CrowdsouRS is a crowdsourced reputation system presented as a browser extension that allows users to rate the trustworthiness of websites on a 1 to 5 scale. The extension communicates ratings to a centralized server which calculates reputation scores for websites using a Bayesian method. A user study found the system was effective at identifying deceptive websites and most users believed CrowdsouRS could help address misleading online content. However, limitations included a small number of ratings for some sites and a biased participant pool.
TULUNGAN: A SLANDERING-RESISTANT REPUTATION SYSTEM FOR COLLABORATIVE WEB FILT...IJNSA Journal
Reputation systems measure the credibility of contributions and contributors in collaborative web systems. Measuring the credibility is significant since a collaborative environment generally allows anyone with Internet access to provide contribution.
Collaborative web systems are susceptible to malicious users who intentionally provide inaccurate contents. With the help of reputation systems, the effect of such malicious activities can be reduced. Reputation systems allow Internet users to rate the contributions made by other users. However, there are malicious users who will go beyond providing wrong contributions. They attempt to make reputation systems useless by launching attacks such as slandering. Slandering happens when a malicious user or group of malicious users intentionally provide a negative rating to accurate contributions provided by good users. Such activity lowers the reputation of good users and in most cases it even helps improve the reputation of slandering users.
This paper presents a reputation system called Tulungan that is designed to measure the contributor and rater reputation of users of a collaborative web system that is used for web filtering. User contributions are in the form of URL categorizations. It is the role of Tulungan to determine the correctness of the categorizations. A simulation is presented to validate the resilience of Tulungan in the presence of slandering users. The result of the simulation shows that Tulungan is not only resistant to slandering but it is still effective even if the number of good users is less than its slandering counterpart. Even if there are only 15% good users, the number of correct URL categorizations outnumbers incorrect contributions.
Community Marketing: Using Customer & Peer Endorsement to Lift Conversions, G...Percussion Software
Websites that leverage peer participation and allow visitors to interact socially drive more results. 79% of online retailers reported that consumer-generated rating and reviews improved site conversion rates (eMarketer). And the trend doesn’t affect only consumer-facing businesses: 90% of B2B buyers first turn to the internet, including user-generated content (TechTarget/CMO Council).
Learn:
• How leveraging peer endorsement can lift conversation rates and drive more sales, leads, or revenue.
• Different ways of fostering participation on your website - comments, ratings, reviews, and polls.
• Actionable steps you can take and best practices for implementing community features.
TULUNGAN: A SLANDERING-RESISTANT REPUTATION SYSTEM FOR COLLABORATIVE WEB FILT...IJNSA Journal
Reputation systems measure the credibility of contributions and contributors in collaborative web systems. Measuring the credibility is significant since a collaborative environment generally allows anyone with Internet access to provide contribution.
Collaborative web systems are susceptible to malicious users who intentionally provide inaccurate contents. With the help of reputation systems, the effect of such malicious activities can be reduced. Reputation systems allow Internet users to rate the contributions made by other users. However, there are malicious users who will go beyond providing wrong contributions. They attempt to make reputation systems useless by launching attacks such as slandering. Slandering happens when a malicious user or group of malicious users intentionally provide a negative rating to accurate contributions provided by good users. Such activity lowers the reputation of good users and in most cases it even helps improve the reputation of slandering users.
This paper presents a reputation system called Tulungan that is designed to measure the contributor and rater reputation of users of a collaborative web system that is used for web filtering. User contributions are in the form of URL categorizations. It is the role of Tulungan to determine the correctness of the categorizations. A simulation is presented to validate the resilience of Tulungan in the presence of slandering users. The result of the simulation shows that Tulungan is not only resistant to slandering but it is still effective even if the number of good users is less than its slandering counterpart. Even if there are only 15% good users, the number of correct URL categorizations outnumbers incorrect contributions.
Community Marketing: Using Customer & Peer Endorsement to Lift Conversions, G...Percussion Software
Websites that leverage peer participation and allow visitors to interact socially drive more results. 79% of online retailers reported that consumer-generated rating and reviews improved site conversion rates (eMarketer). And the trend doesn’t affect only consumer-facing businesses: 90% of B2B buyers first turn to the internet, including user-generated content (TechTarget/CMO Council).
Learn:
• How leveraging peer endorsement can lift conversation rates and drive more sales, leads, or revenue.
• Different ways of fostering participation on your website - comments, ratings, reviews, and polls.
• Actionable steps you can take and best practices for implementing community features.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
Trust Metrics In Recommender System : A Surveyaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
A brief report on Lunch Club, an online platform that allows people to connect for networking reasons. The report analyzes the company and provides recommendations for the future.
Social life in digital societies: Trust, Reputation and Privacy EINS summer s...i_scienceEU
Ralph Holz (Technische Universitat Munchen)
Pablo Aragon (Barcelona Media)
Katleen Gabriels (IBBT-SMIT, Vrije Univeriteit Brussel)
Janet Xue (Macquaire University)
Anna Satsiou (Centre for Research and Technology Hellas- Information Technologies Institute)
Sorana Cimpan (Universite De Savoie)
Norbert Blenn (Delft University of Technology)
More information: http://www.internet-science.eu/
Effective General Ledger and Journal Entry Fraud Detection Using Data AnalyticsFraudBusters
FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware.
The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts.
FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again.
White-Collar Crime Fighter Newsletter Subscribe Now at No Cost!
FraudResourceNet has made the premier Anti-Fraud newsletter, White-Collar Crime Fighter freely available to all. All this is required is to complete the registration form with your work email address!
The widely read newsletter, White-Collar Crime Fighter brings you expert strategies and actionable advice from the most prominent experts in the fraud-fighting business. Every two months you'll learn about the latest frauds, scams and schemes... and the newest and most effective fraud-fighting tools, techniques and technologies to put to work immediately to protect your organization.
When it comes to fraud, knowledge of the countless schemes, how they work and red flags to look for will help keep you, your organization and your clients safe.
At FraudResourceNet we understand this and take great pride in providing our FREE White Collar Crime Fighter newsletter -- filled with exclusive articles and tips to provide the knowledge you need.
Make sure you stay informed. Sign up for White Collar Crime Fighter newsletter and we’ll keep you up-to-date on special promos, training opportunities, and other news and offers from FraudResourceNet!
Signing up is easy and FREE. If you have not already subscribed to our newsletter, please sign up to get started!
Sign up for the White Collar Crime Fighter Newsletter (a $99 value ... now completely FREE)
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CrowdsouRS: A Crowdsourced Reputation System for Identifying Deceptive Web-contents
1. CrowdsouRS: A Crowdsourced Reputation
System for
Identifying Deceptive Web-contents
Presented By
Md. AbuTalha Masiur Rahman Siddiki
Reg: 2012331008 Reg: 2012331028
CSE, SUST CSE, SUST
Supervised By
Dr. Farida Chowdhury
Assistant Professor
Dept. of CSE, SUST
5. Motivation (Cont.)
▪ From Hate SpeechTo Fake News:The Content Crisis Facing Mark
Zuckerberg.[1]
▪ Facebook, GoogleTake StepsTo Confront Fake News.[2]
▪ Google steps up fight against fake news by expanding fact-check tool.
[3]
6. Motivation (Cont.)
▪ Poorly preparation for controlling and sanctioning the substantial
and increasing number of users and service providers with unethical,
malicious and criminal intentions.
7. Motivation (Cont.)
▪ Internet activities are not uniformly well intentioned, because they
are increasingly motivated by financial profit and personal gain which
can lead to unethical and criminal behavior.
▪ Early optimism of Internet replaced by cynicism and diminishing
trust in the Internet as a reliable platform for building markets and
communities.
▪ The purpose of security mechanisms is to provide protection against
malicious parties.
▪ Soft security mechanisms that can provide protection based on input
from the whole community.
8. Our Contribution
We present the CrowdsouRS, a browser-extension based
Trust and Reputation system for the web.
We present its architecture and implementation details
to illustrate our design choices that make it easily
deployable.
We present a thorough user-study to test the usability
and applicability of our system.
9. What is Trust?
▪ Trust, according to Deutsch (1973) is:
“Confidence that [one] will find what is
desired [from another] rather than what is feared.”
▪ Trust is a directional relationship between
two parties that can be called trustor and
trustee.
10. What is Reputation?
Reputation is about character or standing. A strong reputation builds
trust.
According to Cambridge English Dictionary,
"The opinion that people in general have about someone or something, or
how much respect or admiration someone or something receives, based on
past behavior or character.“
11. Trust Vs Reputation
▪ According to Josang, differences between trust and reputation can
be illustrated with the following statements:
a. "I trust Hanif Enterprise because of their reputation."
b. "I trust EnaTransport in spite of their bad reputation."
12. Reputation System
▪ A reputation system computes scores or ratings from community or
each consumer or a system called reputation computation engine.
Two unknown parties can be beneficiary from this system.
Consumer can take the decision looking at reputation
Service provider can also sell its service or product.
13. Reputation System (Cont.)
According to Resnick et al. reputation systems must have the following three
properties to operate at all:
▪ Longevity factor should be taken into account so that it can lead to an
expectation of future interactions.
▪ Current interactions ratings are grabbed and shared.
▪ Decisions about current interactions should be on the basis of ratings about
previous interactions.
16. Methodology
▪ We need to collect data from communities, users .
▪ For reputation system implementation in a general level we describe
a simple trust & reputation system extension named “CrowdsouRS”
which can be installed on browser.
18. Why Extension?
▪ This allows the reputation score of any Web page to be visualized to
the user, as well as the user to rateWeb sites andWeb pages.
▪ The extension communicates with a centralized server which keeps
the reputation scores of allWeb pages.
▪ AWeb page can be rated by the user with a discrete set of different
levels as described above.This architecture is illustrated in next.
24. Collecting Data
From browser’s active tab’s URL data is collected on 1 to 5 scale. The
scale is described as follows:
– Very Untrustworthy
– Untrustworthy
– Average
– Trustworthy
– VeryTrustworthy
25. Method Selection
▪ We have used the Bayesian system which encompasses
Bayesian multinomial system.
▪ The Bayesian multinomial system mainly is based on Dirichlet
probability distribution.
▪ As we have used one to five scale rating system, this method is
appropriate for our study.
26. Why Bayesian multinomial system ?
▪ Other methods vary so hugely, and they do not take longevity into
account.
▪ Bayesian can reduces loss function.
▪ Bayesian is more stable and robust method to compute reputation
score.
27. Score Calculation (Cont.)
Now, we use the formula
Here,
– i represents rating levels,
– sy(i) represents the score of I,
– Ry(i) represents the total number of votes of I,
– C is the value of choice , we consider c = 2 here,
– a(i) represents base rate of i.
28. Score Calculation (Cont.)
Now, Our actual reputation score can be computed as:
As we are taking five different levels i.e k = 5, v(i) can be:
30. Result Analysis(Cont.)
The result of average reputation scores for different websites as
generated by CrowdsouRS utilizing the ratings provided by the
participants is presented in previous slide.
– According to this figure, more than 70% people assume that Facebook and Google are
very trustworthy while only less than 2% people mark these web sites untrustworthy.
– However, when we calculate the reputation score of abcnews.com.co, 75% people
rightfully indicate that this website contains deceptive misleading contents.
This shows that CrowdsouRS has been able to identify deceptive
contents which validates our assumption.
32. Satisfaction with the Reliability of CrowdsouRS
We have evaluated the experience and
perception of users regarding CrowdsouRS.
It is evident from the result that the user
experience has been mostly positive.
Among all users, around 5%
participants have been satisfied.
And 22%of the participants have
been highly satisfied.
While 29.3% participants remain
neutral.
33. Influence of CrowdsouRS
Most of the participants
(around 70%) believe that
CrowdsouRS can be an
effective tool to fight against
deceptive misleading online
contents.
34. Limitations
Many problems and drawbacks in the reputation system and our study is
not free of limitations.
▪ The number of votes cast in the database is minimal in amount for
some websites and URLs.
▪ The majority of our participants from the campus-oriented
neighborhood (95% were students, and others were recent ex-
students) that our participant pool had been biased, they did not
represent the general public.
35. References
[1] NPR.org. (2016). From Hate SpeechTo Fake News:The Content Crisis Facing Mark
Zuckerberg. [online]Available at:
http://www.npr.org/sections/alltechconsidered/2016/11/17/495827410/from-hate-
speech-to-fake-news-the-content-crisis-facing-mark-zuckerberg [Accessed 18 Apr.
2017].
[2] NPR.org. (2016). Facebook,GoogleTake StepsTo Confront Fake News. [online]
Available at:
http://www.npr.org/sections/alltechconsidered/2016/11/15/502111390/facebook-
google-take-steps-to-confront-fake-news [Accessed 18 Apr. 2017].
[3] Sulleyman,A. (2017). Google steps up fight against fake news by expanding fact-check
tool. [online]The Independent. Available at: http://www.independent.co.uk/life-
style/gadgets-and-tech/news/google-fake-news-fight-fact-checking-tool-expand-
three-countries-brazil-mexico-argentina-a7589741.html [Accessed 18Apr. 2017].