for a general audience, this book contains mostly social and behavioral science theory and models, but there is considerable discussion of structure and process that has roots in mathematical and quantitative modeling. One of the main applications of this social system is economic choice in investing, selecting and purchasing products and services, systems of taxes, banking (mortgages, borrowing, savings), and retirement systems. Other quantitative social choice systems discussed include environmental effects, health care plans, gambling, and organ donations. Softer issues that are also subject to a nudge-based approach are marriage, education, eating, drinking, smoking, influence, spread of information, and politics. There is something in this book for everyone.The basis for this libertarian paternalism concept is in the social theory called "science of choice", the study of the design and implementation of influence systems on various kinds of people. The terms Econs and Humans, are used to refer to people with either considerable or little rational decision-making talent, respectively. The various libertarian paternalism concepts and systems presented are tested and compared in light of these two types of people. Two foundational issues that this book has in common with another book, Network of Echoes: Imitation, Innovation and Invisible Leaders, that was also reviewed for this issue of the Journal are that 1 ) there are two modes of thinking (or components of the brain) - an automatic (intuitive) process and a reflective (rational) process and 2) the need for conformity and the desire for imitation are powerful forces in human behavior. …

Nudge: Improving Decisions about Health, Wealth, and Happiness

The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size.

https://www.pnas.org/content/pnas/113/3/554.full.pdf

The spreading of misinformation online

PROTRACED COUNTERINSURGENCY: CHINESE COIN STRATEGY IN XINJIANG by MAJ J. Scott LaRonde, USA, 95 pages. In 1949, following the conclusion of its revolutionary war against the Chinese Nationalist forces, the People’s Liberation Army (PLA) peacefully occupied China’s western most province of Xinjiang. For nearly sixty years, the PLA has conducted a counterinsurgency against several, mostly Uyghur-led, separatist movements. Despite periods of significant violence, particularly in the early 1950s and again in the 1990s, the separatist forces have not gained momentum and remained at a level one insurgency. Mao ZeDeng is revered as a master insurgent and the father of Fourth Generation Warfare. Strategists in armies worldwide study his writings on revolutionary and guerilla warfare. This monograph concludes that Mao, as well as the communist leaders who followed him, was also successful at waging protracted counterinsurgency. For nearly sixty years, separatist movements in Xinjiang, Tibet, and Taiwan have all failed. This monograph analyzes the conflict in Xinjiang and concludes that the Chinese continue to defeat the separatist movement in Xinjiang through a strategy that counters Mao’s seven fundamentals of revolutionary warfare.

/pdf/approved-for-public-release-distribution-is-unlimited-2qs7y34q3g.pdf

Approved for Public Release; Distribution is Unlimited

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

/pdf/processing-social-media-messages-in-mass-emergency-a-survey-2wkyj3y36n.pdf

Processing Social Media Messages in Mass Emergency: A Survey

As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.

https://dl.acm.org/doi/pdf/10.1145/3219819.3219903

EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.

/pdf/aidr-artificial-intelligence-for-disaster-response-lpkt7euv5m.pdf

AIDR: artificial intelligence for disaster response

Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.

/pdf/extracting-information-nuggets-from-disaster-related-yyklc6oibm.pdf

Extracting Information Nuggets from Disaster- Related Messages in Social Media

During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or “tweets”). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting.

/pdf/tweetcred-real-time-credibility-assessment-of-content-on-2vwpu96qv9.pdf

TweetCred: Real-Time Credibility Assessment of Content on Twitter

The overflow of information generated during disasters can be as paralyzing to humanitarian response as the lack of information. Making sense of this information--Big Data--is proving an impossible challenge for traditional humanitarian organizations, which is precisely why they're turning to Digital Humanitarians. This new humanitarians mobilize online to make sense of vast volumes of data--social media and text messages; satellite and aerial imagery--in direct support of relief efforts worldwide. How? They craft ingenious crowdsourcing solutions with trail-blazing insights from artificial intelligence. This book charts the spectacular rise of Digital Humanitarians, highlighting how their humanity coupled with innovative Big Data solutions is changing humanitarian relief for forever. Praise for the book: ...examines how new uses of technology and vast quantities of digital data are transforming the way societies prepare for, respond to, cope with, and ultimately understand humanitarian disasters. --Dr. Enzo Bollettino, Executive Director, The Harvard Humanitarian Initiative, Harvard University ...explains the strengths and potential weaknesses of using big data and crowdsourced analytics in crisis situations. It is at once a deeply personal and intellectually satisfying book.--Professor Steven Livingston, Professor of Media & Public and International Affairs, Elliott School of International Affairs, George Washington University

Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.

/pdf/practical-extraction-of-disaster-relevant-information-from-3ot01u6h5h.pdf

Patrick Meier

Papers

AIDR: artificial intelligence for disaster response

Extracting Information Nuggets from Disaster- Related Messages in Social Media

TweetCred: Real-Time Credibility Assessment of Content on Twitter

Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response

Practical extraction of disaster-relevant information from social media