Social computing is a rapidly growing and constantly evolving technology that is aimed at increasing communication, encouraging collaboration, and enhancing productivity among people and resources. Social computing applications or Web 2.0 are built on a range of advanced and supporting technologies that enhance collective action and interaction which currently dominates the Web (Parameswaran & Whinston 2007).
Social computing applications are categorized into social media, social bookmarking, and social networks categories as identified by the continuing Web 2.0 trend (Schwartz et al. 2009; Amer-Yahia, 2009). Each of these categories has been embodied by various social software and web sites. Some of the best-known and equally famous social web sites that dominate the web are Facebook, YouTube, Twitter, Wikipedia, Delicious, and LinkedIn.
Leveraging principles and best practices from social media – sharing, prioritizing, discussing – enterprises can make knowledge sharing more efficient and effective.
The Changing Role of Information Professionals: New Opportunities Created by ...Larry Hawes
As Social Business management philosophy and Web 2.0 technologies are being introduced and adopted in organizations, information professionals in established roles (e.g. Corporate Librarian, Knowledge Manager) are investigating how they may best contribute and create value in the new environment. This presentation examines Enterprise 2.0 and Social Business trends and offers a vision for, and actual examples of, how information professionals can make themselves indispensable in collaborative organizations.
Social computing is a rapidly growing and constantly evolving technology that is aimed at increasing communication, encouraging collaboration, and enhancing productivity among people and resources. Social computing applications or Web 2.0 are built on a range of advanced and supporting technologies that enhance collective action and interaction which currently dominates the Web (Parameswaran & Whinston 2007).
Social computing applications are categorized into social media, social bookmarking, and social networks categories as identified by the continuing Web 2.0 trend (Schwartz et al. 2009; Amer-Yahia, 2009). Each of these categories has been embodied by various social software and web sites. Some of the best-known and equally famous social web sites that dominate the web are Facebook, YouTube, Twitter, Wikipedia, Delicious, and LinkedIn.
Leveraging principles and best practices from social media – sharing, prioritizing, discussing – enterprises can make knowledge sharing more efficient and effective.
The Changing Role of Information Professionals: New Opportunities Created by ...Larry Hawes
As Social Business management philosophy and Web 2.0 technologies are being introduced and adopted in organizations, information professionals in established roles (e.g. Corporate Librarian, Knowledge Manager) are investigating how they may best contribute and create value in the new environment. This presentation examines Enterprise 2.0 and Social Business trends and offers a vision for, and actual examples of, how information professionals can make themselves indispensable in collaborative organizations.
Social Media @ Jubilee Graduate Centre. Series of sessions on the use of social media in academic practice. Delivered to PhD students and Early Career Researchers (ECRs). Session Three: Collaboration and Networking. 17 February 2008. Co-authored with LeRoy Hill.
From Monologue to Dialogue: Building Relationships the Social WaySue Beckingham
An introduction to social media and other interactive tools to enhance your communication strategy. Presentation at the Professional Pensions Communications Forum, London. http://events.professionalpensions.com/commsforum/speakers
Sue Featherstone, Principal Lecturer and
Sue Beckingham, Educational Developer, Sheffield Hallam University
Provides a "101" tutorial on social media technologies and the way they are used to advance corporate social responsibility or cause marketing. Particular emphasis on frameworks to enhance a company's effectiveness at identifying the right tools and using them effectively.
The content journey from Creation to Collaboration and EngagementDheeraj Chowdhury
This preesentation was presented at the Gov2,0 conference in Canberra in October 2012, The aim was to share the future of content and the speed at which it is evolving. A case study of the evolution of content in NSW DEC. Finally a look at the emerging platforms to help enterprises leverage technology in developing an integrated social content strategy.
Conférence - Université Internationale du Multimédia - connecting people to b...Erwan Le Nagard
Présentation effectuée dans le cadre de l'UIM (Université Internationale du Multimédia). Quelle relation existe entre la dimension sociale et technique du lien dans les imaginaires d’Internet. En effet, la notion de lien est probablement la clé de voûte de toute réflexion lorsque l’on est professionnel du web. Du point de vue technique, le lien est l’élément unitaire de la performance et de la visibilité (SEO) et du point de vue social, le lien est la base relationnelle du marketing communautaire (SMO). La mise à disposition d’APIs par les principaux médias sociaux permet ainsi aux marques d’accéder aux informations partagées par les internautes pour en tirer partie et créer de la valeur, avec pour imaginaire sous-tendu la contraction de la distance sociale et l’interconnexion des systèmes comme si « des cerveaux se connectaient à d’autres cerveaux ».
An overview of social media for the Eugene Chamber's Women Business Leaders group - including how to maximize your reach on the social Web by partnering with Citizen Marketers.
BigFoot Digital: Dramaturgical self and content marketing strategyMelissa Hoover
In an era where online users share and disseminate public content, a 'digital' dramaturgical self image is being built and stored. The question arising from this is, who or what is building our digital dramaturgical self? Furthermore, are users even aware of having another builder involved in shaping the image of their digital dramaturgical self? The methodological approach will include a website remodelling, plugin installation—with the specific purpose of increasing audience conversation—setting up a content marketing strategy, social media engagement and collaboration efforts, and Web 2.0 digital sphere visibility. Projects such as Bigfoot Digital Footprint aim to increase audience awareness, but more so, to encourage audience engagement in PEST (Public Engagement with Science Technology) science communication and creation.
Social Media @ Jubilee Graduate Centre. Series of sessions on the use of social media in academic practice. Delivered to PhD students and Early Career Researchers (ECRs). Session Three: Collaboration and Networking. 17 February 2008. Co-authored with LeRoy Hill.
From Monologue to Dialogue: Building Relationships the Social WaySue Beckingham
An introduction to social media and other interactive tools to enhance your communication strategy. Presentation at the Professional Pensions Communications Forum, London. http://events.professionalpensions.com/commsforum/speakers
Sue Featherstone, Principal Lecturer and
Sue Beckingham, Educational Developer, Sheffield Hallam University
Provides a "101" tutorial on social media technologies and the way they are used to advance corporate social responsibility or cause marketing. Particular emphasis on frameworks to enhance a company's effectiveness at identifying the right tools and using them effectively.
The content journey from Creation to Collaboration and EngagementDheeraj Chowdhury
This preesentation was presented at the Gov2,0 conference in Canberra in October 2012, The aim was to share the future of content and the speed at which it is evolving. A case study of the evolution of content in NSW DEC. Finally a look at the emerging platforms to help enterprises leverage technology in developing an integrated social content strategy.
Conférence - Université Internationale du Multimédia - connecting people to b...Erwan Le Nagard
Présentation effectuée dans le cadre de l'UIM (Université Internationale du Multimédia). Quelle relation existe entre la dimension sociale et technique du lien dans les imaginaires d’Internet. En effet, la notion de lien est probablement la clé de voûte de toute réflexion lorsque l’on est professionnel du web. Du point de vue technique, le lien est l’élément unitaire de la performance et de la visibilité (SEO) et du point de vue social, le lien est la base relationnelle du marketing communautaire (SMO). La mise à disposition d’APIs par les principaux médias sociaux permet ainsi aux marques d’accéder aux informations partagées par les internautes pour en tirer partie et créer de la valeur, avec pour imaginaire sous-tendu la contraction de la distance sociale et l’interconnexion des systèmes comme si « des cerveaux se connectaient à d’autres cerveaux ».
An overview of social media for the Eugene Chamber's Women Business Leaders group - including how to maximize your reach on the social Web by partnering with Citizen Marketers.
BigFoot Digital: Dramaturgical self and content marketing strategyMelissa Hoover
In an era where online users share and disseminate public content, a 'digital' dramaturgical self image is being built and stored. The question arising from this is, who or what is building our digital dramaturgical self? Furthermore, are users even aware of having another builder involved in shaping the image of their digital dramaturgical self? The methodological approach will include a website remodelling, plugin installation—with the specific purpose of increasing audience conversation—setting up a content marketing strategy, social media engagement and collaboration efforts, and Web 2.0 digital sphere visibility. Projects such as Bigfoot Digital Footprint aim to increase audience awareness, but more so, to encourage audience engagement in PEST (Public Engagement with Science Technology) science communication and creation.
India is proposing an ambitious plan to substantially raise spending on providing free drugs for India’s 1.2 billion population. But there are doubts over the plan’s implementation. India wants to spend up to 300 billion rupees ($5.4 billion), or 0.5% of gross domestic product, on procuring drugs to be distributed through governmentrun hospitals and clinics by 2017. Currently, India spends about 60 billion rupees ($1.1 billion), or 0.1% of GDP.
Most government hospitals in India are overcrowded, understaffed and lack medicines and supplies. “Significant shortages in the number of doctors, nurses, paramedics and hospital beds per 1,000 population in India pose a great challenge for speedier
implementation of universal healthcare in the country,” said Tapan Ray, director general of the Organisation of Pharmaceutical Producers of India, or OPPI, a lobby group for Pharma MNCs in India.
So, you think your web application is reasonably secure? Well, based on the statistics, you're probably wrong. This talk examines real-world security problems in Rails applications, and shows how they can be mediated.
Indian Pharma - Top Guns Brainstorm at BrandStorm and FFE 2015Anup Soans
Indian Pharma's top talent from leading MNC and Domestic Pharma companies, Pharma Consulting firms like PwC and Healthcare Communication experts from Medulla - CEOs to SBU Heads will share their knowledge at BrandStorm and FFE 2015 on March 13th and 14th at The Westin Hotel, Mumbai.
Pik's portfolio of his recent works. Now he is a freelance editor, writer and marketing project director. You are welcome to take a look at my previous works. Feel free to contact me if there is anything we could work out together.
Have fun first, Business later.
Marc Dangeard, the Founder of Entrepreneur Commons, did this presentation at the YES - European Confederation of Young Entrepreneurs EXECOM event, in Thessaloniki, Greece, on Friday June 25th 2010
Comunicare con i Motori di Ricerca senza essere fraintesi: alla scoperta del ...Mamadigital
l primo vero fattore di ranking è rappresentato dalla capacità di comunicare correttamente con i Search Engines.
Il protocollo HTTP è l'opportunità per gestire efficacemente i processi di crawling nei nostri siti fornendo indicazioni in modo chiaro ai Search Engine. Nell'intervento verranno condivisi approcci ed esempi reali in cui la gestione attraverso il protocollo HTTP è stata risulitiva ed efficace anche in termini di effort, oltre che la recentissima case history in cui l'attivazione del protocollo HTTPS ha generato in Google inaspettati (e non reali) cali di visibilità.
Space travel is dangerous and expensive, but it doesn't have to be. Find out about an alternative way to reach orbit that is rapidly becoming feasible and may eventually change how we view our world.
Você quer saber como tornar sua estratégia no Instagram mais efetiva? Criar uma conta no Instagram é fácil. O mais difícil é conseguir atrair os seguidores certos e principalmente transformá-los em clientes. Então, por onde começar? Compartilhamos 5 Táticas Simples para você usar no Instagram para atrair mais seguidores e aumentar seu engajamento.
Então, passe por este SlideShare e veja como incrementar seu marketing no Instagram!
A presentation introducing various social media tools and their application in a university research environment. This presentation was given at York University, Toronto, Canada
Workshop delivered for the Enterprise and Innovation Academy on digital marketing for SMEs and workplace teams. The workshop is divided in five parts. Introduction, Digital Disruption, Content Marketing, Social Media and Planning.
This workshop was delivered along with practical tasks in another PowerPoint presentation. Participants were also encourage to use the internet to look for answers to the some of the tasks presented.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mining and analyzing social media hicss 45 tutorial – part 1
1. Mining and Analyzing Social Media
HICSS 45 Tutorial – Part 1
Dave King
January 4, 2012
2. Agenda: This is how the slides are
organized
• Part 1
– Introduction – Bio, Resources, Social Media
– Data Mining – Processes and Example
– Text Mining – General Processes and Example
– Predicting the Future – The Portmanteaus
• Part 2
– Sentiment Analysis
– Social Network Analysis - Introduction
2
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
3. Biography: Dave King
• Currently, EVP of Product Development
and Management at JDA Software
• 30 years in enterprise package
software business
• 15 years as university professor
• 14 years as Co-Chair of the Internet &
Digital Economy Track (HICSS)
• Long time interest in various aspects of
E-Commerce & Business Intelligence
• Tutorial topic primarily reflects a
personal interest and tangentially a
job(s) related interest.
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
4. Personal Experiences with
Analytics
• Taught applied statistics, math modeling & mathematical sociology
• In software R&D for 30 years
– Optimization in the 80s
– Natural Language Frontends
• NLI Query & CMU Robotics Lab
– EIS Competitive Analysis
• Dow Jones and Reuters
• Verity Topics
• NewsAlert
– InXight’s Hyperbolic Tree
– Supply Chain Analytics
• In the case of text analysis and it’s practical application, often
audiences have been small, bewildered, and fleeting
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
5. Mining and Analytics Resources
5
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
6. Mining and Analytics Resources
6
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
7. Mining and Analytics Resources
7
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
8. Mining and Analytics Resources
8
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
12. Social Media Defined
Marta Kagan
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
13. Social Media Defined: …Sort of …
13
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
14. Social Media Defined:
Actually, it’s 33 Definitions
1. Media for social interaction, using highly accessible and scalable 18. Not one thing. It’s five distinct things:
communication techniques. 19. Digital, content-based communications based on the interactions enabled by a
2. Various user-driven (inbound marketing) channels (e.g., Facebook, Twitter, plethora of web technologies
blogs, YouTube). 20. Collection of online platforms and tools that people use to share content,
3. Most transparent, engaging and interactive form of public relations profiles, opinions, insights, experiences, perspectives and media itself,
4. What we do and say together, worldwide, to communicate in all direction at facilitating conversations and interactions online between groups of people.
any time, by any possible (digital) means. 21. Platform/tools.
5. New marketing tool that allows you to get to know your customers and 22. Act of connecting on social media platforms.
prospects in ways that were previously not possible. 23. How businesses join the conversation in an authentic and transparent way to
6. Platforms that enable the interactive web by engaging users to participate in, build relationships.
comment on and create content as means of communicating 24. The notion that social media is about the technology that facilitates individuals
7. Consists of any online platform or channel for user generated content. and groups of people to connect and interact, create and share.
8. Digital content and interaction that is created by and between people. 25. Any of a number of individual web-based applications aggregating users who
9. Shift in how we get our information. Social media allows us to network, to find are able to conduct one-to-one and one-to-many two-way conversations.
people with like interests, and to meet people who can become friends or 26. Media channel that relies on listening and conversation, as opposed to a
customers. monologue, to get your point across, make a connection and build a
10. Platforms for interaction and relationships, not content and ads. relationship.
11. Online platforms and locations that provide a way for people to participate in 27. Social media is all about leveraging online tools that promote sharing and
these conversations. conversations, which ultimately lead to engagement with current and future
12. People’s conversations and actions online that can be mined by advertisers customers and influencers in your target market.
for insights but not coerced to pass along marketing messages. 28. Social media: Evolution, Revolution and Contribution -by the ability of
13. Tools, services, and communication facilitating connection between peers everybody to share and contribute as a publisher
with common interests. 29. Social media is communication channels or tools used to store, aggregate,
14. Online technologies and practices that people use to share content, opinions, share, discuss or deliver information within online communities.
insights, experiences, perspectives, and media themselves. 30. Social Media is simply another arrow to be shot in a company’s marketing
15. Ever-growing and evolving collection of online tools and toys, platforms and quiver.
applications that enable all of us to interact with and share information. 31. Social media platforms make it easier to share information–usually online.
Increasingly, it’s both the connective tissue and neural net of the Web. 32. Any object or tool, that connects people in dialogue or interaction — in
16. Reflection of conversations happening every day, whether at the supermarket, person, in print, or online.
a bar, the train, the watercooler or the playground. 33. Wild, Wild West of Marketing, with brands, businesses, and organizations
17. Online text, pictures, videos and links, shared amongst people and jostling with individuals to make news, friends, connections and build
organizations. communities in the virtual space.
14
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
15. Social Media Defined: If a Picture isn’t
worth a 1000 words, then …
15
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
16. Social Media Defined
Online technologies and practices
for social interaction
enabling the sharing of opinions, insights,
experiences, perspectives and media itself
16
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
17. Social Media Defined: Categories
17
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
19. Social Media is Huge: Users
Marta Kagan
750 Million: Facebook
200 Million: Twitter
100 Million: LinkedIn
19
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
20. Social Media is Huge!
Marta Kagan
If Facebook
were a country,
it would be the
3 rd largest in
the world
20
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
21. Social Media Data:
Research Opportunity
“Every day, Twitter
generates more
social network
data than the
entire field of SNA
possessed 10
years ago.”
21
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
23. Social Media Data:
Part of a Bigger Picture
23
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
24. Social Media Data:
Ways in big data is creating value
• Makes information
transparent and usable at
much higher frequency.
• Provides more transactional
data in digital form, that can
be used to improve
performance across the
board.
• Allows ever-narrower
segmentation of customers to
tailor products or services.
• Improves decision-making
through sophisticated.
• Improves the development of
the next generation of
products and services
24
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
25. Data Mining: Defined
Discovering meaningful
patterns from large data
sets using pattern
recognition technologies.
25
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
26. Data Mining: CRISP-DM
Real-World
Data
Data Consolidation
Business Data
Understanding Understanding
Data
Preparation
Data Cleaning
Deployment
Modeling
Data Transformation
Evaluation
Data Reduction
Well-Formed
Cross-Industry Standard Process for Data Mining Data
26
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
27. Data Mining:
General Data Assumptions
Structured
Transformed
Well-Formed
27
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
28. Data Mining: Example
Affinity Analysis
28
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
29. Data Mining: Example
1. Market Basket Analysis: Items for Sale:
Apples Bananas Cherries
2. Possible Transactions: With one item or a collection of items selected as
the Driver or Independent Variable
No X Y No X Y
1 A B 7 C A
2 A C 8 C B
3 A B C 9 C A B
4 B A 10 A B C
5 B C 11 A C B
6 B A C 12 B C A
3. Objective is to empirically determine those groups of items that occur
frequently together in a set of transactions, producing a set of rules of the
form X -> Y.
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
30. Data Mining: Example
1 1 1 1
Transaction ID Items
2 1 0 0
1 Apple
3 0 1 1
1 Banana
4 0 1 1
1 Cherry
5 1 1 0
2 Apple 6 1 1 0
3 Banana 7 1 0 1
3 Cherry 8 1 1 0
4 Banana 9 1 1 1
4 Cherry 10 1 1 0
5 Apple Sum 8 8 5
5 Banana
6 Apple
6 Banana
Standard Market Basket Measures:
7 Apple
7 Cherry
Support: Rule’s coverage (% match antecedents)
8 Apple N(X & Y)/ N(T) Example: N(A & B)/ N(T) = 2/7 = 29%
8 Banana
9 Apple Confidence: Rule’s predictive ability (% consequent | antecedent)
9 Banana N(X & Y)/ N(X) Example: N(A & B)/ N(A) = 2/4 = 50%
9 Cherry
10 Apple Lift: Predictive improvement (ratio of observed support for X&Y to support if X& Y
10 Banana independent -- S(XuY)/S(X)S(Y) Example: (2 x7)/(4/7)(5/7) = .7 or 70%
30
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31. Data Mining: Example
Rule selection usually based Parameters
Min. Support 40%
on minimum support & confidence Min. Confidence 75%
No X Y N(XuY) N(T) S(XuY) N(X) Conf N(Y) S(X) S(Y) Lift Rule
1 A B 6 10 60% 8 75% 8 80% 80% 94% Ok
2 A C 3 10 30% 8 38% 5 80% 50% 94%
3 A B C 2 10 20% 8 25% 4 80% 40% 78%
4 B A 6 10 60% 8 75% 8 80% 80% 117% Ok
5 B C 4 10 40% 8 50% 5 80% 50% 125%
6 B A C 2 10 20% 8 25% 3 80% 30% 104%
7 C A 3 10 30% 5 60% 8 50% 80% 150%
8 C B 4 10 40% 5 80% 8 50% 80% 200% Ok
9 C A B 2 10 20% 5 40% 6 50% 60% 133%
10 A B C 2 10 20% 6 33% 5 60% 50% 111%
11 A C B 2 10 20% 3 67% 8 30% 80% 278%
12 B C A 2 10 20% 4 50% 8 40% 80% 156%
31
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32. Data Mining:
Simple Example
But, what if the baskets were described in the
following manner:
– Jane bought a handful of maraschinos and a couple of
granny smiths.
– Harold purchased a bag of appls and 2 bananas.
– Bill paid for a pound of cherries but decided not to buy
the three durians because of their odor.
How could we automate the analysis?
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
33. Social Media Data:
33
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34. Social Media Data: Commonality?
34
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35. Text Mining: Defined
Using data mining to discover patterns
in a collection of documents
35
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36. Text Mining:
CRISP-Like Processes
Real-World
Text Data
Document
Business
Understanding
Document
Understanding
Consolidation
Document Establish the
Preparation
Corpus
Deployment
Documents
Modeling
Corpus Refinement
(Token, Stem, Stop…)
Feature Selection
Evaluation
& Weighting
Term-
Doc-Matrix*
36
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
37. Text Mining Process:
Sample Corpa
• Brown Corpus – first million word corpus compiled in 60s at
Brown U., 500 samples across 15 genres, each ~2000 words with
POS tags (Lancaster-Oslo-Bergen Corpus – British equivalent)
• Linguistic Consortium Treebanks – collections of manually
tagged and parsed (tree structures) of sentences from a variety of
sources (includes well-known Penn Treebank collection)
• Reuters 21578, RCV1 & V2, TRC2 -- collections (1000s of)
Reuter’s English & multi-lingual news stories classified into topics and
grouped into training & test sets
• Pang & Lee’s Sentiment Analysis – 1000 positive and 1000
negative movie reviews
• MEDLINE – An extensive collection of articles and abstracts
(18M+) used in a variety of biomedical and linguistic text mining
applications
• WordNet® -- large lexical database of English grouped into sets of
cognitive synonyms (synsets) and interlinked by means of
conceptual-semantic and lexical relations.
• 20 Newsgroups -- collection of approximately 20,000 newsgroup
documents, partitioned (nearly) evenly across 20 different
newsgroups each representing a different topic.
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
38. Text Mining Process:
Corpus Refinement
Common representation of tokens within and between documents
Eliminate
Tokenization Normalize Stemming
Stop Words
• Tokenization —Parse the text to generate terms. Sophisticated
analyzers can also extract phrases from the text.
• Normalize — Convert them to lowercase.
• Eliminate stop words — Eliminate terms that appear very often (e.g.
the, and, …).
• Stemming — Convert the terms into their stemmed form—remove
plurals and different word forms (e.g. achieve, achieves, achieved –
achiev) [note: word about synonyms – WordNet Synset]
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
39. Text Mining:
Feature Extraction & Weighting
Feature
Extraction “Bag of Words, Terms
or Tokens”
Vector Representation ->
Word, Term, Token or Pairs-Triplets
x Doc Matrix
Token1 Token2 Token3 Token4 …
Doc1 1 2 2 4 Words or Tokens are
Doc2 4 2 3 0
attributes and documents
Doc3 1 1 1 0
Doc4 1 1 1 2
are examples
…
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
40. Text Mining:
Transforming Frequencies
• Binary Frequencies: tf =1 for tf>0; otherwise 0
• Term Frequencies: tf(i,j)/Sum of tf(i,j) in Doc K
• Log Frequencies: 1 + log(tf) for tf>0; otherwise 0
• Normalized Frequencies: Divide each frequency by SQRT
of Sum of Squares of the frequencies within the vector
(column)
• Term Frequency–Inverse Document Frequency
– TF * IDF
– Inverse Document Frequency: log(N/(1+D)) where N is total
number of docs and D is number with term
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
41. Text Mining: Simple Example
Listening Post is an art installation by Mark
Hansen and Ben Rubin that culls text
fragments in real time from thousands of
unrestricted Internet chat rooms, bulletin
boards and other public forums.
41
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42. Text Mining: Simple Example
42
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43. Text Mining: Simple Example
sentence
imageid
Blogs feeling
“I feel” posttime
“I’m feeling” postdate
posturl
15-20K
gender
Feelings
born
Per Day
country
Contains state
Every
1 of 5000 city
10 Mins
Pre-Determined lat
Feelings lon
conditions
43
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44. Text Mining: Simple Example
Query API Result
<?xml version="1.0" ?>
http://api.wefeelfine.org
<feelings>
:8080/ <feeling imageid="-
ShowFeelings? mZmybPrOGTZ+xukpcU7jg"
display=xml& feeling="better"
sentence="i feel almost 100 better
returnfields= aside from that weird sandy feeling in
Sentence my throat"
&postdate=2010-11-25 posttime="1321633467"
postdate=2010-11-25="0"
&limit=500
posturl="http://jenngreenleaf.blogspot.com
/2011/11/im-coming-down-with-cold-or-
am-i.html"
gender="0" country="united states"
state="maine" city="richmond"
lat="44.091522" lon="-69.801787"
conditions="4" />
…
44
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45. Text Mining: Simple Example
• i'm done believing you don't know what i'm feeling
• i feel so out of place
• i'm feeling healthy
• i never feel down when i'm with her
• i love the feeling
• i feel like i've been run over by a truck
• i feel so positive today
• i feel like a poor man's pin up girl
45
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
46. Text Mining: Simple Example
• Input String (128925 chars; 24282 spaces)
– "i have found to be helpful especially during those times when i am feeling
discouragedni have a 50km commute and just the lack of the sense of freedom that
driving brings just leaves me feeling scaredni seem to be feeling better mostly…"
• Tokenize (26465 tokens)
– ['i', ', 'have', 'found', 'to', 'be', 'helpful', 'especially', 'during', 'those', 'times', 'when', 'i',
'am', 'feeling', 'discouraged', 'i', 'have', 'a', '50km', 'commute', 'and', 'just', 'the', 'lack',
'of', 'the', 'sense', 'of', 'freedom', 'that', 'driving', 'brings', 'just', 'leaves', 'me', 'feeling',
'scared', 'i', 'feel', 'noone', 'know', 'if', 'you', 'were', 'me', 'you', 'will', 'feel', 'the', 'same',
'way‘, …]
• Set of Tokens (3045 distinct tokens)
– ["'", "'believe", "'d", "'en", "'encoding", "'feedlinks", "'forever", "'gets", "'http",
"'ismobile", "'isprivate", "'item", "'languagedirection", "'ll", "'locale", "'ltr", "'m",
"'mefaked", "'mobileclass", "'mr", "'no", "'okay", "'on", "'pagetitle", "'pagetype", "'re",
"'s", "'t", "'toned", "'url", "'us", "'utf", "'ve", "'yes", '0', '034', '039', '0aeverytime', '0d',
'10', '100', '101',…]
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
47. Text Mining: Simple Example
Corpus Word Length Sentence Length Lexical Diversity
We Feel Fine 4 17 8
Gutenberg Corpus
Austen-persuasion.txt 4 23 16
Bible-kjv.txt 4 33 79
Blake-poems.txt 4 18 5
Carroll-alice.txt 4 16 12
Melville-moby.txt 4 24 15
Milton-paradise.txt 4 52 15
Shakespeare-caesar.txt 4 12 8
Shakespeare-hamlet.txt 4 13 7
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
48. Text Mining: Simple Example
• Eliminate Stopwords (175 words - 'a', 'about', 'above', 'after', …)
– Set of tokens (12827) with stopwords eliminated ['ab', 'abit', 'able', 'abs',
'absolute', 'absolutely', 'absorb', 'abuse', 'accomplished',
'accomplishment', 'achieve', 'achieved', 'across', 'acted', 'action',
'activities', 'activity', 'actually', 'acura', 'add', …]
– Content (11896 or 45% of tokens not stopwords – 4053 with tokens
starting with apostrophes and #s eliminated )
• Stemming
– Stemmed tokens (11896) ['abdomen', 'abdul', 'abil', 'abl', 'abrupt', 'absolut',
'abstract', 'academ', 'accept', 'accid', 'accomplish', 'accur', 'accus', 'accustom',
'achi', 'achiev', 'acknowledg', 'across', 'action', 'activ‘…]
– Set of tokens in stemmed content(2283) ['abdomen', 'abdul', 'abil', 'abl', 'abrupt',
'absolut', 'abstract', 'academ', 'accept', 'accid', 'accomplish', 'accur', 'accus',
'accustom', 'achi', 'achiev',…]
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
49. Text Mining: Simple Example
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
51. Text Mining: Simple Example
Madness Murmerings Montage
Mobs Metrics Mounds
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
52. Prediction
Collective, macroscopic
trends which can be
scientifically inferred by
harnessing publicly
accessible data from
the Internet.
52
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
54. Prediction: Sources
Easily accessible digital traces:
What we surf
Whom we “friend”
What we say
Where we go
What we buy
How we play
54
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
57. Prediction: Infodemiology
Information + Epidemiology:
Science of distribution and
determinants of information
in an electronic medium,
specifically the Internet, or
in a population, with the
ultimate aim to inform public
health and public policy
Coined by Gunther Eysenbach, Univ. of Toronto
57
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59. Prediction: Infodemiology
A Major Application - Practical
Vi
Regional, Weekly Syndromic Surveillance
59
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60. Prediction: Infodemiology
An Alternative Approach
Text Mining of Worldwide Newswires, Web Sites
and Various Offline Reports
60
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61. Prediction: Infodemiology
Utilizing Aggregate Search Data
Monitoring and analyzing
queries from Internet search
engines or peoples' status
updates on microblogs for
syndromic surveillance to
predict disease outbreaks
61
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
64. Prediction: Infodemiology
Utilizing Aggregate Search Data
Dependent Dependent Traditional, Aggregate
Variable at Variable at Publicly Search
Time t Time t - n Available Index or
(Standard = b0 + b1 (Standard + b2 Explanatory + b3 Social +e
Publicly Publicly Variable Media
Available Available Freq.
Measure) Measure) Count
Standard Linear Prediction Model
64
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
65. Prediction: Infodemiology
Utilizing Aggregate Search Data
“Detecting Influenza Epidemics Using Search
Engine Query Data” (Ginsberg et. al.), 2/19/09
• Aggregating historical logs of search queries
from 2003-2008, computing weekly time series
• Logit(P) = b0 + b1 * logit(Q) + e
– P – percentage of ILI physician visits
– Q – query fraction 45 highest influenza queries
• r is between .80-.96 for 9 regions
65
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
71. Prediction: Infodemiology
Utilizing Tweets
“Nowcasting Events from the Social Web
with Statistical Learning,” Lampos and
Cristianini, ACM IS&T, 9/11
• Text analysis of 50M tweets for 3 regions of UK
from 6/09-4/10 (303 days)
• HPA weekly reports of GP consultations with ILI
diagnosis correlated with number of “hybrid
grams”
• Average “r” of .911
71
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
72. Prediction: Infodemiology
A Major Application – Text Analysis
50M Tweets
Corpus
3 Region UK, 6/09-4/10
Corpus Lower Stop
Tokens Stems
Refinement Case Words
Feature 1- 2 Hybrid N-Gram
Selection Grams Grams Grams Freqs
72
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73. Prediction: Infodemiology
Utilizing Tweets
Discarded
when
n<50
BoLasso - Bootstrap LASSO (least absolute shrinkage and selection operator
73
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
76. Prediction:
Now + Forecasting:
Predicting the present
by analyzing large
volumes of data that
can be used to
"forecast" current
events for which
official analysis has
not been released
76
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
78. Prediction:
Sample Studies with Search
Authors Date (Mnth-Year) Dependent Variables Explanatory Variables Model Results
Song, Pan, Ng Apr-10 Weekly Hotel Bookings in Indexed Search Volumes from Log of Room Nights for Log of Search Test various statistical models; all gave
Charleston, SC Google Trends/Insights Jan Volumes - Charleston, Travel Charleston, reasonable forecasts. Best fit model
2008-Aug 2009 Charleston Hotels, Charleston was Autoregressive Distributed Lag
Restaurants, Charleston Tourism (ADLM) with a lag period of 6 weeks.
Kholodilin, Apr-10 Year-on-Year Growth Rate 220 Google Trend/Insights Y-o-Y monthly URPC growth rates for 3 Query term principal components
Podstawski, of Monthly US Real Search terms related to Priv sets of regressors -- Sentiment outperform standard Sentiment and
Sliliverstovs Private Consumption, Consumption reduced to 10 (consumer sentiment and confidence); Financial Indicators. A combination of
ALFRED db of Fed Rsrv of principal components for Financial (short term and long term two of the factors work best -- those
St. Louis montly periods from Jan 2005 interest rates and S&P 500); Query related to mobility and health care
to Dec 2009 (combinations of principal components of consumption.
query terms)
Choi, Varian Apr-09 US Census Bureau Google Trend/Insight query Google Trend indices for query Simple seasonal AR models and fixed-
Advance Monthly Retail indices for categories and subcategories related to (log values) of
effects models that includes relevant
Sales (general and subcategories related to retail overall monthly retail trade (NAICS Google Trend variables tend to
specific) and Travel sales (general and specifix) categories), automotive sales, home outperform models that exclude these
(Visitor arrival in Hong and related to Travel sales and travel. variables. In some cases small gains, in
Kong) other substantial.
McLaren, Q2-11 Official monthly Google Trend/Insight query For unemployment, linear AR model For unemployment forecasts, claimant
Shanbhogue unemployment data and indexes for the term "Job with query term, claimant count, and GfK count strongest followed by query term.
housing price growth in Seekers Allowance (JSA)" for consumer confid. as exp vars; for housing For housing prices, the query term was
the UK from June 2004-Jan unemployment and "Estate price growth with query term, Home much stronger than HBF and RICS data.
2011 Agents" for housing Builders and Royal Instit. of Chartered
Surveyors price growth balances as exp
vars.
78
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79. Prediction:
Sample Studies with Social Media
Authors Date (Mnth-Year) Dependent Explanatory Variables Model Results
Variables
Asur, Mar-10 Box-office Promotion tweets-retweets for a particular movie, Regression of 1st weekend box Promotional tweets are weakly
Huberman revenues for (24) tweet rates for particular movie per hour, ratio of office revenues by promotional correlated 1st weekend revs. Tweet
movies positive to negative sentiments for the movie tweets-retweets, by tweet rates rates are very strongly correlated
vs. Hollywood Stock Exchange (min .9) and a stronger predictor than
prices, and 2nd weekend HSX. Finally, tweet rates are strongly
revenues by tweet rates and the correlated with 2nd weekend
sentiment ratio. revenues and sentiments improve
the forecasts slightly.
Gruhl, Guha, Aug-05 Amazon Sales Number of mentions of the book/author in over 300K Cross correlation of time series While sales rank is a poor predictor of
Kumar, Novak, Rank for 2340 blogs whose postings that were maintained by IBM's for sales rank and mentions. the change in sales rankings, a prior
Tomkins bestselling books WebFountain project (over 200K postings/day) spike in mentions predicts quite well
in 4 month period a future spike in sales rank.
(Jul 2004-Aug
2004) and spikes
in these sales
ranks
Sadikov, Aug-09 Movie critic Basic features that count movie references in blogs, Linear regression for weekly Minimal correlation between
Parameswaran, ranking, user count movie references taking into account ranking rankings and sales data by blog rankings and references and
Venetis ranking, 2008 and indegree of the blogs where they appear, references and sentiment. sentiment. Strong correlation
gross sales, consider only references made within a time window between references and gross sales
weekly box office before or after a movie release date, features that but week with sentiment. Strongest
sales (weeks 1-5) consider positive sentiment; and combinations of relationships with timing of
these. References based on spinn3r.com blog data references in weeks after release.
set 11/07-11/08
79
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81. Prediction: Idiom, a Sculpture of
10s of 1000s of Books
81
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82. Prediction: It comes in many
Shapes but not Sizes
Omphalos Book Cell
Matej Krén
Gravity Mixer
82
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83. Prediction: Culturnomics
Culture + Genomics:
Application of high-
throughput data
collection and analysis
to the study of human
culture.
83
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84. Prediction: Culturomics
“Quantitative Analysis of Culture Using
Millions of Digitized Books,” Science, 12/16/10.
84
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86. Prediction: Culturomics 2.0
Culturomics 2.0: Forecasting Large-Scale Human
Behavior Using Global News Media Tone in Time
and Space, Kalev Leetaru, 9/11
• The tone of real-time consciousness reflected in the media can
be used to forecast broad social behavior.
• Combined three massive news archives totaling more than 100
million articles worldwide to explore the global consciousness
of the news media.
• Employs a large shared-memory supercomputer (University of
Tennessee SGI Altix supercomputer Nautilus with 1024
processors and 4-TB of memory)
• Using the tone and location of the reports, (claims to have)
predicted the outcome of the Arab Spring and the location of Bin
Laden within radius of 125 miles
86
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
89. Prediction: Culturomics 2.0
Features of Stories or Tweets
• Tone/Positivity/Negativity. Ratio of + to - tone (-
100 to 100)
• Polarity. Emotional charge (0 to 100)
• Activity. Intensity of "active language" (0 to 100)
• Personalization. Degree to which the writer
attempts to bring the reader into the fold (0 to
100)
• Questions/Exclamations. Tweet tone indicators of
non-word items
• Geocoding. Location of story content
89
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
90. Prediction: Culturomics 2.0
Features of Stories or Tweets
100M Articles from the: Sentiment Mining,
New York Times (1945-05) Geocoding,
Sum. of Wrld Brdcasts (1979-10) Entity Extraction Geocoding
Google News articles (2006-11) Nautilus Supercomputer Feature Scores
2.4 Petabyte
Network with over
10M entitles
90
Copyright 2011 JDA Software Group, Inc. - CONFIDENTIAL
92. Prediction: Culturomics 2.0
NY Times View of Tone
http://contentanalysis.ichass.illinois.edu/Culturomics20/nyt-movie-
1000x1000.gif
92
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93. Prediction: Culturomics 2.0
SWB View of Tone
http://contentanalysis.ichass.illinois.edu/Culturomics20/swb-movie-
1000x1000.gif
93
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