Using Social Media Analytics - Social Media Week Dubai March 2017Umar khan
Business use social media channels to connect with customers, to answer questions, and to just "be there" for their community. How do they know if social media channel is successful, and they are meeting their goals? Most social media channels have analytics or insights that help figure this out. Let's explore analytics for different social media channels and explains what businesses should track and why. Also, what it takes to create a strong and sustainable social media presence, including successes and bummers.
Using Analytics To Investigate, Evaluate & DecideTunheim
Presentation by Noelle Hawton, David Erickson and Natalie Wires of Tunheim Partners on February 4, 2010, before the Minnesota Council on Nonprofits Communicator Series: Communication to Conversation: Engaging in Today’s World.
2014 Guide to the Social Media Landscape. Discussion topics include:
(1) What is social media?
(2) Important Terms and Definitions
(3) List of Top Networks and Platforms
(4) Helpful Tools : Google Alerts
(5) 2014 Web and Social Trends
the current state of... Search Engine Optimization (SEO) (Oct, 2015)Brian Alpert
Presented at the Smithsonian National Museum of Natural History Social Media Summit, 7/21/15, updated 10/1/15. A presentation exploring the current state of affairs vis a vis Search Engine Optimization (SEO). Areas of exploration include on-page and off-page SEO, the role social media plays, and tips for search-optimizing the leading social media sites. Overview of the current state of App Indexing. Links to many resources are sprinkled throughout.
Google Analytics: MVPs and Game-Changing New FeaturesBrian Alpert
Part two of Seb Chan & Brian Alpert’s "Web Metrics and Google Analytics for Museums" workshop looks at some of the most significant recent changes to Google Analytics. With many improvements released over the course of the 2013-14, Google dramatically altered the landscape of the tool's capabilities. The presentation discusses such GA "MVPs" as Advanced Segmentation and Event Tracking, and provides an overview of some of the many new features, including Demographics and Interests reports, custom channels and content grouping, and the coming change to Universal Analytics. Case studies and slides showing best practices and "tips and tricks" are also included, as well as links to the valuable resources used to collect the information. Presented 4/2/14 at Museums and the Web 2014, Baltimore Maryland.
Using Social Media Analytics - Social Media Week Dubai March 2017Umar khan
Business use social media channels to connect with customers, to answer questions, and to just "be there" for their community. How do they know if social media channel is successful, and they are meeting their goals? Most social media channels have analytics or insights that help figure this out. Let's explore analytics for different social media channels and explains what businesses should track and why. Also, what it takes to create a strong and sustainable social media presence, including successes and bummers.
Using Analytics To Investigate, Evaluate & DecideTunheim
Presentation by Noelle Hawton, David Erickson and Natalie Wires of Tunheim Partners on February 4, 2010, before the Minnesota Council on Nonprofits Communicator Series: Communication to Conversation: Engaging in Today’s World.
2014 Guide to the Social Media Landscape. Discussion topics include:
(1) What is social media?
(2) Important Terms and Definitions
(3) List of Top Networks and Platforms
(4) Helpful Tools : Google Alerts
(5) 2014 Web and Social Trends
the current state of... Search Engine Optimization (SEO) (Oct, 2015)Brian Alpert
Presented at the Smithsonian National Museum of Natural History Social Media Summit, 7/21/15, updated 10/1/15. A presentation exploring the current state of affairs vis a vis Search Engine Optimization (SEO). Areas of exploration include on-page and off-page SEO, the role social media plays, and tips for search-optimizing the leading social media sites. Overview of the current state of App Indexing. Links to many resources are sprinkled throughout.
Google Analytics: MVPs and Game-Changing New FeaturesBrian Alpert
Part two of Seb Chan & Brian Alpert’s "Web Metrics and Google Analytics for Museums" workshop looks at some of the most significant recent changes to Google Analytics. With many improvements released over the course of the 2013-14, Google dramatically altered the landscape of the tool's capabilities. The presentation discusses such GA "MVPs" as Advanced Segmentation and Event Tracking, and provides an overview of some of the many new features, including Demographics and Interests reports, custom channels and content grouping, and the coming change to Universal Analytics. Case studies and slides showing best practices and "tips and tricks" are also included, as well as links to the valuable resources used to collect the information. Presented 4/2/14 at Museums and the Web 2014, Baltimore Maryland.
Metrics, Metrics, Everywhere - Choosing the Right Ones for Your Website and S...Brian Alpert
Museums and the Web 2015 workshop includes the analytics process, case studies and a social media framework. Presented by Brian Alpert, Erin Blasco, Effie Kapsalis and Sarah Banks, Smithsonian Institution.
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...Brian Alpert
A common chorus from museum professionals is how challenging it is to make data-driven decisions with which to improve their programs. Popular tools such as Google Analytics are intuitive and seemingly easy-to-use, yet when the time comes to use data to measure a program's stated goals, too often the main question surrounding the data is "So what?" This workshop will focus on bringing clarity to this challenge. Presented at MCN2012, on 11/7/12.
Measuring Social Media: Assessing Your ImpactKelli Hansen
Using social media effectively can help people feel more personally connected to your library, but it also takes a lot of work. How can you know whether you're getting the most for your investment? Effectively measuring social media can tell you what’s working for your audience, what needs to change, and where to concentrate your time and effort. In this presentation, find out about planning techniques and free tools that can help you get the data you need to better reach and serve your users through social media.
Presented at the 2015 MOBIUS Annual Conference, June 2, 2015.
http://mobiusannualconference2015.sched.org/event/b6bb7ef596edfb69fc32e130a5bdca9d#.VW3hls9Viko
The Ultimate Guide to using Social Media Media AnalyticsSocialmetrix
How to get insights from quantitative data to improve your
social media performance.
-How do you measure social media?
-How to use quantitative data to improve your Audience.
-How to use your social analytics to create a Content MKT Strategy for social media.
-How to use quantitative data to improve your engagement.
-How to get valuable insights from your Competitors Analytics.
-How to get valuable insights from your Campaign Analytics.
Introduction into Social Media and Social Media AnalyticsDr Matt McDougall
An introduction to social media and the measurement platforms of social media analytics. Some interesting examples of social media gone wrong and some other examples of companies doing social media.
Metrics, Metrics, Everywhere: Choosing the Right Ones for Your Website and So...Brian Alpert
Social media has connected millions of people in ways never before possible, disrupting the landscape and breathing new life into the old questions: "Why is this important and how do we know it's working?" Only now, the answers are more complex. Today's landscape is a splintered collection of new channels, sublimely named yet inscrutable metrics, and a dizzying array of tools both free and paid, offering a dizzying range of possibilities with which to answer the classic analytics question, "What do I measure?" and its first cousin, "What does that have to do with our program?" At this MCN 2013 workshop, the presenters worked with participants to refine and articulate this conversation through a series of examples, case studies, and recommendations. In addition to social media, a healthy dose of web analytics is included, with a particular focus on Google Analytics.
Social Media Monitoring—A presentation for @MidwestBrian Huonker
Why Social Media Monitoring?
The answer is simple: People trust the opinions of others more than brand advertisements, official websites or PR releases. In fact, people trust the opinion of others, especially their peers, more than almost anything. This means that online opinions on your brands and products really matter. Quite a lot, to be frank. So, let us list a number of reasons for you to keep a close eye on the online chatterbox:
- Measuring Marketing effectiveness
- Manage crisis pro-actively
- Do effective online public relations
- Benchmark your competition
- Identify new market opportunities
- Get market insights in the most cost effective manner
Learn more about the @Midwest Social Media Conference by visiting atMidwest.com.
Social Media 101 for Not-for-Profits [Webinar]Shai Coggins
This is the slide deck I used for the social media 101 webinar I conducted for Connecting Up 2012 (#CU12) conference delegates last 19 Apr 2012. Great turnout + plenty of interesting questions after the presentation.
Search social-media-&-reputation-management-thunder-seoMax Thomas
Presentation on Search, Social Media and Online Reputation Management given by Max Thomas of Thunder SEO to SDSIC in San Diego, CA, on January 7, 2011.
Growth hacking is a marketing technique developed by technology startups which uses creativity, analytical thinking, and social metrics to sell products and gain exposure. It can be seen as part of the online marketing ecosystem, as in many cases growth hackers are simply good at using techniques such as search engine optimization, website analytics, content marketing and A/B testing which are already mainstream. Growth hackers focus on low-cost and innovative alternatives to traditional marketing, e.g. utilizing social media and viral marketing instead of buying advertising through more traditional media such as radio, newspaper, and television. Growth hacking is particularly important for startups, as it allows for a "lean" launch that focuses on "growth first, budgets second. "Facebook, Twitter, LinkedIn, AirBnB and Dropbox are all companies that use growth hacking techniques.
Metrics, Metrics, Everywhere - Choosing the Right Ones for Your Website and S...Brian Alpert
Museums and the Web 2015 workshop includes the analytics process, case studies and a social media framework. Presented by Brian Alpert, Erin Blasco, Effie Kapsalis and Sarah Banks, Smithsonian Institution.
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...Brian Alpert
A common chorus from museum professionals is how challenging it is to make data-driven decisions with which to improve their programs. Popular tools such as Google Analytics are intuitive and seemingly easy-to-use, yet when the time comes to use data to measure a program's stated goals, too often the main question surrounding the data is "So what?" This workshop will focus on bringing clarity to this challenge. Presented at MCN2012, on 11/7/12.
Measuring Social Media: Assessing Your ImpactKelli Hansen
Using social media effectively can help people feel more personally connected to your library, but it also takes a lot of work. How can you know whether you're getting the most for your investment? Effectively measuring social media can tell you what’s working for your audience, what needs to change, and where to concentrate your time and effort. In this presentation, find out about planning techniques and free tools that can help you get the data you need to better reach and serve your users through social media.
Presented at the 2015 MOBIUS Annual Conference, June 2, 2015.
http://mobiusannualconference2015.sched.org/event/b6bb7ef596edfb69fc32e130a5bdca9d#.VW3hls9Viko
The Ultimate Guide to using Social Media Media AnalyticsSocialmetrix
How to get insights from quantitative data to improve your
social media performance.
-How do you measure social media?
-How to use quantitative data to improve your Audience.
-How to use your social analytics to create a Content MKT Strategy for social media.
-How to use quantitative data to improve your engagement.
-How to get valuable insights from your Competitors Analytics.
-How to get valuable insights from your Campaign Analytics.
Introduction into Social Media and Social Media AnalyticsDr Matt McDougall
An introduction to social media and the measurement platforms of social media analytics. Some interesting examples of social media gone wrong and some other examples of companies doing social media.
Metrics, Metrics, Everywhere: Choosing the Right Ones for Your Website and So...Brian Alpert
Social media has connected millions of people in ways never before possible, disrupting the landscape and breathing new life into the old questions: "Why is this important and how do we know it's working?" Only now, the answers are more complex. Today's landscape is a splintered collection of new channels, sublimely named yet inscrutable metrics, and a dizzying array of tools both free and paid, offering a dizzying range of possibilities with which to answer the classic analytics question, "What do I measure?" and its first cousin, "What does that have to do with our program?" At this MCN 2013 workshop, the presenters worked with participants to refine and articulate this conversation through a series of examples, case studies, and recommendations. In addition to social media, a healthy dose of web analytics is included, with a particular focus on Google Analytics.
Social Media Monitoring—A presentation for @MidwestBrian Huonker
Why Social Media Monitoring?
The answer is simple: People trust the opinions of others more than brand advertisements, official websites or PR releases. In fact, people trust the opinion of others, especially their peers, more than almost anything. This means that online opinions on your brands and products really matter. Quite a lot, to be frank. So, let us list a number of reasons for you to keep a close eye on the online chatterbox:
- Measuring Marketing effectiveness
- Manage crisis pro-actively
- Do effective online public relations
- Benchmark your competition
- Identify new market opportunities
- Get market insights in the most cost effective manner
Learn more about the @Midwest Social Media Conference by visiting atMidwest.com.
Social Media 101 for Not-for-Profits [Webinar]Shai Coggins
This is the slide deck I used for the social media 101 webinar I conducted for Connecting Up 2012 (#CU12) conference delegates last 19 Apr 2012. Great turnout + plenty of interesting questions after the presentation.
Search social-media-&-reputation-management-thunder-seoMax Thomas
Presentation on Search, Social Media and Online Reputation Management given by Max Thomas of Thunder SEO to SDSIC in San Diego, CA, on January 7, 2011.
Growth hacking is a marketing technique developed by technology startups which uses creativity, analytical thinking, and social metrics to sell products and gain exposure. It can be seen as part of the online marketing ecosystem, as in many cases growth hackers are simply good at using techniques such as search engine optimization, website analytics, content marketing and A/B testing which are already mainstream. Growth hackers focus on low-cost and innovative alternatives to traditional marketing, e.g. utilizing social media and viral marketing instead of buying advertising through more traditional media such as radio, newspaper, and television. Growth hacking is particularly important for startups, as it allows for a "lean" launch that focuses on "growth first, budgets second. "Facebook, Twitter, LinkedIn, AirBnB and Dropbox are all companies that use growth hacking techniques.
Social Media @Home and @Work:Understanding Who Is Using and WhyCaroline Dangson
1. How does IDC define social media?
2. What is the size of the market?
3. How has social media evolved?
4. How is it changing the way consumers and businesses communicate?
5. Case Studies
6. IDC U.S. social networking behaviors and attitudes survey data
7. Implication of research and essential guidance
Delivered at SMX Social Media 2014, this presentation explores the user social sharing behavior and how to craft a user experience that capitalizes on user preference for social search.
Web analytics and social media metrics provide you with powerful ways to track how people interact with your content and what they’re saying about your foundation. They help you understand what works (and what doesn’t!) with your constituents and donors.
Similar to “I Like” - Analysing Interactions within Social Networks to Assert the Trustworthiness of Users, Sources and Content (20)
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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…
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
2. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Introduction
Current Social Networks:
Provide generic privacy settings for sharing information
Do not take user’s trust into account
3. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Introduction
In reality, we only share parts of our
information with whomever we trust
4. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Social Factors
Trust judgments are influenced by Social Factors:
Past interactions with a person
Opinions of a person’s actions
Other people’s opinions
Rumours
Psychological factors impacted over time
Life events
and so forth
These can be hard to compute since the
information required is limited and unavailable in
Social Networks
5. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Research Questions
1. What is the user’s perception of trust?
2. Which information extracted from Social
Networks is useful for computing trust?
3. For what and for whom can trust be
computed from the information in
Social Networks?
6. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey
To analyse how trust can be inferred from Social
Networks
We focus on whether trust can be asserted from
user interactions within the Social Networks
User interactions with:
Other users
Content shared within the Social Networks
7. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey
User interactions:
Sharing of content from external sources
Re-sharing or retweeting content
“Like”, or “+1” or “favourite”
Comments or replies
Tags or mentions
8. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey
178 participated in the survey: 65% male and 35% female
Age:
Social Network Accounts:
Age Category Participants
18 - 20 3%
21 - 29 45%
30 - 39 32%
40 - 49 13%
50 - 59 6%
60+ 1%
Social Networks Participants
Facebook 88%
Google+ 69%
Twitter 82%
LinkedIn 85%
None of the Above 1%
9. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey
Occupations of participants
Occupation Categories Participants
Computer and Mathematics 59%
Education, Training and Library 26%
Business and Financial 13%
Management 8%
Architecture and Engineering 6%
Arts, Design, Entertainment, Sports and Media 5%
Life, Physical and Social Sciences 3%
Office and Administrative Support 2%
Healthcare Support 1%
Community & Social Service 1%
Sales 1%
Unemployed 1%
10. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey
Two parts:
1. Usage Patterns
2. User’s Trust Perception in Social Networks
Usage Patterns: how often users use each social
user interaction and on which Social Network
User’s Trust Perception: analyses what users trust
when they use these social user interactions
11. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
How often do you share content from external
sources within Facebook, Google+, Twitter and
LinkedIn?
User Survey:
Usage Patterns
12. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Usage Patterns
How often do you re-share or retweet what other
users share within Facebook, Google+, Twitter and
LinkedIn?
13. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Usage Patterns
How often do you use the like, +1 and favourite
buttons on Facebook, Google+, Twitter and
LinkedIn?
14. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Usage Patterns
For what do you use the like, +1 and favourite
buttons on Facebook, Google+, Twitter and
LinkedIn?
15. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Usage Patterns
How often do you comment or reply on Facebook,
Google+, Twitter and LinkedIn?
16. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Usage Patterns
How often do you tag or mention other users on
Facebook, Google+, Twitter and LinkedIn?
17. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What is your perception of the meaning of the word
“Trust”?
18. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What do you trust when you share external content
into Facebook, Google+, Twitter and LinkedIn?
19. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What do you trust when you re-share or retweet
content within Facebook, Google+, Twitter and
LinkedIn?
20. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What do you trust when you like, +1, or favourite
within Facebook, Google+, Twitter and LinkedIn?
21. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What do you trust when you comment or reply to
posts within Facebook, Google+, Twitter and
LinkedIn?
22. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What do you trust when you tag or mention other
users within Facebook, Google+, Twitter and
LinkedIn?
23. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
User’s Trust Perception
What do you trust when you are tagged or
mentioned within Facebook, Google+, Twitter and
LinkedIn?
24. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Summary
Overall Participants’ Activity of Social User
Interactions
25. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
User Survey:
Summary
Overall Participants’ Perception of Trust
26. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Asserting Trust:
Trusting the Source
Trust for the source can be asserted from:
The share button
The re-share or retweet buttons
Trusting the source:
» denotes the user’s subjective trust value for a particularτ
source
» w denotes the trust a third party user has in the user’s social
graph
» s denotes the number of shares and re-shares related to the
source
27. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Asserting Trust:
Trusting the Content
Trust for content can be asserted from:
The share button
The re-share or retweet buttons
The like, +1 and favourite buttons
Trusting the content:
» denotes the user’s subjective trust value for a particularτ
content
» w denotes the trust value a third party user has in the user’s
social graph
» c denotes the number of shares, re-shares, likes, +1s and
favourites related to the content
28. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Asserting Trust:
Trusting the User
Trust for the user (i.e. requester) can be asserted
from:
The like, +1 and favourite buttons
The comments or replies to posts
The tags of the requester tagged by the information owner
The tags of the information owner tagged by the requester
Trusting the user (i.e. requester):
» denotes the user’s subjective trust value for a particularτ
requester
» r denotes the number of likes, +1s, favourites, comments,
replies and tags related to the user and the requester
29. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Conclusion & Future Work
We focused on:
Analysing the user’s perception of trust and
How trust can be inferred from Social Networks
User survey that analysed the usage patterns and
the user’s trust perception of:
The share button
The re-share or retweet buttons
The like, +1 or favourite buttons
The comment or reply buttons
The tag or mention buttons/options
30. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Conclusion & Future Work
The results have revealed that users are concerned
with asserting trust for:
The source that created the content
The content
The user requesting personal information
Future work:
Implementing the trust assertions in our Privacy
Preference Framework (see previous publications)
– To enforce privacy preferences based on these trust
assertions
31. INSIGHT Centre for Data Analytics www.insight-centre.org
Semantic Web & Linked Data
Research Programme
Thanks!
@owensacco
owen.sacco@deri.org
@johnbreslin
john.breslin@nuigalway.ie
Editor's Notes
Generic privacy settings – SN do not provide privacy settings for each part of the information SNs do not cater for trust, neither capture trust not even provide users to enter any trust information Assumes that every person in ones social graph has the same trust level Does not provide users to enter trust values for different user lists Cumbersome to manage contacts with respect to trust
We aim to answer these questions by analysing user interactions in Social Networks
The results show that participants prefer to share content from external sources into Twitter and Facebook since 39% of the participants frequently share within Twitter and 33% within Facebook. 31% of the participants occasionally share external content into Twitter and 47% into Facebook. LinkedIn is the least used for sharing external content since 64% never share external content into LinkedIn. Google+ is also not popular for sharing since 40% never share content within Google+.
The results show that participants prefer to re-share or retweet from within Twitter since 38% of the participants frequently retweet within Twitter. Although only 16% frequently re-share in Facebook, 56% do re-share occasionally whilst in Twitter 34% occasionally retweet. LinkedIn is the least popular Social Network for re-sharing since 70% never re-share. Google+ is the second least preferred Social Network since 46% never re-share.
The results show that the “Like” button in Facebook is the most frequently used since 48% of the participants use that functionality whereas only 13% frequently use the “ Favourite ” button in Twitter. However, 39% occasionally use the “ Favourite ” button in Twitter whereas 36% occasion- ally use the “ Like ” button in Facebook and in Google+ 36% occasionally use the “ +1 ” . LinkedIn is the least preferred Social Network for using the “ Like ” button since 66% never use this functionality. In Google+, 35% of the participants never use the “ +1 ” and in Twitter, 30% of the participants never use the “ Favourite ” button.
In Facebook, 69% “Like” comments, 67% “Like” status updates, 63% “Like” photos, 54% “Like” external content, 34% like videos and 32% like profile updates. In the other Social Networks, the results show a similar trend whereby participants “Like”, “+1” or “ Favourite ” more status updates, comments, external content and photos. In Twitter for in- stance, external content is the most “ Favourite ” since 35% of the participants “ Favourite ” external content and 31% “ Favourite ” status updates. Whereas in Google+, 25% of the participants “ +1 ” external content and 20% “ +1 ” status updates. Once again, LinkedIn is the least preferred Social platform for using the “ Like ” button.
The results show that commenting in Facebook and replying in Twitter are the most frequently used since 31% frequently comment in Facebook and 25% reply in Twitter. Moreover, 52% occasionally comment in Facebook and 38% reply in Twitter. Once again, LinkedIn is the least preferred Social Network for commenting since 67% partic- ipants never comment in LinkedIn. Moreover, in Google+ 46% never comment.
The results show that tagging and mentioning other users is the least of the user interaction types used. Only 24% frequently tag in Facebook; 21% frequently mention other users in Twitter and in Google+ only 1% frequently tag. Moreover, 42% occasionally tag users in Facebook, 42% occasionally mention users in Twitter and 19% occasionally tag users in Google+. Again, LinkedIn is the least preferred Social Network for tagging since 78% of the participants never tag. In twitter, 21% never mention users; in Facebook 24% never tag users; and in Google+ 54% never tag.
From the results it can be noted that 65% of the participants are more concerned with trusting the source. 57% of the participants perceive trust as trust in the content and trust in the belief that a person will act according to the user’s expectations. Surprisingly, only 45% of the participants have selected that trust means a person is reliable if s/he shares content with the participant.
The results show that participants trust the content most when they use the share button.
Participants also perceive trust- ing the content more whilst re-sharing or retweeting what other users have already shared. This result and the result of sharing external content within these Social Networks illustrate that the act of sharing results more in trusting the content.
The results once again show that by using these features, participants trust more the content. Moreover, the results also show that by using this functionality, participants trust more the person who is sharing the content rather than the source who created the content. Therefore, the “Like”, “+1” and “ Favourite ” buttons are used to capture the trust for the content and the trust for the person sharing the content.
With this user interaction type, participants trust more the person who created the post. This is because comments or replies might also contain content that might reflect distrust in the content or source. This is illustrated in the results as 37% of the participants trust the content and 24% trust the source whilst commenting or replying. In this study we only focus on how we can capture trust from the act of the interactions. However, it would be interesting as future work to analyse how to capture trust or distrust from the semantics of the comments or replies.
The results show that the participants trust more the person who they are tagging.
These results illustrate that the perception of trust in the other person tagging or mention the user is lower than the perception of trust in the other person being tagged or mentioned by the user.
These results show that all the user interaction types are mostly used in Facebook and Twitter. Hence, these Social Networks are the optimal for capturing trust for these social user interaction types.
These results illustrate that we can therefore correlate trust and the user interactions as follows: The trust for the source who created the content can be captured using (1) the sharing and (2) the re-sharing or retweeting user interactions; The trust for the content can be captured using (1) the sharing, (2) the re-sharing or retweeting; and (3) the “like” or “+1” or “ favourite ” user interactions; and The trust for the user requesting personal information (i.e. the person) can be captured using (1) the “like” or “+1” or favourite; (2) the comments or replies; (3) tags or mentions and (4) tagged or mentioned user interactions.
The user’s subjective trust value for the source can therefore be calculated as the weighted average of all the shares and re-shares of content related to the source weighted by the trust of users either directly connected to the user or indirectly connected through other users that have shared and re-shared any content related to the source.
The user’s subjective trust value for the content can therefore be calculated as the weighted average of all the shares; re-shares or retweets; likes, +1s and favourites of the content; weighted by the trust of users either directly connected to the user or indirectly connected through other users that have shared, re-shared, liked, +1 and favourite the same content.
The user’s subjective trust value for the requester can therefore be calculated as the relationship between the sum of interactions consisting of: the “likes”, “+1s” or “ favourites ” of the content related to the user and the requester; the comments or replies between the user and the requester; the tags of or tagged by the user and the requester; and the total sum of all the interactions of the user.