Social Network Analysis in Predictive Policing

Social Network Analysis in Predictive Policing

Author: Mohammad A. Tayebi

Publisher: Springer

Published: 2016-10-11

Total Pages: 141

ISBN-13: 3319414925

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This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.


Predictive Technology in Social Media

Predictive Technology in Social Media

Author: Cristina Fernández-Rovira

Publisher: CRC Press

Published: 2022-07-07

Total Pages: 210

ISBN-13: 1000626148

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Can behaviour on social media predict future purchase patterns? Can what we click on social media foresee which political party will we vote for? Can the information we share on our wall foretell the next series I might want to watch? Can the likes on Instagram and Facebook predict the time one will spend on digital platforms in the next hour? The answer is no longer science fiction. It points to the ability of mainstream social media platforms such as Facebook and Twitter to be able to deliver specialised advertising services to highly targeted audience segments controlled by the billions of devices that flood our daily lives. At the same time, it highlights a more relevant problem: can social media guide, suggest or impose a certain behaviour or thought? Everything seems to indicate that they can do it. Predictive Technology in Social Media comprises 10 essays that reflect on the power of the predictive technology of social media in culture, entertainment, marketing, economics and politics. It shows, from a humanistic and critical perspective, the predictive possibilities of social media platforms, as well as the risks this entails for cultural plurality, everyday consumption, the monopolistic concentration of the economy and attention, and democracy. The text is an invitation to think, as citizens, about the unbridled power we have ceded to digital platforms. A new voice to warn about the greatest concentration of communicative power ever seen in the history of humanity.


Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

Author: Bart Baesens

Publisher: John Wiley & Sons

Published: 2015-08-17

Total Pages: 406

ISBN-13: 1119133122

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Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.


Predictive Analytics

Predictive Analytics

Author: Eric Siegel

Publisher: John Wiley & Sons

Published: 2016-01-12

Total Pages: 368

ISBN-13: 1119153654

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"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a


The SAGE Handbook of Social Media Research Methods

The SAGE Handbook of Social Media Research Methods

Author: Anabel Quan-Haase

Publisher: SAGE

Published: 2022-09-02

Total Pages: 860

ISBN-13: 1529788889

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The SAGE Handbook of Social Media Research Methods spans the entire research process, from data collection to analysis and interpretation. This second edition has been comprehensively updated and expanded, from 39 to 49 chapters. In addition to a new section of chapters focussing on ethics, privacy and the politics of social media data, the new edition provides broader coverage of topics such as: Data sources Scraping and spidering data Locative data, video data and linked data Platform-specific analysis Analytical tools Critical social media analysis Written by leading scholars from across the globe, the chapters provide a mix of theoretical and applied assessments of topics, and include a range of new case studies and data sets that exemplify the methodological approaches. This Handbook is an essential resource for any researcher or postgraduate student embarking on a social media research project. PART 1: Conceptualising and Designing Social Media Research PART 2: Collecting Data PART 3: Qualitative Approaches to Social Media Data PART 4: Quantitative Approaches to Social Media Data PART 5: Diverse Approaches to Social Media Data PART 6: Research & Analytical Tools PART 7: Social Media Platforms PART 8: Privacy, Ethics and Inequalities


Predictive Social Media

Predictive Social Media

Author: Jim Lupkin

Publisher: Spov Publishing

Published: 2021-05-17

Total Pages: 220

ISBN-13: 9781737036814

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A Proven System to Grow Your Business . . . Today. Social media is the most energized business frontier the world has ever known, yet no one has been able to successfully predict outcomes. Until now. Jim Lupkin, one of the world's foremost social media architects, disrupts the status quo in social media. From the metrics behind his exclusive word-of-mouth formula to his luminous challenge to redefine success, Jim empowers readers to escape the traditional, artificial game of business in favor of an authentic, relationship-first social media movement. Vast and precise, innovative and actionable, Predictive Social Media escorts businesses of all sizes, solopreneurs to global corporations, out of the online darkness and into the light of a predictive way to engage the world.


Applied Predictive Modeling

Applied Predictive Modeling

Author: Max Kuhn

Publisher: Springer Science & Business Media

Published: 2013-05-17

Total Pages: 595

ISBN-13: 1461468493

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Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.


AI-Powered Social Media Marketing 2025

AI-Powered Social Media Marketing 2025

Author: Jason P Anderson

Publisher: Jason P Anderson

Published: 2024-10-15

Total Pages: 121

ISBN-13:

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Have you ever wondered how some brands effortlessly dominate social media while others struggle to gain visibility? In today’s fast-paced digital landscape, harnessing the power of artificial intelligence (AI) is no longer just an option; it is essential for success. This guide dives deep into the intersection of AI and social media marketing, revealing how you can leverage cutting-edge technologies to transform your online presence and drive significant revenue. Explore the revolutionary impact of AI on social media marketing and discover key trends that are shaping the future. From automating content creation and audience engagement to optimizing advertising strategies, this resource provides actionable insights to elevate your marketing game. Understand why integrating AI is crucial for generating income online and learn how businesses and creators are monetizing their social platforms effectively. Discover essential AI tools designed to streamline your marketing efforts, enhance customer engagement, and analyze performance metrics. With a focus on practical applications, you will learn how to automate tasks, create compelling content, and use data-driven insights to refine your strategies. Whether you are an entrepreneur, marketer, or content creator, these insights will empower you to make smarter decisions and boost your earnings. Delve into platform-specific strategies for maximizing profits on popular social media channels. Gain insights on how to optimize your presence on Facebook, Instagram, TikTok, and more by utilizing AI to drive engagement, enhance targeting, and increase conversion rates. With practical tips and techniques, you will learn how to monetize your efforts through ads, e-commerce, influencer marketing, and more. In addition to practical applications, this guide addresses the ethical considerations of using AI in social media marketing. Understand the importance of data privacy and transparency in building trust with your audience, ensuring long-term loyalty and success. Prepare to unlock new revenue streams and elevate your marketing strategy by embracing the potential of AI in social media. This comprehensive resource equips you with the knowledge and tools necessary to navigate the evolving landscape of digital marketing, ensuring you remain competitive and profitable. Embrace AI, transform your marketing efforts, and achieve the success you’ve always envisioned.


Opinion Mining and Sentiment Analysis

Opinion Mining and Sentiment Analysis

Author: Bo Pang

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 149

ISBN-13: 1601981503

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This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems.


Social Media Management

Social Media Management

Author: Amy Van Looy

Publisher: Springer Nature

Published: 2022-05-03

Total Pages: 290

ISBN-13: 303099094X

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This is the second edition of the undergraduate textbook 'Social Media Management' which extends the original edition's scope beyond the business angle. The textbook continues with the perspective of organizations - not individuals - and clarifies the impact of social media on their different departments or disciplines, while also exploring how organizations use social media to create business value. To do so, the book pursues a uniquely multi-disciplinary approach by embracing IT, marketing, HR, and many other fields. While the first edition was inspired by the rise of social media tools, the second edition is characterized by a digital economy with increasing digitalization efforts due to newly emerging technologies in Industry 4.0 and the COVID-19 pandemic. Readers will benefit from a comprehensive selection of extended topics, including strategies and business models for social media, influencer marketing, viral campaigns, social CRM, employer branding, e-recruitment, search engine optimization, social mining, sentiment analysis, crowdfunding, and legal and ethical issues. Each chapter starts with one or more teaser questions to arouse the readers’ interest, which will be clarified per topic. The second edition also provides ample self-test materials and reflection exercises.