The Reputation-specific slides from our IA Summit 2009 Workshop, The Architecture of Social Websites. Workshop given by Christina Wodtke, Joshua Porter, Christian Crumlish, and myself Bryce Glass.
5 Reputation Missteps (And how to avoid them)Bryce Glass
Web2.0 Expo presentation from F Randall Farmer and Bryce Glass, authors of the O'Reilly / Yahoo! Press book "Building Web Reputation Systems."
This talk addresses five common fallacies in designing your site or community's reputation system.
A simple model for reputation is presented: reputation is information used to make value judgments about a person or thing within a context for a time.
Each of the concepts are discussed in some detail, and finally a simple example is presented (traffic estimates on Google Maps for Android) that demonstrates the model at work, and how to use it to make system design improvements.
10 practical questions for designing a reputation system. This talk was (partially!) given at the 2008 IA Summit.
Some of the themes and points from this presentation are expanded on in our book, Building Web Reputation Systems (O'Reilly, 2010) See more at http://oreilly.com/catalog/9780596159801
Understanding people comes in a lot of flavors. An uncommon flavor is understanding people deeper than explanations and opinions. It's getting inside people’s minds to see how they achieve their larger human intentions and purposes without reference to your organization. The goal is to allow for later inspiration that represents the complicated inner world of people's approaches, rather than being constrained by existing systems and conventions.
After re-framing the problem as if your organization does not exist, you come back to reality with deeper understanding that influences your solutions.
Indi will define this deeper understanding, outline how collect the data, and show how to curate the knowledge in a depiction of the reasoning-patterns (mental model diagrams) and the thinking-styles (behavioral audience segments).
Focused on social media strategies and effective ways to monitor success for your non-profit or change-focused organization. Christopher Berry, Group Director of Marketing Science at Critical Mass will speak on practical social analytics.
Discovery Is The New Cocaine - Going Beyond EngagementMing
[Questions? mingyeow@gmail.com]
Web2 is about participation, but what comes after that? We think it is all about Discovery, the art of helping users serendipitously discover content and people that they did not know they wanted to know. Discovery is what makes people come back again and again, interact, and explore.
This deck explains what Discovery is, the psychological reasons behind it, and presents a set of very very practical examples and guidelines on how it can be implemented.
The inspiration for this deck was simple- We ourselves were frustrated by apparent random-ness at which we were implementing discovery for our own startup (discoverio), and were not able to find any resources that presented discovery in a holistic manner.
So just like anyone of you would have done, we decided to come out with process, and share it around with the rest of the world! :)
The most powerful piece is probably the very last slide, but we had not had time to expand on it yet.
Here it is, and we will love feedback! Contact details at the end. ;)
By Ming Yeow Ng, Yu-Shan Fung, Andreas Weigend
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
5 Reputation Missteps (And how to avoid them)Bryce Glass
Web2.0 Expo presentation from F Randall Farmer and Bryce Glass, authors of the O'Reilly / Yahoo! Press book "Building Web Reputation Systems."
This talk addresses five common fallacies in designing your site or community's reputation system.
A simple model for reputation is presented: reputation is information used to make value judgments about a person or thing within a context for a time.
Each of the concepts are discussed in some detail, and finally a simple example is presented (traffic estimates on Google Maps for Android) that demonstrates the model at work, and how to use it to make system design improvements.
10 practical questions for designing a reputation system. This talk was (partially!) given at the 2008 IA Summit.
Some of the themes and points from this presentation are expanded on in our book, Building Web Reputation Systems (O'Reilly, 2010) See more at http://oreilly.com/catalog/9780596159801
Understanding people comes in a lot of flavors. An uncommon flavor is understanding people deeper than explanations and opinions. It's getting inside people’s minds to see how they achieve their larger human intentions and purposes without reference to your organization. The goal is to allow for later inspiration that represents the complicated inner world of people's approaches, rather than being constrained by existing systems and conventions.
After re-framing the problem as if your organization does not exist, you come back to reality with deeper understanding that influences your solutions.
Indi will define this deeper understanding, outline how collect the data, and show how to curate the knowledge in a depiction of the reasoning-patterns (mental model diagrams) and the thinking-styles (behavioral audience segments).
Focused on social media strategies and effective ways to monitor success for your non-profit or change-focused organization. Christopher Berry, Group Director of Marketing Science at Critical Mass will speak on practical social analytics.
Discovery Is The New Cocaine - Going Beyond EngagementMing
[Questions? mingyeow@gmail.com]
Web2 is about participation, but what comes after that? We think it is all about Discovery, the art of helping users serendipitously discover content and people that they did not know they wanted to know. Discovery is what makes people come back again and again, interact, and explore.
This deck explains what Discovery is, the psychological reasons behind it, and presents a set of very very practical examples and guidelines on how it can be implemented.
The inspiration for this deck was simple- We ourselves were frustrated by apparent random-ness at which we were implementing discovery for our own startup (discoverio), and were not able to find any resources that presented discovery in a holistic manner.
So just like anyone of you would have done, we decided to come out with process, and share it around with the rest of the world! :)
The most powerful piece is probably the very last slide, but we had not had time to expand on it yet.
Here it is, and we will love feedback! Contact details at the end. ;)
By Ming Yeow Ng, Yu-Shan Fung, Andreas Weigend
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Presentation from Cassandra O'Neil on Strengths Based Approaches made on November 18, 2008 to the HNK Consultants Community and the Arizona Evaluation Network.
Wikimania 2009: Answers Community Moderationguest20df0e2
Yahoo! Answers is the largest Q&A service with hundreds of thousands of questions asked and answered every day. Traditional moderation systems failed to scale with the products growth. To address these challenges the team deployed a Community Moderation system which empowering trusted Answers members to report and remove abusive content automatically. One of the challenges to making the system work was to understand who to trust. The Answers team built a rich reputation model based on an analysis of over a dozen different system actions. Creating a successful community moderation system required changes in technology, community guidelines & policy, and user experience design.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
4. “ Reputation is another potential source of information about an opponent's fighting ability . Reputation is defined here as the estimation held by one individual of another individual's qualities or characteristics . Reputation is thus a property of one animal in relation to another. One animal's reputation may be learned by another through personal experience with it, or secondhand, through the experiences of others .” — Deception, Perspectives on Human and Nonhuman Deceit By Robert W. Mitchell, Nicholas S. Thompson Photo: “ Scars ” by Imansyah™ used under Creative Commons license .
5. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time .
6. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . What kinds of information? Where does this information come from ?
7. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . What kinds of value judgments can we make?
8. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . We call these reputable entities . Reputable entities may be people on your site, or they may be the objects & artifacts that they interact with. What qualities do good reputable entities possess? Is people reputation ( karma ) demonstrably different than content reputation ?
9. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . Can one reputation serve all contexts ? Global vs. local reputations Choosing the right scope . Is reputation portable ? Can I “carry” it from context to context?
10. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . Nothing lasts forever. Reputations should decay . Use time-based filters for reputation-ranked content.
14. RE as Person Reputation extends one’s identity… … esp. when said identity is weak Metadata is reputation Gathered from Ratings Associations & Affiliations are also reputation Rep can be displayed as a score
15. RE as Object Objects frequently come from people . Statistical Evidence Volume of response Community Ratings
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33. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . What kinds of information? Where does this information come from? How do we experience it ?
64. Reputation is… Information used to make a value judgment about an object or person within a context for a period of time . What kinds of value judgments? Who’s doing the judging? How are these judgments expressed ?