Motivations

Introduction

Social Capital

Since the manifestation of digital communities, we have witnessed a growth in the general influence of online accounts and groups. In tandem, the opaqueness of the network identities has also grown. This general noise and inefficiency allows for behavior manipulation, giving rise to somewhat malicious tools such as bots, imported followers, and artificial content. Existing social networks lack transparent valuation of social value. They have done a very good job of utilizing data analytics for target advertising but offer little information significant to valuing an account or action. This makes it difficult to distinguish genuine participants from artificial or malicious ones, appealing from disliked, or trustworthy from sly. In some cases, this has been drastic enough to push users to create their own software and bots that identify and block malicious behavior. [Geiger] There is no efficient manner to get proper social representation of an account, content, or group from each user's own perspective. With new technologies, we explore new ways to distribute and represent social capital through market incentives.

Incentivizing Curation

The curation of others are vital to the internet economy; it is the core component upon which value metrics are built. This is most clear in online product/service reviews. The results of a 2017 study by Podium show that 93% of American consumers say that online reviews have an impact on their purchasing decisions. 91% of 18-34 year old consumers trust online reviews as much as personal recommendations. Star rating is the number one factor used by users to judge businesses. [Podium]

However, treating these social metrics as highly accurate does not necessarily make them so. University of Colorado Boulder professors Langhe, Fernbach, and Lichtenstein investigated the actual and perceived validity of online user ratings and found a \"substantial disconnect between the objective quality information that online user ratings actually convey and the extent to which consumers trust them as indicators of objective quality.\" [Langhe] They analyzed 344,157 Amazon ratings of 1,272 products in 120 product categories and compared them to ratings on Consumer Report as well as resale value. They conclude that average user ratings correlate poorly with Consumer Reports scores, and, while Consumer Reports correctly predict resale value, user ratings do not.

Additionally, Langhe et al. argue that "consumers fail to consider these issues appropriately when forming quality inferences from user ratings and other observable cues. They place enormous weight on the average user rating as an indicator of objective quality compared to other cues. They also fail to moderate their reliance on the average user rating when sample size is insufficient." In other words, the average user lacks both the data and know-how to make accurate inferences on the quality of products based on user ratings.

Through the conclusions of this study, we can draw two important explanations for the invalidity of most online opinions:

  1. When reviewers are vetted and paid to review products (as is the case in Consumer Reports), their opinions are usually a stronger indication of quality. Without a mechanism to ensure this, reviews lose their meaning.

  2. Online sites are doing a poor job in presenting existing metrics to users in order to enable better qualitative decisions.

Despite both the high value attributed to user opinion and the importance of monetizing it for accurate representation of quality, our own research suggests that less than 0.001% of online opinion is monetized today. Without any incentive to review honestly, users tend to express negative reviews more often and more extremely, with little to no reason to contribute positive reviews. This lack of monetization also tips the balance for malicious actors looking to manipulate their perceived quality: the incentives to create a false opinion for money from a malicious actor outweigh those to review honestly.

The Power of Attention

Online attention is a key ingredient in harvesting influence. Several entities have capitalized on this notion, quickly trading it as a commodity of product sales and branding. Additionally, it gave power and voice to advocacy. Donald Trump's ability to master Twitter to capture the minds of millions during his 2016 electoral campaign was historical. [NYT] On Fox News, he doubts he'd be here if it weren't for social media. [Fox]

Legendary salesman Sally Hogshead once wrote, "Attention is the ultimate form of currency." This has become increasingly correct with the growth of the internet. In current social networks, attention is commercialized in two core ways: (1) advertising --to sell something, whether directly or indirectly through branding, and (2) social capital--to motivate some sort of action for another utility. Each platform remains economically efficient through the resale of what attention it could capture in exchange for its 'free' content.

The concept of content has morphed. Some of the most viewed videos of today have a one-day shelf-life, referred to as 'stories'. Similar to how record sales are separately counted than song streams, social interactions of all kinds have a different worth and must be measured differently. The legacy social media platforms are not measuring varying forms of engagement independently. While creators were building followings for themselves, they were in tandem building them for specific platforms. Creators and curators built the backbone of the strongest internet economies but rarely received a fair portion of the returns or control of those platforms. Furthermore, attention is traded in automated markets and determined by machine learning strategies, benefiting platform and advertisers while hurting organic creators and users.

Unsustainable Distribution of Social Capital and Returns

Social networks are ubiquitous. 83 percent of Americans have a social media account, and 77 in the United Kingdom. 2.8 billion people use social media. The large titan of attention, Facebook, creates 68% of social media traffic and 7% of all online traffic. [Tachalova] According to an in-depth study by media management platform Hootsuite, it has 1.65 billion monthly active users and 1.09 billion daily active users. [Hootsuite] Twitter has 310 million monthly active users and, every second, 6,000 tweets are sent. [WeRSM] These networks span generations, nations, and cultures. Instagram, worth $102 Billion, has more than 400 million active monthly users. Of its user base, 75 percent are outside the U.S. Users 'like' 3.5 billion photos and share 80 million more.

Certainly those statistics show that what has been accomplished is significant. What is also worth mentioning is the economic rent amassed by these social media giants; upon a closer look at the behavior of users, one can find inconsistencies in representation and exchange of value. Influence can be bought with several methods. Some examples include purchasing followers outright (both human and bots depending on price), using automated bots to engage with other users to increase one's own reach, and advertising cheaply to foreign users outside of normal target demographics. A grocery store in Los Angeles can advertise to active users of a developing nation for cents on the dollar and grow their perceived following for the signaling benefits regardless of expected return. Users are sold in bulk as followers, likes, and comments, sometimes soliciting customers through messaging of those same applications, as shown in the figure to the left.

In Culture and Power: The Sociology of Pierre Bourdieu, David Swartz begins with "Culture provides the very grounds for human communication and interaction; it is also a source of domination." The same could be stated about the online behemoths that presently host our culture. While traditional social networks have expanded the size and function of communication, marketing, and organization, their monopolistic increase in internet market power has drawn a wide divide in incentives between their services and their user base. While there are forces that compel large networks to comply to certain restrictions, the implications of the extent of their hold over human behavior is concerning. For example, in 2010, Facebook ran a stealth experiment on 61,000,000 American accounts during the US congressional elections to see how small messages (banners above the news feed) could affect user's voter turnout and more. [Bond] They argue: "The results show that the messages directly influenced political self-expression, information seeking, and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends."

Contextually, this effect is noteworthy. Their results suggest that "Facebook social message increased turnout directly by about 60,000 voters and indirectly through social contagion by another 280,000 voters, for a total of 340,000 additional votes." That represents about 0.14% of the country's voting age population in 2010. For context, George Bush beat Al Gore in Florida by 537 votes in 2000. If a similar tight race occurred today, it wouldn't be hyperbole to assume that Facebook could alter the political landscape of the United States, certainly with their >400 percent growth in active users since 2010. [Statista]

In this case, there may be a reputational cost to influencing human behavior that Facebook could not ignore: if operators meddled with elections, the economic and social backlash would be destructive. Two real-life events discourage this theory. First is the above-mentioned study: Facebook ran the voting experiment in 2010 but only made the results public in 2012 under their free will. If they had maliciously kept this study internal, the U.S. public and government may not have ever been aware. In a hypothetical case where they would be in fact acting with malicious intent, Facebook would also have an easy time hiding this from the public. The second convincing event is the Cambridge Analytica data breach, which had little to no impact on Facebook's market dominance. Despite exposing over 80 million users' information to third parties, resulting in trust in Facebook falling by over 50% in the following weeks, [NBC] daily active users, minutes of usage, and advertising revenue all increased. [BI] This suggests that not only can giant social networks hide their manipulation, but also that average users are too network-dependent for their sentiment to be notably reflected in Facebook's economics.

Advertisement is currently the primary form of online content monetization, often considered the 'original sin' of the internet. [Zuckerman] Content creators in top social media platforms have little direct way to monetize their fans' attention or get a share of ad revenue. In response, they have resorted to grossly ineffective workarounds. This resulted in an economy that disproportionately rewards a handful of centralized social media companies instead of the content creators that give such platforms value. Members of thriving online communities that exist on popular social media platforms currently have no control in the developments of the sites they helped grow; the length and extent of their engagement is undermined. Additionally, there is no direct relationship between the demand of users and sponsored content/monetization. This stunts influencers' ability to build user loyalty around an account or channel. There are no real incentives to support quality content and discourage poor behavior. The websites domain is a central point of failure and is susceptible to censorship.

Upon examining the makeup of existing centralized social networks, we conclude that social capital is misrepresented and easily purchased, and the monetization of attention is one-sided or separate from the network itself.

Current Decentralized Solutions

At the consensus layer, there are several inefficiencies that can be improved upon with social capital. Nakamoto consensus uses proof-of-work to cleanly solve several issues in majority decision making, abandoning the notion of "one-IP-one-vote" for "one-CPU-one-vote". [Nakamoto] However, one problem that arises as a result is the power ascribed to outsourced physical capital. Participants can gain more influence over the network by purchasing computational power with money from other economic systems. This means that the relationship between capital spent to maintain the network and the capital earned for doing so is not quite internal: 1 kW of electricity purchased with USD has equal power over the Bitcoin network as 1 kW purchased with BTC. PoS and DPoS mechanisms improve on this problem by requiring miners to stake network tokens to participate in consensus ('one-token-one-vote'). Yet, it still does not properly reflect network participation: staking 1 network token that was purchased on an exchange provides the same mining power as 1 network token earned via mining. This dependence on physical capital makes networks susceptible to byzantine behavior from capital-rich outside parties as well as hinders the most-mover-advantages of participating in consensus. The ability to transparently quantify and represent social capital can provide stronger models for decentralized systems.

Beyond transaction consensus, there are a handful of distributed ledgers, platforms, and applications being built to decentralize social networks and advertising. Steem, a decentralized social network built using delegated proof-of-stake (DPoS), mints new tokens and rewards content creators for their involvement. While Steem has done a better job than most in battling the dilemmas of user experience in decentralized solutions, it still misses the mark on smoothness and barrier to entry, similarly performing poorly on fair distribution and user retention. In order to mitigate sybil attacks, the Steem network places certain barriers on accounts that are hurting their onboarding percentages. For example, the sign-up process can take up to 2 weeks and sometimes new users never receive approval from Steem witnesses responsible for creating accounts. Additionally, it has certain immutable characteristics that discourage easy user activity: account names are unchangeable once chosen, account passwords are long immutable private keys that need to be entered at every login and posts/comments are very difficult if not impossible to delete once added to the network.

Value Proposition