All About Trust and Reputation in the Digital World

Tag: Trust

Crowdfunding, Keeping Digital Criminals at Bay

Crowdfunding Keeping Digital Criminals at Bay

As crowdfunding is gaining popularity as a means of raising money in the digital world, thanks to the rules under consideration by the U.S. Securities and Exchange Commission under the 2012 JOBS Act, trust and reputation management in relation to online collaborative funding is becoming a serious issue.  Indeed, both donation-based crowdfunding and investment-based crowdfunding attract digital criminals.

More precisely, donation-based crowdfunding services, provided by sites such as Kickstarter and Fundly, and investment-based crowdfunding services, provided by sites like AngelList and MicroVentures, are prone to:

  • digital scamming where scammers create fake projects to steal money from the backers.  In one case last year, one large crowdfunding site almost handed over $120,000 to a fake start-up.  There were more than 3,000 backers involved;
  • digital money-laundering where  “dirty” money coming from criminal activities is invested through equity, debt, or tax deductible donations.

In this context, proper trust and reputation management strategies should be implemented by crowdfunding sites.  In our view, an efficient trust and reputation strategy, that aims to detect fraudsters preemptively, has to combine:

  • human intelligence-based approaches that implement formal due diligence and vetting processes;
  • algorithmic-based approaches that automatically analyze the credentials provided by each participant,  monitor the participants’ activities and reputation in digital arenas such as social networks and digital marketplaces, and whenever possible check their track records within other crowdfunding sites. 

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Trust, Reputation and Digital Dualism

Trust, Reputation and Digital Dualism

Digital dualism is defined by social media theorist Nathan Jurgenson as the belief that the digital and physical worlds are separated, with the physical world being fully real and the digital world being virtual.  The digital dualists posit that the two worlds are engaged in a competition (for time, attention, participation, etc.). In their view, the physical and digital worlds are not complementary.  Nathan Jurgenson rejects digital dualism as a fallacy.  He argues that what happens in one world has direct effects in the other world.

We completely agree with Nathan Jurgenson.  From a reputation standpoint, what happens in the digital world has a direct impact on the reputation of individuals, businesses, and institutions in the physical world.  Indeed, over the past few years, we have witnessed countless situations where a bad reputation in the digital world wrecked careers, drove companies out of business and even led to appalling consequences such as the suicide of cyberbullied teenagers.

Conversely, what happens in the physical world has a great impact on trust and reputation in the digital world.  For example, customers’ experience (good or bad) with businesses and institutions in the physical world (e.g., restaurants, hotels, shopping centers, healthcare institutions, education institutions) translates into reputation in the digital world, thanks to online services such as those provided by review sites.    

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Fake Reviews, the Plague of Rating Services

Fake Reviews The Plague of Rating Services

Once again, Yelp is  in the news for fake reviews.  This time around, it is a mattresses and furniture  store in La Mesa, California.  “This business created a dozen or so accounts on Yelp from the same IP address (their IP address) which they used to create fake reviews of their own business,” said Vince Sollitto, Vice President of Corporate Communications at Yelp.  “The business then used those accounts to message people on Yelp offering to pay $25 by PayPal or mail for a five star vote,” Sollitto said.

Yelp Consumer Alert

As outlined in a previous post in this blog, fake ratings, along with the lack of models to manage trust placed in raters, haunt existing rating services and are  major issues undermining their credibility.  These issues should be addressed urgently, as more and more people rely on rating services to make increasingly important decisions.  We are of the opinion that these issues have to be addressed by combining a computational approach with a human controlling effort in order to substantially improve the overall trustworthiness of rating services, and this is what Trustiser is all about.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Online Advertising and Trust

Online Advertising and Trust

A recent study, based on responses from 58,000 respondents in the US and 16,000 respondents in Europe, conducted by Forrester Research reveals interesting findings about the trust placed by consumers in various online advertising and promotion approaches (see figure below).  The study shows that 70% of consumers in the US (61% in Europe) trust brand or product recommendations from friends and family. While 55% of US consumers (33% in Europe) trust professionally written online reviews and 46%  of US consumers (38% in Europe) trust consumer written online reviews.


As outlined in a previous post in this blog, this level of trust in friends and online opinions doesn’t come as a surprise.  It is the logical result of the good reputation that any person tends to have among his friends and the natural trust that online consumers place in authoritative reviewers and aggregated ratings (the so-called wisdom of crowds).  Both friends and online authoritative and aggregated opinions can be viewed as reliable sources.  The recommendations from reliable sources are of primary importance, they help human brain in making decisions very quickly because they have a big impact on trust inference.

That is the reason why we do think that the digital world should move towards the establishment of topic-related, experience and/or expertise-oriented, trust-based hierarchies of reviewers.  Those hierarchies will significantly reinforce, for the better, consumers’ reliance on online reviews and recommendations.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

The 10 Commandments of Digital Exodus

The 10 Commandments of Digital Exodus

A socio-cultural revolution, unprecedented in human history, is well underway, thanks to the acceleration of the exodus of human activities (trade, finance, education, entertainment, etc.) to the digital world.  It’s the Digital Exodus.

In this post, we would like to share with you what we think are the 10 commandments that govern the Digital Exodus.

  1. You should not steal others’ identities or use pseudonyms; you should instead reveal your real identity  in order to foster trust because trust is the foundation upon which human activities are based.
  2. You should not worship your digital ego, otherwise you will run the risk of being drowned in a narcissistic whirlpool.
  3. You should not say or write something that you are unable to justify, or at least put in context, the rest of your life because the digital world has a memory that lasts forever and remembers everything.
  4. You should not forget that you are outfitted with a virtual megaphone and that the opinions you express can be potentially heard by every person on the planet and, for better or for worse, ignite reactions.
  5. You should not overlook your digital reputation; you should strive to have the best reputation in every digital arena you go to.
  6. You should not ignore the wisdom of crowds and you should, at the same time, avoid the herd mentality.
  7. You should not be solitary; you should collaborate because collaboration is the essence of the digital world with countless participants involved in creating new knowledge and enhancing the intelligence of both individuals and groups.
  8. You should not hide your intellectual property, unless it represents a major breakthrough leading to revolutionary transformations; you should instead share your ideas, designs, and developments and let the digital world embrace and promote your creativity.  It is the best way to achieve whatever you are aiming for.
  9. You should not seek flash (immediate) monetization; you should instead contribute in an outstanding way to create a strong, competence-based reputation, gain recognition, and then capitalize on your reputation.
  10. You should not miss the revolving wave of the evanescent present so that your contributions to the digital world remain relevant to the dynamics of the “global brain”.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Can We Trust the Crowd Miners?

Can We Trust the Crowd Miners

The digital world is caught in a data deluge, caused to a large extent by the huge collection of actions, ratings, recommendations, opinions, and mere information (in the form of text, audio, or video) generated every day by the citizens of the digital world.  This phenomenon has not gone unnoticed by the research and commercial communities.  As a result, many companies and universities have invested heavily in developing various data mining techniques to harness the exaflood of data generated by the data deluge and discover valuable knowledge and relevant patterns.  

Of particular interest is crowd mining, where gigantic databases of social information are mined to extract useful knowledge.  One example is dishtip, a service offered by TipSense.  TipSense devised a data mining algorithm which is able to reveal best dishes at restaurants by crunching millions of reviews, mentions, and photos of food.

Crowd mining looks very promising but the data extracted from social databases convey malicious content, such as fake ratings and recommendations, that can corrupt the results of crowd mining tools.  In this context, several approaches have been developed to fight malicious content by cleaning the data.  In the realm of rating services, several universities (e.g. Cornell University) and companies (e.g. Google) are working hard to detect fake ratings. 
However, we do believe that fake rating detection algorithms are necessary but not sufficient to deliver high quality data to crowd mining tools.  Indeed, all ratings are not equal, that is the reason why each rating has to be weighted by the trust placed in the user who performed the rating.  In this context, Trustiser will push the envelope by providing crowd mining engines with reliable ratings generated by a community of members arranged hierarchically; the basis of the hierarchy is the trust placed in raters in relation to each topic.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Service Marketplaces and Trust

Service marketplaces and trust

As outlined by Charles Petrie, the world is inevitably heading towards a “sea of  services” because there are powerful market forces for distributing work through technology-mediated service marketplaces: cutting costs while improving productivity, flexibility, and even capabilities.  Self-employment will predominate and people will assemble their collective skills and current tasks to form “flash companies”.  Auctions will be the basic pricing model.  Contracts will be simple.

Trust and Reputation management will be at the core of this revolution.  Indeed, topic-related, competence-based trust and reputation management will allow service seekers (be they individuals, groups, or businesses)  to strike deals directly with reliable, skilled service providers.  It will also make it easier for service providers (be they individuals, groups, or businesses)  to offer service seekers proven, experience or expertise-based services.

Already, existing service marketplaces rest on some basic, yet essential trust and reputation management features.  For example,  paid Q & A service relies on professionals who have been selected with competence-based trust in mind and verified by leading third-party vendors.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Humans, Trust and Reputation

Humans trust and reputation

In order to manage efficiently trust and reputation among humans in the digital world, two important dimensions should be taken into account.

The first dimension is related to the organization of human societies. Human societies are organized hierarchically, and the basis of the hierarchy is trust. Indeed, the trust placed in an individual with regard to a given topic determines his position in the hierarchy related to the topic. The level of trust, and the reputation it creates, depends on the sincerity and experience or expertise exhibited by the individual in relation to the topic. Therefore, trust and reputation management in the digital world should also be hierarchically organized, topic-related and experience or expertise-linked.

The second dimension revolves around the importance of ratings for humans. Indeed, for the human brain, ratings, recommendations, and opinions are much more useful than mere information because ratings and opinions, especially ratings and opinions from reliable sources such as renowned experts, knowledgeable people, and close friends, have a big impact on trust inference. Trust inference is of primary importance because it helps the human brain in making decisions very quickly. In this respect, we would like to quote the columnist Charles McCabe: “Any clod can have the facts, but having opinions is an art”. Therefore, trust and reputation management in the digital world has to adopt, whenever possible, rating-centric and opinion-driven approaches.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Rating the Raters

Rating the Raters

Rating services are playing an increasingly important role in the digital world as more and more individuals rely on these services to plan for the future, that is make critical or life-style-driven decisions, solve problems or discover opportunities.  In this context, the success of rating services will depend to a large extent on their ability to establish a trusted relationship with their users.

As a matter of fact, existing rating services generate a great deal of distrust among their users because they are fed by humans who all too often are able to discover and use the flaws in the algorithms that underpin current rating services.  In addition, current rating services place the same amount of trust in all users, regardless of their sincerity and the level of their experience or expertise-linked knowledge.  On top of that, businesses are devising strategies of all sorts to exert an influence on their customers’ ratings and reviews. Some businesses have gone so far as to pay targeted customers to get positive ratings and reviews.

Basically, there are two major issues that need to be addressed:
– Existing rating services are not able to defend themselves against individual and collaborative fake ratings and reviews, and thus are subject to manipulation.
– There is a lack of models to manage efficiently the trust placed in each rater.

Let’s take a closer look at each of these issues.

The inability to counter fake ratings

According to Bing Liu, a computer science professor at the University of Illinois at Chicago and a leading researcher in the area of fake online ratings, “for some products, up to 30 percent of ratings can be fake”.  Moreover, according to Gartner, by 2014, 10 to 15% of online ratings will be fake and paid for by companies. Fake ratings and reviews, a very cheap way of marketing, can be either excessively negative, bashing competitors’ products, services or brands, or overly positive,  raving about the products, services or brands of specific businesses.
Fake ratings and reviews can be published directly by businesses or posted by customers who receive perks for their contributions. Fake ratings and reviews can also be obtained by relying on professional favorable ratings providers.

A lot of effort and money are spent in research and development to rise to the challenge and devise mathematical models that detect and combat fake ratings and reviews.  For example, a team of researchers from the University of Rhode Island is underway with a project to develop algorithms that can serve as a defense against collaborative, profit-driven manipulations of online rating services.

The lack of models to manage trust placed in raters

The raters space is completely “flat” in existing rating services, and there is no hierarchy in terms of competence, that is experience or expertise-linked knowledge.  Moreover, current rating services are not intrinsically organized to attract competent raters.  As a result, everyone can rate anything in any domain.  Although some rating services include features aimed at tracking raters’ activities and giving a competence-based weight to each rating, we are far from addressing this issue adequately and effectively.

At this point, it is interesting to draw a parallel with Google’s search engine.  Considering that the web is a graph of documents, the power of Google’s search engine algorithms revolves around assigning a reputation index (a Page Rank) to each document and classifying the documents by their reputation inside the graph.  These algorithms not only take into account  the number of documents linking to a given document, but they also consider that a document partially inherits the reputation assigned to the documents that point to it.

If we consider the analogy with the graph of documents, rating services should include a graph of raters and implement algorithms similar to Google’s Page Rank algorithms.  But assigning a reputation index to each rater in the graph of raters through incoming reference count would not be sufficient to estimate the real reputation of a rater because:
– The graph of raters is more complex.  Reference counting alone is not sufficient to express this complexity.  The type of relationship a rater has with other users and the level of influence he exerts within the community of users should also be taken into account.
– The reputation index shouldn’t be considered as a single scalar number because when we look at the graph of raters from the angle of knowledge, the graph lives in multi-dimensional space, each domain of knowledge should be considered as a dimension, and reputation index should take the form of a vector.

In summary, although there is a great deal of effort to make rating services more reliable, the accomplishment of this goal is a long way off.
We do believe that a pure algorithmic approach is not sufficient to fix all these problems.  There is a need to combine a computational approach with a human controlling effort in order to substantially improve the overall trustworthiness of rating services.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

On Social Influence in the Digital World

On Social influence in the digital world

Social influence in the digital world is a very hot topic nowadays, it describes an individual’s ability to affect other people’s thinking and actions in online social communities.

Social Influence is intimately connected to trust and reputation.  Indeed, top influencers in a given topic tend to be trusted people that enjoy a good reputation with regard to the topic.

Identifying top influencers in the digital world creates tremendous opportunities for individuals, including influencers, and businesses.  Individuals can find competent, trusted advisors to assist them in planning for the future, i.e. make critical or lifestyle-driven decisions, solve problems or discover otherwise unseen opportunities.  Businesses can target socially influential individuals and hire them as advocates to promote their brands, products, and services.  In addition, top influencers can monetize their expertise and/or experience by offering, for both individuals and businesses, competence-based services.

Although social influence in the digital world can be easily defined, Its formalization through a computational model is much more difficult.  Several startup companies, such as Klout, PeerIndex and others, have attempted to take up the challenge.  They devised algorithms aimed at expressing social influence.  The algorithms use data generated by users through their activities and contributions within existing online social networks (Twitter, Facebook, LinkedIn, etc.) to calculate, in particular, an aggregate estimate of social influence (a number between 1 and 100).
These companies claim that this number, called a score, reflects:
– The number of people under an individual’s influence.
– How much an individual influences people.
– The influence of the individual’s network.

However, several questions have arisen:
– What is the exact meaning of this number?
– Does this number represent an actionable knowledge?
– How reliable is this number, given that it is based on data from non qualified, non hierarchical social networks (Twitter, Facebook, LinkedIn, etc.)?

On top of that, when the models devised by Klout, PeerIndex and others were tested in the real world, major weaknesses were found:
– It’s quite easy to create “fake” individuals with high social influence scores
– It’s quite easy to manipulate and boost an individual social influence score with a set of simple tips

The bottom line is that current algorithms that measure social influence by using data from non qualified, non hierarchical social networks have major loopholes that allow individuals to create, quite easily, a phony good reputation.

From our point of view, social influence measurement has to move towards using, primarily, data from qualified, hierarchical social networks.  The qualification and hierarchies need to be topic-related, competence-linked and trust-based.  In this way, social influence measurement will allow individuals to rely on skilled, trusted advisors and will provide businesses with qualified, fine grain-selected, topic-related marketing targets.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

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