All About Trust and Reputation in the Digital World

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)

Collaborative Consumption and Trust

Collaborative Consumption

A major trend in the digital world is collaborative consumption where consumers use online collaborative services to rent, share and trade goods and services.  Moreover, consumers can use online collaborative services to borrow money through peer to peer lending or raise capital through crowd funding.

Examples of online collaborative services include Groupon (online collaborative purchase), Airbnb (online collaborative travel), Getaround (online collaborative car sharing), and Kickstarter (online collaborative funding).

In this context, trust and reputation management plays a critical role in enabling new economic models that underpin collaborative consumption.

Indeed, ensuring that a renter is not going to trash an apartment or a car in the context of an online peer to peer rental service and guaranteeing  that a lender is not going to lend “dirty” money coming from criminal activities in the context of an online peer to peer lending service are paramount issues.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

Online Reviews and Trust

Online reviews and trust

A survey conducted in 2011 by Nielsen shows very interesting findings regarding trust and reputation in the digital world.  The survey was conducted between August 31 and September 16, 2011 and polled more than 28,000 online consumers in 56 countries throughout Asia Pacific, Europe, Latin America, the Middle East, Africa and North America.  The results of the study were released in April 2012.

The survey reveals that online consumer reviews are the second most trusted form of advertising with 70% of global consumers surveyed online indicating they trust them, an increase of 15% in 4 years.  While 92% of consumers around the world say they trust earned media, such as word-of-mouth and recommendations from friends and family, above all other forms of advertising.

Overall, the survey shows that consumers around the world continue to see recommendations from friends and online consumer opinions as by far the most credible.

This level of trust in friends and online consumer 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.  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.

In this context, we do strongly believe that the digital world should move towards the establishment of formalized hierarchies of reviewers that are topic-related, experience and/or expertise-oriented and trust-based.  Those hierarchies will significantly reinforce, for the better, online users’ reliance on online reviews and recommendations.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

The Raison d’Etre of this Blog

The raison d'etre of this blog

This blog is devoted to trust and reputation in the digital world.  As we spend a bigger and bigger chunk of our lives in the digital world, working, socializing, buying, playing and training, trust and reputation among the participants, be they human beings or computer-based entities, have become paramount issues.  Especially when the participants do not have sufficient knowledge about each other.

Indeed, in this technology-mediated environment, the reputation of a participant has a big impact on trust inference among other participants, and that plays a crucial role in making decisions as to whether or not to engage in collaboration through interactions, activities, or transactions.  As collaboration has become the essence of the digital world and the driving force behind the emergence of technology-mediated social intelligence (think of Google Apps for productivity tools, Facebook and Twitter for social networking, and YouTube for video sharing) it is of primary importance to develop and refine comprehensive and efficient trust and reputation management models.

Trust and reputation are intertwined and complementary in the sense that trust is a continuous measurement influenced by, among other factors, reputation (whether good, bad or nonexistent),  while reputation (or social image) is a status earned, for a certain period of time, through earlier collaborative engagements.  Of course, trust and reputation have to be contextualized.  In other words, they are linked to an environment, an application, a community, a topic, etc.

Trust and reputation management touches on a wide range of disciplines, including social sciences, network science, information systems, data management, artificial intelligence, and game theory.

This blog will provide, on a regular basis, thoughts, analysis, and studies’ findings about trust and reputation in the digital world.  All feedback are welcome.  The intent is to provide a foundation for a discussion on trust and reputation in the ever growing digital world.

We look forward to hearing from you.

Rafik Hanibeche & Adel Amri (Trustiser Founders)

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