How to Avoid and Minimize Fake Social Media Reviews

October 22, 2012

Introduction

I was surprised to come across a press release from the research firm Gartner, which stated that “by 2014, 10-15 Percent of Social Media Reviews to Be Fake, Paid for By Companies.” As someone who relies on reviews to make purchasing decisions (e.g. on Amazon, Yelp, TripAdvisor and many other sites), this concerns me.

For actions such as views, Likes and followers, the “cost” (overhead) is low, while the action can be performed somewhat anonymously. A review, on the other hand, requires more “work,” and is often associated with some sort of identity (profile) of the reviewer.

In the press release, Gartner indicated that companies will emerge to assist brands: “Gartner analysts said they expect a similar market of companies to emerge specializing in reputation defense versus reputation creation.”

I have a better solution – and that’s to “attack” the root of the problem, which is the review site itself. Thankfully, many review sites are already structured to separate the quality reviews from the fake reviews.

Let’s look at some examples and consider some related ideas.

Review the Reviews.

“Meta,” according to Wikipedia, is “a concept which is an abstraction from another concept, used to complete or add to the latter.” To determine the worthiness of reviews, there’s nothing bet-ah (better) than meta (bad pun).

Let’s consider the reviews on Amazon. First, notice that the heading is “Most Helpful Customer Reviews.” Amazon allows users to indicate whether a review was helpful and then sorts their reviews list in order of “highest number of helpful review ratings” first.

The “Most Recent” reviews are listed off to the right column, in less prominent real estate. Also note that the reviewer is an “Amazon Verified Purchase,” which means that he purchased the book on Amazon.

Granted, one can still manipulate the system, as the New York Times detailed in a piece titled “The Best Book Reviews Money Can Buy.” But the Amazon system is effective because it relies on its users to tell us which reviews have been helpful. It also means that to display the “verified purchase” label, a fake reviewer would need to purchase the book on Amazon.

Establish “On-Site” Reputation.

In the Amazon example, the helpful reviews rose to the top, while the “non-helpful” reviews remained at the bottom. In this way, the Amazon reviews are similar to search engines, as few people click past Page 1 of search results pages (and the cream rises to the top).

In addition to rating the reviews, sites could establish reputation ratings for end users. eBay has been an innovator on this front, with their Feedback ratings. If you’ve ever purchased something on eBay, you probably viewed the seller’s ratings and read through comments (on that seller) left by other users.

Of course, an online review is a much different than an online purchase. Reviews won’t garner as much feedback as transactions. But the concept remains: allow users to establish reputation on the site, which will influence other users’ judgment on the published reviews.

Amazon, in fact, has a program called “Hall of Fame Reviewers” and Yelp has a program called the Yelp Elite Squad. Reviews that prominently display these sorts of reputation “achievements” (next to the reviewer) emphasize the “high reputation users” over those who may have ulterior motives (i.e. fake reviews).

Integrate Third Party Reputation Data.

Services such as Klout, Kred and PeerIndex aggregate public data (about you) to calculate online reputation scores. While not quite as useful as “on-site” reputation, linking reviewers to an online influence profile could help ward off fake reviews.

Influence equals credibility. And in considering whether a review is bonafide, I’d take an online influence score over nothing (i.e. an anonymous profile).

Deeper integrations between review sites and online influence services could tie “review topics” (e.g. books on Finance) to “influence topics” (e.g. Finance).

So, for instance, a review of a Finance book could link to the reviewer’s “Finance topic” page on the online influence site. Users reading the review could then determine how much weight to place on that particular review.

Integrate Third Party Social Identities.

Blogs and web sites use services such as Livefrye to conveniently integrate social identities (e.g. Twitter, Facebook, LinkedIn) to web site and blog comments. Tying reviews to a social identity is far better than anonymous reviews. At minimum, the reader can visit the social profile of reviewers to make a judgment on their worthiness.

Conclusion

Online reviews play an enormous role in worldwide purchasing decisions. As with any data source, effectiveness is closely tied to credibility.

If 10-15% of social media reviews are fake, then credibility suffers. And when that happens, people will look for other means of purchasing decision research. As such, web sites that provide reviews should look to successful examples from Amazon, Yelp, eBay and others to help avoid and minimize fake reviews.

Note: I invite you to connect with me on .

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Book Review: Viral Loop

February 12, 2011

Image courtesy of Amazon.com

Introduction

Adam L. Penenberg’s “Viral Loop” was published in 2009, but retains a lot of relevance in 2011.  Its subtitle is “From Facebook To Twitter,  How Today’s Smartest Businesses Grow Themselves”.  The book begins by telling the story of the web site “Hot or Not” and how that web site (in 2000) “rode a simple idea to a fortune”, by virtue of “an insanely viral scheme”.

It goes further back in time to the original viral models, Tupperware and Ponzi Schemes and then works its way up through many of the present day (or past-present day) Web 2.0 success stories (e.g. Mosaic, Netscape, Ning, Hotmail, eBay, PayPal and more).

The Viral Coefficient

Early in the book, Penenberg explains the “viral coefficient”, or the “number of additional members each person brings in” (to a web site or service).  The success of a web site, or even a YouTube video, “going viral” hinges on this figure.  Penenberg explains that if the coefficient is equal to 1, the site “will grow, but at a linear rate, eventually topping out”.  Then, “above 1, it achieves exponential growth”.

The early growth of Ning was due to the fact that its viral coefficient was 2.0 – “each person who signs up is worth, on average, two people (compounded daily)”.  And while Ning doesn’t attain the lofty position it once had, its viral coefficient (and how it achieved it) is important in understanding its early success.

Web 2.0 History Lessons

Viral Loop Cover with Social Media icons

Image courtesy of ViralLoop.com

Netscape

In addition to explaining viral coefficients and how viral loops are created, Penenberg provides interesting history lessons (stories) behind some of the web’s most well known creations.  He tells the story of how Marc Andreessen created the Mosaic browser at the University of Illinois in Urbana-Champaign, left Illinois to move west to Silicon Valley, and there co-founded Netscape with Jim Clark.  Version 1.0 of the Netscape browser was released on December 15, 1994, and Netscape engineers “rigged servers so a cannon fired every time a browser was downloaded”.

eBay

In a chapter titled “eBay and the Viral Growth Conundrum”, Penenberg tells the story of Pierre Omidyar, whose inspiration for eBay came from a stock order gone bad – he placed a pre-IPO order for a stock, only to see it jump 50% on the day of its IPO. Omidyar, whose business was called AuctionWeb, hosted the site on eBay.com because his desired domain, Echobay.com, was already taken.

PayPal

The chapter “PayPal: The First Stackable Network”, takes us through the very genesis of PayPal, starting with a lecture at Stanford, given by Peter Thiel. Max Levchin was one of six in the audience. They agreed to meet for breakfast the next week and over breakfast, agreed to launch a start-up around Levchin’s ideas for cryptography software.  The initial company was called Fieldlink and went through a few iterations of cryptography business ideas until they settled on the idea that would become PayPal.

Epilogue

Before reaching the Epilogue, we learn about the beginnings of several other well-known names, including Flickr, MySpace, Bebo and Facebook.  In the Epilogue, Penenberg summarizes the characteristics of viral loop companies and compares the similarities to human population growth – “the human population growth  rate [also] mirrors the curves for companies like Skype, Hotmail, Ning, Facebook”.

Penenberg’s book makes me ponder the coming decade (2011-2020). What new viral loop companies will be created (and how) – and who will be this decade’s Hot or Not, Ning and Netscape?


Gamification Predictions for 2011

December 22, 2010

Introduction

At Mashable, Gabe Zichermann (@gzicherm) provided his 5 Predictions for Game Mechanics in 2011.  Gabe’s article inspired me to provide my own predictions.

A New Name in 2011

In the second half of 2010, the term “gamification” became bi-polar: you either loved it or hated it.  People on the “love” side see it as the future of engagement and marketing.  People on the “hate” side see it as a gimmick.

Gabe provides his thoughts in an article at Huffington Post.  While the term is effective in capturing the essence, it’s not perfect.  As a result, “gamification” will be used less and less in 2011.  In its place will be a set of new terms, based on its specific applications (e.g. game-based marketing, game-based social initiatives, etc.).

A Sub-Industry Develops


This is more an observation, rather than a prediction (since it’s already happening): an industry has developed around “gamification”.  When folks convene for a conference or summit, that’s my measuring stick to tell me that an industry is emerging.  In the virtual events space, that happened in 2009 with the Virtual Edge Summit (which, by the way, has its third annual conference, also in January 2011).

If you look at the sponsor and speaker lists for this event, you’ll see a number of start-ups who built their business around gamification.  In 2011, we’ll see some “bubble like” behavior (perhaps we’re already seeing it now), where entrepreneurs look to build the next great gamification companies.  In the second half of 2011, however, the bubble settles and the early winners emerge.

Related: Gamification gets its own conference (VentureBeat)

Game Mechanics for The Greater Good

Jane McGonigal of Palo Alto-based Institute for the Future once said, “Any time I consider a new project, I ask myself, is this pushing the state of gaming toward Nobel Prizes? If it’s not, then it’s not doing anything important enough to spend my time.” (source: Salon.com article from 2007).

In 2011, we’ll see game mechanics applied increasingly to the “greater good” – initiatives that can change the world.

Armchair Revolutionary is a great example – consider one of their slogans, “shape the future by playing a game”.  In 2011, lots of “revolutionaries” emerge to rally those who can, to provide help to those in need.

Game Mechanics Go Mainstream – But Consumers Don’t Know It

Game mechanics are going mainstream, but the typical user won’t know that they’re participating in them.  They simply know that they’re engaging in enjoyable activities (side note: there will be similar growth in Foursquare, Gowalla, etc., but users, of course, won’t know that they’re using “location based services”).

For example, Universal Studios announced successful sales of their “Despicable Me” DVD – their press release attributes some of the success to a “Minions Madness” promotion, “a points-based reward and social media program spotlighting the film’s beloved mischief-makers, the Minions.” This promotion was powered by Bunchball, a game mechanics start-up.

Bunchball (and related companies) has built a nice client list of broadcast networks, cable networks and film studios.  In 2011, additional media outlets come on board.  Game mechanics  go more and more mainstream, even though the typical mainstream user doesn’t know it.  Watch out in 2012, however, as consumer-based game mechanics suffer some fatigue (as consumers then see “much too much” of it).

Established Web Players Incorporate Game Mechanics

2011 sees established players incorporate game mechanics to increase engagement (e.g. “time on site”, clicks, e-commerce sales, etc.).

Google adopts game mechanics as a means for bridging their search business and social services (e.g. adding game mechanics to Google Me). Others who add game mechanics include Netflix, eBay and Groupon.  Of course, it’s natural to expect that more and more virtual event experiences will add game mechanics, too.

Conclusion

2010 has been an interesting year for gamification. 2011 will kick off with an industry event and where we go from there will be exciting to watch.  I’ll check back mid-year with a report card on these predictions. Here’s hoping I attain the “crystal ball badge”.


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