Facebook, it is argued, is eating the media. With the launch of autoplaying native video and lightning fast-loading Instant Articles, Facebook isn’t just letting publishers share their content with readers – it is hosting the content itself, and in doing so, shaping how audiences experience that content.

The trouble for the media, it seems, is that once Facebook controls both publishing and distribution, it will have even more power to control publishers’ traffic and access to audience data – and therefore their monetization as well. But might this be a power grab that serves users and saves publishers from themselves?

The dangers of depending on Facebook

Publishers find themselves in a tricky spot. None of them set out to become beholden to Facebook. Most fell into depending on Facebook passively because their readers were sharing on Facebook without prompting. Once they realized the incredible ability of the platform to drive traffic, they started engaging more deliberately. Even BuzzFeed, who built their audience and monetization model on Facebook sharing (it accounts for over 50% of their traffic), did so because Facebook was where the audience was, not because they wanted to depend on the platform (CEO Jonah Peretti’s famous ‘distributed’ content strategy).

And publishers are right to be wary of depending on Facebook. Their brand partners provide a cautionary tale: they were encouraged to build large followings on Facebook, only later find they needed to pay Facebook in order to reach a meaningful number of those fans. Zynga faced a similar fate when it found overnight that the in-app purchases for its Facebook-hosted games would have to be made via Facebook ‘credits’ – a service that Facebook would charge 30% for. In both cases it’s clear that Facebook gained from a change that arguably had little impact on users (in the case of brands it was arguably negative – cutting users off from content they’d signed up to see).

A user-centric power grab?

But, I would argue, this move by Facebook is not simply a nefarious grab for money and power. This is a push for a drastically better user experience. Money and power are simple a pleasant extra. With both Facebook Video and Instant Articles, Facebook has dragged publishers, kicking and screaming, toward providing users with a cleaner, faster browsing experience.

The problem for users was that publishers’ desire for ad revenue and user data had led them to overload their sites with megabytes of software that added nothing to the user experience, but added massively to loading times and data consumption. A New York Times report showed that over half the loading time of leading publishers’ mobile sites was driven by the need to load advertising tech and content. These delays meant that people browsing Facebook and then seeking to load articles or videos from the publisher sites were stuck staring at blank loading screens. This was a bad user experience that made browsing on Facebook less attractive, and therefore reduced the value of the users to Facebook.

Facebook’s grab cut through this. With Facebook hosted content, Articles would load instantly,  and videos would play automatically, as long as they were hosted by Facebook itself. And in the process, Facebook would control more of the user experience – making it cleaner, clearer and more uncluttered.  Facebook can then give the publishers data on users and share with them the revenue from any ads that run alongside the content.

Publishers saved from themselves

In other words, the power grab can been as Facebook saving publishers from themselves. It lets them get data about customers and get revenue from targeted ads with a vastly superior user experience.

The devil, of course is in the detail. Facebook knows that this user experience is compelling and has resulted in radically higher user engagement. (Publishers know this too – just as BuzzFeed or the Washington Post). And while commercial terms are no public, it seems safe to assume that Facebook is capturing a substantial portion of the additional value it creates.

There is no doubt that this move deepens the publishers’ dependence on Facebook. But it does so in service of their end users. More importantly, it is also an acknowledgement from Facebook that publishers are critically important to its ability to engage and retain users. Already Facebook has shown that it’s not immune to publisher pressure, with reports in November suggesting it is looking at changes to Instant Articles that will improve publishers’ revenue per article.

On top of that, having Facebook cajole publishers into Facebook-hosted content seems a significantly lesser evil than having Facebook launch its own content production in house. In that light, this represents Facebook insisting that both sides stick to their knitting: Facebook will focus on a compelling, user-friendly experience it can sell ads against, and publishers will focus on producing great content.

The price is eternal vigilance

This isn’t to say that publishers can rest easy under the benevolent gaze of Facebook. They will have to watch Facebook carefully for plans to adjust the terms of these programs. Continuing to maintain their own sales and ad tech capabilities will likely be necessary, to give credibility to a thread to pull out of the Facebook hosting ecosystem. However it seems that continuing to produce great content that people want to share on Facebook – thereby remaining important to Facebook – is the best insurance policy in an uncertain world.

By: Steve Hind


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A great business idea…

Earlier this year, GoButler was founded by three ex-Rocket Internet executives. Backed by the venture capital firm General Catalyst Partners as well Hollywood star Ashton Kutcher, the firm aims to revolutionize the way people “get whatever they are looking for”. On Twitter, GoButler claims to be “your 24/7 concierge, your personal assistant… for FREE!” They promise to get you whatever you want as long as it is legal – you only need to send them a request via SMS. Likewise, Facebook recently beta launched “M.”, a virtual assistant that operates via Facebook’s Messenger. Seems like a cool thing! No need to have millions of different apps anymore, such as one to reserve dinner tables, one to book flights, and one to order cloths. GoButler is your one-stop shop for everything and aims to become THE new way of search.

…that, however, does not seem to work quite perfectly yet

I recently tested the service four times – so far, I’m not very impressed. I asked them to organize a daytrip to the Niagara Falls for me and friend while we were in Toronto a couple of weeks ago. Failed: Despite many messages back and forward explicitly stating the pick-up and drop-off location and times, GoButler was not able to organize a trip that met our stated expectations. We ended up organizing the trip ourselves. I send three more travel and shopping related search requests. Either they were not able to find what I was really looking for or the price was much higher than the one I found myself. These four test runs illustrated that what ought to be simplifying my life in fact made it more complicated. Even if GoButler manages to improve their search results over time, do they really have a chance of being successful in the long run? I see two major challenges: (1) Scalability and (2) profitability.

Is this business scalable?

Currently, all SMS requests are handled by dozens of humans sitting in an office in New York City. Scalability of the business seems rather challenging. I’d argue, either a significant amount of automatization and artificial intelligence is required to handle the search requests, or a tremendous amount of people need to be hired. The former, technology-wise doesn’t seem perfectly feasible nowadays and the latter seems extremely expensive.

Can this business be profitable?

GoButler promises not charging customers any on-top fees for their concierge service. Instead, the firm aims to monetize indirectly through an affiliate model: Once they have established a large enough user base, they aim to approach partners such as delivery restaurants, doctors, airlines, and online shops to negotiate revenue share models for every generated order. I see multiple challenges related to this approach: 

First, it seems questionable whether a revenue share approach will result in the best experience for the customer. The pizza delivery company that pays the highest revenue share to GoButler may not necessarily deliver the most delicious Pizza, too. Yet, GoButler may be inclined to place orders through this very delivery service due to financial incentives.

Second, it is unclear whether GoButler’s customer leads indeed create any additional revenue for their affiliates. Would customers have placed the orders anyway? If there is no proof of incremental revenues, partners are likely to disregard the concierge service.

Third, related to scalability, it seems rather challenging to easily build up partnerships with so many goods and services providers in order to really make money off every type of order placed through GoButler.

Fourth, even if the three aforementioned concerns turn out not to be valid, I’d question whether the unit economics of this business can really work out profitably. Let’s run the numbers: In my test inquiries, the average processing time per request was approximately 10 minutes. Hence, I’d assume one employee can handle six customer requests per hour. Let’s conservatively assume further that he/she manages to convert each of the leads into an actual deal worth on average USD 15 (average price of a food order). Lastly, let’s assume GoButler’s affiliates are willing to share 10% of their revenues. This would result in a total revenue for GoButler of USD 9 per hour. Pretty slim – is this even enough to cover the employee’s salary? Not to mention, the chance that every search requests indeed gets converted seems rather unlikely – just remember that none of my four test runs were even close to be converted.

Time to shift gears

One thing seems to be clear: GoButler will need to burn a lot of the investors’ money to test the viability of its business model. With GoButler’s current monetization approach, it will require significant growth of the customer base before one can even assess whether or not GoButler will indeed be able to establish the necessary partnerships to generate revenues and eventually profits. Following Clayton Christensen’s advice – a management guru from Harvard Business School – who says one should be “patient for growth but impatient for profit”, I would recommend to shift gears a little bit: Instead of offering the service to any and every one free of charge, GoButler may target high value customers who see a clear benefit in this concierge service and are thus willing to pay a premium for it. Alexander Goerlach – visiting Scholar at Harvard who recently visited the NYC office of GoButler in his role as the U.S. representative of the German Federal Association of German Startups – says: “There are so many extremely busy people out there who need to get things done – GoButler seems like the perfect service for them. If they get on board, I can well imagine that Google will consider consolidation options with GoButler.”

I personally believe that the extremely busy people amongst us – who not have a real personal assistant – may rely on a concierge service such as GoButler for most of their searches one day. But nonetheless, I find it hard to imagine that one day search engines will be disregarded completely. Let’s stay tuned and see how GoButler’s and Facebook’s virtual assistants roll out over time.

By: Noshad Irshad

 


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Companies that build online marketplaces struggle to solve the dreaded chicken and egg problem—how do you get buyers without sellers and sellers without buyers onto the platform? Many of today’s familiar online marketplaces—like Uber, AirBnB, eBay, Xbox Live, PayPal, OpenTable, and Angie’s List—managed to crack this challenge and reach sustainable scales.  In addition to creating useful products for their users, most analysts would agree that these businesses also benefit greatly from positive network effects between their buyers and sellers. That is to say, each additional buyer or seller on the platform generally has a positive effect on his or her counterparty:

  • More sellers à more options for quality, price, and convenience for buyers
  • More buyers à more opportunities to transact for sellers

Many people are ready to give these companies the benefit of having solved for positive network effects on their platform. In fact, I too fell into this trap until Professor Gautam Ahuja of Ross School of Business (visiting at HBS last year) made me re-examine this belief. Rather, there exists an opportunity to more deeply understand these companies by exploring the network effects across all party interactions. We can gain a better idea of the costs involved in growing the business and what valuable follow-on products should be developed. The basis for this company analysis begins by understanding that multi-sided platforms must inevitably also have multiple network effects. To clarify what this means, it might be easier to first understand what network effects look like on a single-sided platform.

A Single-Sided Network Example: Early Facebook

When Facebook first came on the scene in the mid-2000s, it resembled a single-sided platform. In the time before marketers were peppering the site with ads—weren’t those the days!—users basically joined other users in a system where all users were alike. We can think about Facebook’s network effects by looking at what happened when a single new user joined (an increment of +1 new users).

The incremental user’s content provided other users with more to see and explore. This content came in the form of posts, photos, and music preferences and meshed seamlessly into other existing users’ experiences. An existing user now has more to explore and interact with on the site—like seeing pictures of the new user’s trip to Bali or discovering linked articles the new user shared. Existing users’ content might also get more interactions from the new user in the forms of more likes, comments, and tagged photos. These could all be described as positive effects as they generally enhanced the value for Facebook users.

However, the addition of a new user could make the Facebook experience turn negative as well. The new user might constantly post pictures of their cat and make it difficult for existing users to find the content they really wanted to see in their newsfeed. The new user could also be someone existing users didn’t want on the site, like a coworker or boss, which would make them less inclined to share photos or other content. Quite simply, the network effects induced by one new Facebook user could be positive or negative.

The insight into Facebook’s network effects helps us understand a lot about what followed in the company’s history. Because the effects were predominantly positive, people invited their friends and family to the site virally, reducing expensive marketing and growth costs. The site exploded and reached 1.49 billion monthly active users as of June 2015 and saw 1 billion different users log in on the sam day in August 2015. Facebook subsequently developed products like a filterable newsfeed (no more cat pictures from the new user) and privacy tools to reduce users’ pain points (only share photos with friends or make posts visible to friends “except XYZ” people) that resulted from its rapid growth and negative network effects. These developments could have been predicted by our simple analysis of identifying the network and its positive and negative attributes.

A Multi-Sided Network Example: AirBnB

When thinking about multi-sided networks, the model for analyzing network effects grows more complex. Rather than thinking about how an incremental user will affect the entire network, we should scrutinize who the incremental user is and what network is being affected.

To illustrate this more clearly, let’s consider a two-sided platform like AirBnB. When a new user joins the AirBnB site, we should first consider whether the user is a guest or host. Next, we need to explore how that additional guest or host affects other guests and hosts. For this two-sided network example, there exist 4 possible network effect scenarios:

  • How does an incremental guest affect hosts?
  • How does an incremental guest affect other guests?
  • How does an incremental host affect guests?
  • How does an incremental host affect other hosts?

Understanding network effects can quickly get complicated when dealing with multi-sided platform. The number of unique network effects necessary to consider is n2 for each distinct n type of parties on the platform. As we saw with Facebook, network effects can be either positive or negative, complicating our understanding of two-sided platforms even more. When considering positive or negative effects, the interactions that should be examined are 2n2.

Below I’ve illustrated how AirBnB might experience positive and negative network effects across all 4 of its network change scenarios:

AirBnB Network Effects Diagram

 

An Updated Model for Thinking About Network Effects: Uber

The AirBnB analysis is a useful starting point, but I find it easier to simplify each of the distinct networks into a more manageable characterization. This reduction allows us to quickly understand the dynamics in the networks of a company while maintaining an explainable simplicity. I therefore classify interactions into one of three buckets.

  • Collaborators (Positive)—Parties predominantly enhance the experience of other parties in the network. Examples of collaborators include funders on Kickstarter who together to support an idea or product or gamers on xBox live who play together in multiplayer Halo battles.  Such relationships encourage users to invite other users to a site, and can lead to organic site growth and lower user acquisition costs.
  • Counterparties (Positive)—Parties are involved predominantly in a monetary transaction or exchange that satisfies both sides. Examples buyers and sellers transacting for a deal on Groupon or a mother ordering food via delivery service Sprig. Parties exchange clearly identifiable goods and services, which, when priced at a point such that the transaction clears, creates value for both parties that supports repeat usage and high user lifetime value from multiple transactions.
  • Competitors (Negative)—Parties predominantly compete for resources or opportunities. Examples include applicants applying to Y Combinator where only a select number of applicants are accepted or eBay bidders competing against each other for a baseball autographed by Mickey Mantle. In both cases, competition is likely to give users a worse user experience as they might not secure the opportunity or good or end up paying a higher price. This can result in a lowered user experience, unsubscribing, and high sign-up or reactivation expenses.

By using this framework, it helps me understand the operational costs a company is likely facing and what products they might consider developing in the future. For example, if this analysis were applied to ride-sharing start-up Uber, it might look like this:

Uber Network Effect Diagram

For Uber’s two-sided platform, a large part of the company’s value comes from solving the most obvious network dynamic: matching drivers with riders and riders with drivers. The company was able gain a foothold in markets like San Francisco because cab companies were not keeping pace to satisfy this counterparty need. As more drivers joined Uber, riders benefitted with greater ride availability and more riding options (uberPOOL, uberX, uberXL, uberTAXI, UberBLACK, uberSUV, uberSELECT, uberPOP, uberBIKE, etc.). As more riders sign up, drivers are more likely to match with a pick-up request and earn money for their services. These services led to increased usage by both riders and drivers as value was realized.

However, this dynamic doesn’t necessarily lend itself to growth. Drivers aren’t actively inviting or converting new riders, and new riders aren’t energetically recruiting new drivers. The first time they usually encounter each other is during an Uber ride, at which point they’re both already on Uber’s platform. While a positive experience for each party—a clean, convenient ride for the passenger and a profitable transaction for the driver—will influence who how often the other party uses Uber in the future, they’re not actively growing the platform.

The story gets more interesting when you look at the network dynamic across the rider <-> rider and driver <-> driver dimensions. For current riders, each additional rider chiefly means increased competition for resources. In Uber’s case, this manifests itself in surge pricing when many people try and use the app at the same time—such as during a rainstorm or Friday rush hour. The experience is painful, and users are upset by either the wait time to find a rider or the total cost for the trip. Similarly, as more drivers join the platform, existing drivers face both increased competition for riders and reduced chances for earning surge prices. If driver’s aren’t able to find riders and drive around unoccupied, this cuts into the driver’s ability to earn for time worked.

By understanding the interplays occurring across its networks, it’s easier to identify and appreciate Uber’s growth, marketing, and development challenges over the past few years. While Uber has benefitted from word-of-mouth marketing for its remarkable service, much of Uber’s recent growth depends on promotions and discounts rather than virality. Because riders and drivers aren’t actively working to sign up other drivers and riders without an incentive, Uber bears the burden of these growth costs itself.

For example, Uber offers money to users (riders and drivers) who sign up new riders and use a unique promotion code (mine is below—feel free to join using it!). Additionally, Uber will give new riders one or more free rides upon joining. To sign up new drivers, Uber offers drivers a $500 bonus after completing their 20th rider, $500 dollars for signing up a Lyft driver, and fixed hourly income guarantees to ensure new drivers realize monetary gain immediately—with promotions often varying city by city. These acquisition costs can be large for a company looking to scale globally and help explain why Uber has raised massive amounts of cash to grow operations in areas like China, India, and other parts of Asia. In addition to building and operations costs, a lot of that money will likely go to signing up riders and drivers through aggressive promotions and discounts and launching citywide marketing campaigns. Given Uber rider’s lifetime value from its positive counterparty interactions, such costs can likely be easily justified.

Uber Promotion Code

On the product development side of the business, Uber’s network effects can explain a lot of what the company has focused on building. Paramount to the experience is maintaining a positive rider <-> driver and driver <-> dynamic. Anything that facilitates a quality service has taken precedent in the pipeline to protect the company’s advantages and keep users using the app. Such developments include credit card scanning for easier payment, license plate information to help riders identify drivers, written explanations for 3 star or below reviews to protect drivers’ reputations, and optimal route maps to make sure the most cost-effective route is taken.

Uber has worked to tackle negative network effects inherent to its business as well. Uber launched a fare split feature that aims to make the riding experience more collaborative and less competitive, easily allowing users pay each other and receive ride receipts. Additionally, a feature was added to surge that allows users to be notified once surge has dropped below as certain level, decreasing the pain from increased competition over resources. Finally, uberPOOL matches different rider pairs so that a each group receives a lower fare for carpooling with the other party. These features all subtly aim to shift the experience from competitive to collaborative.

Looking Ahead

In this post, I hope that I’ve helped lay out a new model to help understand network effects for multi-sided online marketplaces. By identifying all the distinct networks that exist and then understanding the interplay of people in those networks (collaborators, counterparties, competitors), one can develop a valuable tool for understanding much about an online business. This insight can be used to analyze a company’s growth and marketing costs—will users sign other users up or do they need to deploy resources? It can also give vision into a business’s product development priorities—what networks need to be protected with better products and which networks need pain points addressed?

 By: Ry Sullivan


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The Pitch

The expansion of social media has had a massive impact on the world of sports. Instead of relying on national news websites to obtain information regarding sports teams and athletes, people are now able to login to their respective Facebook and Twitter accounts to get updates ranging from real-time game progress, to an athlete’s favorite movie. Social media has created a transparent system that allows fans to enjoy amplified engagement in the sports world; athletes to freely engage with their loyal fan-base; and athletic organizations to capitalize on essentially free marketing to reach a broader audience. But does all this have long-term potential?

The Swing

Fans are able to fully interact with the sports world through social media platforms by expressing their opinions directly to an athletic organization, or athlete, as the game happens in real time. Studies show that a majority of sports fans are on social networks while watching games, so they can weigh-in on the action. So if a fan likes or dislikes a certain play, he has the option to immediately inform the athlete or organization directly. This transparent interaction allows the fan to feel more connected to the athlete, the game, and the sport in general.

After a game, an athlete will be able to login to his Facebook or Twitter account to respond to fans, fostering a more “meaningful” athlete-fan relationship. Although some athletes might do this out of the “kindness of their hearts”, it seems more logical to assume that they do this for good PR. Whatever the incentive is, social media provides a channel for fans and athletes to share a deeper connection. In some cases, this could pay off for athletes: Tim Tebow gained popularity with the “Tebowing” meme; Jeremy Lin rode the “Linsanity” waive as long as he could; and Nick Swisher even earned a spot in an MLB All-Star Game due to support from his loyal fan base.

The Drive

Even entire athletic administrations are now capitalizing on an avenue of free marketing by using social media platforms to disseminate athletic progress. Fans simply follow the organization’s respective Twitter account to receive real-time information on individual athletes, and the team as a whole. The popularity of social media has encroached so deeply into the sports world that we are now seeing professional lacrosse players sporting their Twitter usernames on the backs of their jerseys, instead of their last names. Mississippi State even decided to repaint their football end zone to #HAILSTATE. Given that social media platforms create an avenue for free marketing, good PR, and facilitate deep connections between fans, organizations, and athletes, it might seem as if social media hit a homerun in the sports world – what more could you ask for?

The Catch

Although it might seem that the impact of social media on sports seems to only yield positive externalities, it does have vital downfalls that could cause the system to collapse. If used ignorantly, social media could be detrimental to some athletes. Social media platforms give athletes the ability to disseminate uncensored information directly to the public by allowing them to express any opinion, at any given point in time, on any subject – as an athlete myself, it is not hard to imagine that other athletes might say dumb sh*t from time to time. In fact, it actually happens a lot, to the point where athletic administrations incur costs so they can monitor their athletes’ social media use to ensure that athletes do not post anything that could potentially tarnish the athlete’s, or entire organization’s, reputation. Larry Johnson (running back for the Kansas City Chiefs) was released from his NFL contract after publically insulting his coach on Twitter after a game. Rashard Mendenhall (running back for the Pittsburgh Steelers) controversially tweeted about Osama Bin Laden’s death and 9/11, and consequently lost an endorsement deal from Champion. Chad Johnson (wide receiver for the Cincinnati Bengals) was fined $25,000 for merely using Twitter during a preseason game. The negativity even extends to potential college recruits losing scholarship opportunities due to inappropriate content they post on social media.

It might seem that the only downfall of social media’s impact on the sports world lies within the athletes’ ability to express their uncensored thoughts. Sure, athletic organizations are able to pay thousands of dollars to a company that could provide some censorship, but does that really solve the problem? There has been a recent drop-off in athletes using Twitter because of the negative light associated with posting something controversial – “if something has the potential to end your career, you might as well not use it at all”. It is starting to seem that the deeply driven, homerun-like baseball of social media could turn into a routine fly-out. Given the many benefits of social media on the sports world, it is hard to imagine that athletic engagement with social networks will stop completely. This then begs the question: how do you allow athletes to share a deep connection with fans on social networks, while fully restricting them from saying anything controversial? I believe solving this problem will add enough juice to turn social media’s fly-out into a homerun, and ultimately show long-term potential.

References:

http://www.dailydot.com/opinion/morgan-2013-rise-of-sports-fan/

http://www.kttape.com/game-change-social-media-in-the-sports-world-infographic/

http://cdn.ientry.com/sites/webpronews/article_pics/sportssocialkttape5.png

 


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When Social Networks Get Political

Facebook users in the United States who logged in on Election Day (November 4th) were met with a message urging them to vote and a tool to guide them to their polling place. This feature was displayed to all users and was non-partisan in nature; Facebook has not revealed any influence from the United States government or a politically-affiliated group in developing it. This foray into influencing voter turnout begs three questions: is Facebook’s call to action effective? If so, might Facebook monetize this capability in the future, and would users be informed? And lastly, could this replace or augment current corporate speech (such as issue advocacy through lobbying, or financial donations to campaigns)? (Any questions of lawfulness will be omitted from this post, due to lack of legal expertise.)

Regarding effectiveness, Facebook has deployed similar features in 2010 and 2012 to measurable impact. The 2012 effort was affected by code bugs, but the 2010 experiment was run in partnership with data scientists who published a paper in Nature concluding that Facebook caused 0.14% of the US population to vote. (See URL below for details; the main conclusion is that these are truly incremental voters.) This increase in voter turnout is potentially significant enough to change the outcome of state and national elections. Whether users will become inured to Facebook-initiated advocacy is debatable. For example, Facebook is currently promoting donations to charities fighting Ebola in West Africa, and these types of initiatives will likely see diminishing returns if Facebook over-saturates users.

Assuming that Facebook’s efforts to get out the vote are effective, they risk public backlash under two scenarios. First, it’s conceivable that a Facebook engineer might “go rogue” and attempt to influence the actual outcome of an election, not just the level of voter turnout. This could be achieved by displaying the voting message to only select populations, or displaying different (and less effective) messages to targeted demographic segments. It’s possible this could go undiscovered, but even if spotted, there may be no redress available. Given Facebook’s recently-revealed experiment on whether it could impact user emotions (see link to summary below), it’s not clear that the Facebook engineering team can be trusted to make decisions in line with commonly-accepted ethical practices in the social sciences. Second, Facebook could evolve this “call to action” feature into a native advertising format that it sells to political actors, giving them access to voters who may not realize they are viewing a sponsored feature instead of Facebook-generated or user-generated content. (“Native advertising” refers to ad units that appear to be actual site content, and while it is generally marked “sponsored,” many users do not understand they are advertisements.) An uproar over “subliminal” political advertising could cause Facebook to kill the feature. It is unlikely that Facebook will monetize this feature due to brand risk, but the risk of internal teams choosing to selectively deploy the tool remains real.

Even if Facebook controls “unauthorized” selective deployment of the voting tool, it may still choose to intentionally influence election results. Many corporations speak publicly (through their CEOs, press releases, and corporate donations) to influence election or legislative outcomes, and Facebook could choose to do the same to support an issue or a candidate. For example, the CEOs of Starbucks, Whole Foods, and Aetna, among others, spoke publicly against the Affordable Care Act. Already, Mark Zuckerberg has been personally active in campaigning for immigration reform through his FWD.us PAC, and Facebook contributes directly to political campaigns, such as to three politicians who supported the Stop Online Piracy Act and Protect IP Act. Facebook has developed and proven the capability to increase voter turnout, and has incredible insight into its users’ political leanings (based on user-inputted data such as “liking” a political party’s Facebook page, or on predictive data such as age, gender, zip code, job title, etc.) Facebook’s ability to insidiously influence election results is almost unlimited, as users cannot opt out of the messaging, and may not even realize it’s being targeted specifically to them.

Although Facebook appears to want to positively influence the American democratic process in a non-partisan manner, the risks of abuse outweigh the benefits of increased voter participation. Facebook could permanently lose user trust, substantially damaging its revenues. Facebook should restrict its political speech to currently accepted channels and avoid speaking directly to users through Facebook.com.

Sources:

Nature paper on 2010 experiment: http://fowler.ucsd.edu/massive_turnout.pdf

Description of 2014 initiative in TechCrunch: http://techcrunch.com/2014/11/04/facebook-vote/

Description of emotional influence experiment: http://www.slate.com/articles/health_and_science/science/2014/06/facebook_unethical_experiment_it_made_news_feeds_happier_or_sadder_to_manipulate.html


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