The Yup Protocol is a social consensus protocol incentivized by an opinion economy that sits atop the internet. It sets the infrastructure for a new form of social network. Users receive token rewards and build influence on the platform by rating, posting, and curating content. The impact of their ratings and the influence and rewards they receive are proportional to their value as determined by other users. Their assets garnered through staking, engagement, and approval determine the impact of their ratings. The social level mechanism constructs hierarchical governance of the protocol, solving significant digital identity issues, accurate/transparent representation of media, and equitable monetization/ownership of personal information. Fractionalized shares of accounts and communities governed online will encourage fair markets of community-building, entertainment, and advocacy. The network exists within the framework of the protocol.
The components of economic growth traditionally include natural capital, physical capital, and human capital. In addition, the forces that drive actors to interact and organize themselves constitute a significant portion of production. This portion can be described as social capital. In 1988, sociologist James S. Coleman (Stanford, Chicago, Johns Hopkins) defined this as "a variety of different entities, with two elements in common: they all consist of some aspect of social structure, and they facilitate certain actions of actors" [Coleman] The digital realization of social capital has played an important role in the most recent decade but remains to be largely misunderstood and difficult to quantify.
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.
The opinions 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:
- 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.
- 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.
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.
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] Some may conclude that Facebook cannot sway people's votes but only encourage them to do so or not. However, selective display of content to certain demographics and electoral regions would allow Facebook to compel the general body to vote one way or the other.
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.
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.
The Steem three-token economy consists of Steem (STEEM), Steem Dollars (SBD), and Steem Power (SP), a system that has proven to be unnecessary and faulty in practice. [Steem] SBD is supposed to be a stable coin that utilizes algorithmic mechanisms to maintain a price pegged 1:1 to USD. Its ability to maintain stability is directly correlated to the success of the Steem network, a factor that is troubling for its long-term viability as a stable coin. Unfortunately, the price of SBD has failed to remain pegged to the US Dollar, holding a value of over $5 per SBD for 3 months (Dec '17 - Feb '18), peaking at $16 during this time, and currently priced at $0.97. [CMC] SBD's frequent volatility defeats the purpose of a stable currency, confusing users on where ownership of the network lies and how to store their value. Steem Power (SP) is a staked version of Steem with bond-like qualities which relies on user action to stake and unstake their STEEM to alter its supply and doesn't have much more of a use-case. This is quite inefficient and counts on user error to bouey the price of STEEM, giving a strong advantage to profit-driven automated users.
Voting bots plague the Steem network to such an extent that the community accepts them as a part of their ecosystem. Users can pay bots STEEM fees for an upvote on their content that is worth slightly more than they paid, making a quick return on investment. Additionally, users can proxy-stake their SP to voting bots, allowing bots to vote on users' behalfs and distribute rewards made from curation and fees. Users can earn more tokens by proxy-staking to bots than by actually participating in the network. As a result, users are now incentivized to be less engaged and simply proxy-stake their holdings to maximize returns. That, along with the lack of a sponsored content market solution, has caused the community to accept bots as the network's advertisement platform, a way for users to increase the reach of their content. Additional issues include the lack of strong incentives to support channels and accounts, the direct relationship between token holdings and influence on the network which ignores user activity or length of involvement, unannounced shutdowns of the blockchain for updates, and rampant misinformation.
We propose Yup as a second-layer protocol that facilitates the measurement, capture, and exchange of social capital in an anonymous yet transparent opinion-based economy. It identifies content and distributes rewards according to the value (influence) of the opinions associated with that content. In this case, we define content as any specific data online that user(s) deem worth judging, including but not limited to texts, images, videos, locations, accounts, and links. The influence metric is a function of engagement, ownership over time, and reputation (see influence). The decision-making and scaling of this platform will be determined by the community it serves.
The Yup protocol provides:
- Transparency of accounts and filtering tools based on their influence;
- Fair and direct monetization of opinion, influence, and content through free participation;
- Digital identities with social capital at stake;
- Community-driven codes of conduct;
- Anonymous participation;
- Network governance determined by direct influence;
- Equitable distribution of advertising revenue;
- Trustless ownership of network footprint;
- Influencer marketplaces and opportunities;
- Asset sales of verified accounts and groups;
The extent of these benefits mostly depends on the size of the network that participates in this protocol. In the next section, we will explain the components that make up the Yup protocol and how they produce the benefits outlined in this section.
The YUP token is designed to be a fungible crypto asset used to increase impact and engage with the Yup protocol. New tokens are minted under a predetermined schedule. The token reward mechanism mints new YUP tokens and distributes them according to the influence algorithm and LP shares. The account asset exchange allows top accounts to distribute and sell portions of their account in non-fungible tokens.
9,315,081 YUP is minted at genesis and will become accessible over the course of 1 year. The initial one year allocation is as follows:
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50% to Yup Creators and Curators = 4,657,540 YUP
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23% to Liquidity Providers = 2,142,469 YUP
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22% to Team = 2,049,317 YUP
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5% to Treasury = 465,754 YUP
Emissions will occur in 4 phases:
Phase 0 • Day 1: YUPX holders will immediately receive a retroactive distribution of YUP at a 1:1 rate. Additionally, over 10k twitter users will receive a proactive distribution of YUP according to their Twitter actions over the last few months and Yup users' ratings of them. Lastly, ~20% of this will be held for unclaimed creator rewards and 15% will be held by team. This will create an initial supply of 100,000 YUP.
Phase 1 • 1 Year: Daily emissions of 1.25% the total supply of YUP
Day 1: 1,250
Day 2: 1,266
Day 3: 1,281
etc...
Phase 2 • 1,049 Days (2.88 Years): Daily emissions decrease by 100 YUP each day until 10,000 YUP/day.
Phase 3 • indefinitely: Daily emissions of 10,000 YUP
The YUPX token was an experimental token that is supposed to resemble the YUP token that will soon replace it. It functioned as a means of stress-testing the protocol and experimenting with different approaches. YUPX isn’t a speculative asset, but a token.
Total supply: 100,000 YUPX
Token Smart Contract: yupyupxtoken
Influence is the metric used for weighing token reward distribution, network governance, transparent representation of social value and network commitment rather than token staking. It’s supposed to reflect a user’s social value more accurately than 1-for-1 votes or simple token-weighted schemes.
In simple terms:
Influence = Activity + YUP Age + Social Level + Boost
Token Age is the sum of the token value of each input transaction into an account multiplied by the number of blocks or periods since each transaction occurred. In simple terms, this is the number of tokens you have and how long you’ve had them.
Activity is a measure of how valuable the activity of an account is on the network. Mathematically, this is the difference between the value of all previous votes and all previous rewards received by an account.
Social Level is the numerical rank of each account, determined by the orders of all other accounts in a chain that references previous blocks. Simply, this is your social value as determined by all other users.
Boost is a mechanism by which users can burn tokens to increase their vote value, essentially betting bigger on a specific action. The amount that can be burned depends on the user’s current rating value.
It’s important to understand that having a large amount of one of these is not enough to have a high influence score.