Credible

credible

Author:
(1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 1 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract
The Benefits of Simple Writing
The Current Work
Study 1a: Method
Study 1a: Results
Study 1b: Method
Study 1b: Results
Study 2: Method
Study 2: Results
General Discussion, Acknowledgements, and References Abstract This paper evaluated the effectiveness of using generative AI to simplify science communication and enhance public trust in science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work assessed linguistic simplicity across such summaries and public perceptions. Study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small. Study 1b used GPT-4 to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning. Finally, Study 2 experimentally demonstrated that simply-written GPT summaries facilitated more favorable public perceptions of scientists (their credibility, trustworthiness) than more complexly-written human PNAS summaries. AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society. Significance Statement Across several studies, this paper revealed that generative AI can simplify science communication, making complex concepts feel more accessible and enhancing public trust in scientists. By comparing traditional scientific summaries from the journal PNAS to AI-generated summaries of the same work, this research demonstrated that AI can produce even simpler and clearer explanations of scientific information that are easier for the general public to understand. Importantly, these simplified summaries can improve perceptions of scientists’ credibility and trustworthiness as experimentally demonstrated in this work. With small, language-level changes, AI has the potential to be effective science communicators and its possible deployment at scale makes it an appealing technology for clearer science communication. Science Written by Generative AI is Perceived as Less Intelligent, but More Credible and Trustworthy than Science Written by Humans Scientific information is essential for everyday decision-making. People often use science, or information communicated by scientists, to make decisions in medical settings (1), environmental settings (2), and many others (3). For people to use such information effectively, however, they must have some amount of scientific literacy (4) or at least trust those who communicate scientific information to them (5). Overwhelming evidence suggests these ideals are not being met, as trust in scientists and scientific evidence have decreased over time for nontrivial reasons (e.g., distrust in institutions, political polarization, among many others) (6–8). The public’s decreasing trust in scientists and scientific information is unrelenting, which requires more thoughtful research into countermeasures and possible remedies that can be scaled across people and populations. Several remedies have been proposed to make science more approachable, and to improve the perception of scientists. For example, some propose that being transparent about how research was conducted and disclosing possible conflicts of interest (9, 10), having scientists engage with the public about their work (11), or improving scientists’ ability to tell a compelling story (12) can increase public trust. While there is no panacea for dwindling public trust in science and scientists, extant evidence suggests this is an issue worth taking seriously, and it is imperative that scientists discover ways to best communicate their work with the hope of improving how people perceive them and their research. Against this backdrop, the current work argues that how one’s science is communicated matters, and that language-level changes to scientific summaries can significantly improve perceptions of a scientist. Critically, the evidence in this paper suggests scientists may not be the best messengers to communicate their work if one goal is to communicate science simply. In other words, it may be difficult for experts to write for non-experts. Instead, as the current research demonstrates, generative AI can effectively summarize scientific writing in ways that are more approachable for lay readers, and such tools can be scaled to improve science communication efforts at a system level. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Author: Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 1 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 1 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 1 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract The Benefits of Simple Writing The Current Work Study 1a: Method Study 1a: Results Study 1b: Method Study 1b: Results Study 2: Method Study 2: Results General Discussion, Acknowledgements, and References Abstract Abstract The Benefits of Simple Writing The Benefits of Simple Writing The Current Work The Current Work Study 1a: Method Study 1a: Method Study 1a: Results Study 1a: Results Study 1b: Method Study 1b: Method Study 1b: Results Study 1b: Results Study 2: Method Study 2: Method Study 2: Results Study 2: Results General Discussion, Acknowledgements, and References General Discussion, Acknowledgements, and References Abstract This paper evaluated the effectiveness of using generative AI to simplify science communication and enhance public trust in science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work assessed linguistic simplicity across such summaries and public perceptions. Study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small. Study 1b used GPT-4 to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning. Finally, Study 2 experimentally demonstrated that simply-written GPT summaries facilitated more favorable public perceptions of scientists (their credibility, trustworthiness) than more complexly-written human PNAS summaries. AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society. Significance Statement Across several studies, this paper revealed that generative AI can simplify science communication, making complex concepts feel more accessible and enhancing public trust in scientists. By comparing traditional scientific summaries from the journal PNAS to AI-generated summaries of the same work, this research demonstrated that AI can produce even simpler and clearer explanations of scientific information that are easier for the general public to understand. Importantly, these simplified summaries can improve perceptions of scientists’ credibility and trustworthiness as experimentally demonstrated in this work. With small, language-level changes, AI has the potential to be effective science communicators and its possible deployment at scale makes it an appealing technology for clearer science communication. Science Written by Generative AI is Perceived as Less Intelligent, but More Credible and Trustworthy than Science Written by Humans Scientific information is essential for everyday decision-making. People often use science, or information communicated by scientists, to make decisions in medical settings (1), environmental settings (2), and many others (3). For people to use such information effectively, however, they must have some amount of scientific literacy (4) or at least trust those who communicate scientific information to them (5). Overwhelming evidence suggests these ideals are not being met, as trust in scientists and scientific evidence have decreased over time for nontrivial reasons (e.g., distrust in institutions, political polarization, among many others) (6–8). The public’s decreasing trust in scientists and scientific information is unrelenting, which requires more thoughtful research into countermeasures and possible remedies that can be scaled across people and populations. Several remedies have been proposed to make science more approachable, and to improve the perception of scientists. For example, some propose that being transparent about how research was conducted and disclosing possible conflicts of interest (9, 10), having scientists engage with the public about their work (11), or improving scientists’ ability to tell a compelling story (12) can increase public trust. While there is no panacea for dwindling public trust in science and scientists, extant evidence suggests this is an issue worth taking seriously, and it is imperative that scientists discover ways to best communicate their work with the hope of improving how people perceive them and their research. Against this backdrop, the current work argues that how one’s science is communicated matters, and that language-level changes to scientific summaries can significantly improve perceptions of a scientist. Critically, the evidence in this paper suggests scientists may not be the best messengers to communicate their work if one goal is to communicate science simply. In other words, it may be difficult for experts to write for non-experts. Instead, as the current research demonstrates, generative AI can effectively summarize scientific writing in ways that are more approachable for lay readers, and such tools can be scaled to improve science communication efforts at a system level. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Study Finds Generative AI Appears Less Intelligent Yet More Credible Than Humans in Science Writing

For the 22nd part of Unhashed, I reached out to Nathan Keller, the lead developer at Optimus Venture, a community & crowdfunding investment platform for blockchain and crypto projects. So, let’s dive into his insights about the DeFi and crypto space. Q1. Welcome to Unhashed. What’s your crypto story and what led you to start working on Optimus Ventures? Answer: We’ve been running a private crypto investment group for over four years now and during this period we’ve seen firsthand the complicated relationship between DeFi investors and businesses. Given the novel nature of this industry, investors are still skeptical about future DeFi projects. There’s always a possibility that the project they invested in will turn out to be a scam or fail to perform and this essentially drives them away from the industry. From the point of view of businesses, this makes it hard for them to raise funds for the development of the project, and even if they do find investors, the possibility of a pump and dump episode can never be ruled out. This kind of complicated relationship between investors and businesses in DeFi has choked many promising projects into oblivion. And, having seen this for so long, we wanted to create a better way to connect projects and investors, and that is how Optimus was created. Our primary goal with Optimus Ventures is to help raise promising DeFi projects that can contribute to the growth of the industry and add value to the community of investors. All of our projects are carefully vetted to evade the possibility of a scam and our proprietary smart contract ensures that investors are protected. With such measures in place, we hope to improve the complicated investor-business relationship in DeFi and ensure that promising projects have a fighting chance for success. Q2. Raising funds in a KYC-compliant way is a tough ask. Given the fragile regulations in place, how viable is crowdfunding for crypto/DeFi businesses? Answer: Projects launching on the Optimus platform have the liberty to decide whether or not they’d like a KYC. Our role is to connect investors with promising projects, but the project developers are the ones launching the sale smart contract and it can be tailored to suit their needs. Regarding the liability of crowdfunding, DeFi platforms have more or less revolutionized it in recent times. When compared to other ways of fundraising, crowdfunding on DeFi offers transparency, a reliable infrastructure, a good reward system, and security above all. Yes, the regulations are fragile but given that DeFi’s crowdfunding platforms are based on the blockchain, the security risks are minimal. Moreover, on platforms like Optimus where projects are vetted for reliability and potential, the chances of fraud of greatly reduced, and crowdfunding could be the most feasible means of fundraising for emergent projects. Q3. What are the advantages that Optimus Ventures stands to reap with the unique 2-way vesting implementation? Answer: We believe that two-way vesting is the key to improving the relationship between investors and businesses in DeFi. It has numerous advantages for both sides. In two-way vesting, investor assets are locked in a smart contract on our platform. If the project does well to succeed, they project tokens strategically released in fixed time periods. However, if the project goes south, investor assets are still safe and will be returned to them.  This minimizes the risk involved with DeFi investing and allows investors to be protected through the whole ordeal. Q4. What is the viability of evaluating investor behavior? With several variables in play, can investor behavior be a benchmark for accessing investment opportunities in the crypto or DeFi space? Answer: When you work at a crypto investment group for as long as we have, you’ll know how poor-acting investors can hurt promising projects in the DeFi space. In doing so, they not only thwart the chances of the project’s success but only take away investment opportunities from reliable investors. In this regard, having a gauge of investor behavior in the past will give projects an edge, letting them evaluate and work with reliable investors who can actually contribute to the project’s growth. We believe that investor behavior should absolutely be an important metric for accessing early-stage investment opportunities. This will help not just the projects but also genuine investors building projects. Q5. Please elaborate on the concept of 'dynamic allocations' and how that transpires into reality during raising capital. Answer: At the core of the dynamic allocation system is the HODL score that takes investor data from past token sales, and uses it to determine future token sales allocation. The score is based on how many tokens were sold over a period of time since TGE. Another component is allocation usage. A member that does not use the allocation he was given in past sales will get less allocation in future sales. The third component is the percentage of $OPTCM tokens held compared to others in that sale round. So in reality, when there is a new project in Optimus, investors have the ability to express their interest to invest. Once the interest stage is over, the system will automatically calculate individual allocations based on the total allocation and after implementing the dynamic allocation algorithm. What should happen as time goes by, is that dumpers will get less allocation in new projects. Q6. What safety measures are a must for investment protocols and DeFi platforms to adopt in order to protect investors in a dynamic space as crypto? Answer: DeFi platforms have become an easy target for hackers in recent years. They have left no stone unturned to exploit bugs and smart contract code vulnerabilities to pocket billions of dollars worth of assets. This is why it is important for DeFi platforms, especially launchpads to go the extra mile to ensure the safety of investor assets. A full performance and security testing of the protocol can be conducted to ensure that there are weak links that hackers can exploit. Additionally, smart contracts can be audited by renowned security firms like Certik to ensure that they are safe to deploy. Measures like this, though time-consuming at the beginning, can help secure the protocol and retain users’ trust by safeguarding their assets. Q7.How do you view the future of crypto and DeFi investments in the context of taxes and other regulations? As crypto and DeFi industries are slowly making their way towards the mainstream, conversations around regulation are emerging. Coinbase’s recent tiff with the SEC is a clear sign that regulators are finally considering DeFi. There is a good chance that in the coming years, most countries will clarify their stance on cryptocurrencies and some of them have already shown the willingness to classify them as an asset class. In such a case, the returns on crypto investments can become taxable and investors definitely need to keep this in mind. But on the positive side, this means official recognition from the world governments which can increase user trust and make DeFi more appealing to a wide variety of people. Disclaimer: The sole purpose of Unhashed is to unhash (decode) information about projects innovating using blockchain and cryptocurrencies and share it with the community. The does not have any vested interest in any of the projects covered herein. Not that this article shares any, but still, taking investment advice from strangers on the internet is not a wise thing to do. writer

Poor Acting VCs are a Threat to Credible DeFi Projects: Unhashed #22

Entrepreneurs are known to run into dozens of ideas on a daily basis, if not more. While most of them seem to border on absurdity in the beginning, it is often difficult to determine whether or not they would work out in the long run. That’s why the best way of ensuring your idea is worth developing is to learn at the fastest possible pace and have the willingness to try out different experiments. Based on our proven knowledge on product development and experience, we have developed a template document relating to the process of idea validation, and wrote the guide to explain each question and step in a simple, understandable manner. How’s this helpful? Avoid wasting time and resources on something that’s certainly not worth it. Learn proactively and pivot your idea at a much quicker pace. Validate your product idea without actual budget/coding skills. Answering all the questions posed in this guide means it can serve as a worthwhile investor deck. Couple this with running some experiments and you’re well poised to raise money from investors! Most critically, it provides a credible framework to separate good ideas from bad ones. Value Proposition What important truth does your idea uncover that is not mainstream? The following would be a business version of this question: what is that valuable company that nobody is building? Every correct answer is necessarily a secret: something important and unknown, something hard to do but doable ( ). by Peter Thiel It is important for your idea to reveal some unknown, unconventional truth in order to create something of value. Something nobody chooses to talk about aloud, but will become evident to all after it’s unraveled. For more interesting nuggets and insights on read book. startups that attract VC investors Zero to One Define fundamental value assumptions Here at datarockets, we classify three kinds of assumptions: Value assumptions: To define the value of your idea and future product. Business model assumptions: This affects the manner in which you get your product monetized. Growth assumptions: How you’ll ensure growth of your product and continue to attract new customers. Let’s begin with the basics: the value assumptions! What are your fundamental value assumptions? How does it help to resolve real-world problems? On the basis of the previous chapter’s statement, it’s best to define the fundamental value assumptions and experiment with them in the fastest possible manner. is another popular term for fundamental value assumptions. Leap of faith assumptions If your leap of faith assumptions are not true, the whole idea is not viable. This means that if your failed assumption won’t end up ruining the whole idea, then it’s not vitally important to begin with, thus implying that it can no longer be your fundamental value assumption. Growth evaluation Does your idea grow with the advancements of supporting technologies (durability)? Before taking the next step, confirm whether a new, upcoming technology won’t kill your promising idea. Ex: If you’re intending to develop a startup that teaches people how to drive vehicles, it may make sense to assume that with the advance of self-driving cars your business might get destroyed. Existing Solutions Research Has anyone implemented your idea / tried to implement it before? Although a typical process begins with communicating with people whose pain points will be addressed with your product/service, it makes a lot of sense to first research your idea and determine if similar services/solutions are already available in the market. After ensuring that the competition is not excessively intense or your idea provides breakthrough value with its uniqueness, it’s time to proceed further. Find communities that may potentially be facing the problem you’re looking to resolve. Ask how they’re currently dealing with it and if they’d be interested in trying out your way. More often than not, other people may already be solving the problem you plan to solve. They may be doing that using some tricks or manual solutions. Identify and talk to them about how they tackle such challenges, what is it that they struggle with, and whether or not they’d like to explore your way of doing that. Based on who your target audience is, join Facebook groups where your target audience is likely to assemble, gather feedback from your own network, or even consider talking to people on the streets. Remember, whenever you wish to solve someone's problem or fulfil an unmet need in the market, the first step is to get people to stop using other solutions or make changes in lifestyles. After ensuring the problem is worthy of being solved and something that people wouldn’t mind paying for, you’re ready to take the next step. Why do you think your solution is better? After taking the above steps, focus on the advantages and the distinguishing features of your solutions that set them apart from others. List them in a logical order and then explain why people will find more value in your solution. What value will it bring to them? Market Analysis Previously, we’d been assessing and validating our Value Assumptions. Ensuring the viability of your idea was their core purpose. Unfortunately, merely having a good idea to create a long-term profitable product does not suffice. Think about the different ways in which you make money out of your solution via Market Analysis and Business Model Assumptions. Define your potential target market 1. Narrow down your potential target market. Who is your ideal customer? Why should they pay for your solution? Targeting everyone essentially means that you’re not targeting anyone in particular. Concentrating on the appropriate target market is a good approach because it lets you maximize your returns on the time, money and energy you’ve invested. Secondly, it’s far easier to conduct  experiments in a narrow market. Try and define specific groups you wish to target with your service/product alongside their potential sources of revenue. Depending on your idea, you can even group them by job, interests, demographics, etc. Ensure that your target audience will be able to buy your products as the revenue source of each target will be different. Now, there are some industries where people are very unlikely to pay for additional services. As a case in point, the probability of people paying to use social media for communication purposes is very low. In such scenarios, use your discretion and prepare a draft of your . While it needn’t be your final blueprint, it will greatly simplify your task by helping you select more attractive markets. monetization strategy 2. Use Google Trends/Semrush to see how many people google solutions for the problem you are trying to solve Tools like Google Trends or Semrush give you an accurate picture of just how many people are seeking solutions to the problem you’re attempting to resolve. Most importantly, such tools allow you to check who these people are, know what their trends are, and provide a reasonable accurate estimate of the size of your target market. 3. Reach out to five well-known people in the market and ask for feedback on your idea After gathering the necessary information based on the previous steps, select a market (or a couple of them) that seems most promising. The next step in this phase is to gather feedback from specific groups of people relevant to your market. Doing this will give you plenty of actionable insights and the opportunity to further examine your idea from different perspectives. Create a portrait of your target audience In this phase, try to be as specific as possible about all relevant attributes of your target audience that describe them/their lives accurately and narrow them down further - age, gender, interests, position, personality, daily schedule, reading patterns, etc. Regardless of whether you describe a person or a company, depending on your sales model (B2B or B2C), the approach will remain unchanged. Consider the example from the . "1-Page Marketing Plan" book Conduct target user interviews According to Eric Migicovsky, a Y Combinator partner, the best firms are those whose founders establish a direct connection with their users. If you happen to be a founder, consider talking directly to the users of your product or solution. Eric cites a useful example of a structure that can be used to hold your user interviews. After tailoring the structure based on our needs and priorities, this is what we’ve come up with: In his talk, What makes the problem difficult for them? When did they last face it? What have they done to solve the problem? What are the things about existing solutions that they don’t like? How much would they pay to resolve the problem? Business Model Assumptions List your business model assumptions (based on the assumptions on what you assume your customers are going to pay and how much) After defining the problem to be solved, doing user interviews and knowing more about the target audience, it’s now time to brainstorm ways of monetizing your product. It’s a good idea to first get better informed about various before jotting down your assumptions about how your customers would be paying and how much. Put every idea on your mind on the table. Consider these two examples: types of business models I assume users will pay $15 per month to gain unlimited access to music on my platform (Subscription-based model) I assume concert managers will not mind paying $50 to promote their event on my platform if I implement advertising. (Ad-based) Growth Assumptions List your growth assumptions (what is your plan to rope in new users and how much cost would that involve) To survive and thrive, it’s critical to ensure your product grows smoothly and plan out ways in which you’ll attract new users or enter into new deals (partnerships). You’ll have a far better picture of your future marketing investment by writing down all possible growth hacking and marketing ideas. (prepare to get startled by the fact that budgets for marketing are often bigger than those for development purposes ;)) Does your growth engine generate new users from the actions of existing users? List out your acquisition loops. Getting new users effortlessly without spending any money is always a pleasant feeling. In that context, consider exploring which will make a great addition for your monetization strategies. acquisition loops The core idea behind acquisition loops is as follows: The more users you have, the more users are attracted. You can implement acquisition loops in the form of referral programs (e.g. Airbnb and Uber offer free coupons/rides for each referred user) or content loops that are user generated (Reddit, social networks, etc.). Natural acquisition loops are extremely important when you’re planning to execute an Ad-based business model, because the average revenue per user (ARPU) of ad-based models are lower in comparison to let’s say SaaS. That's why they must acquire new users cheaply to generate sustainable profits and reap massive profits. What’s your assumed revenue source? How would your profits/expenses grow as the number of customers grows (scalability assumption)? Needless to say, it’s not easy to accurately predict your expenses and profits. Some startups have funding to cover their expenses for two years or so until the venture starts making profits. If that’s not your case, pay close attention to this aspect and foresee your potential scalability issues and expenses. Competitor Analysis High-level SEO analysis This step needs us to continue working with product positioning and simultaneously address several key questions: How many people are interested in this? How difficult is it to rank on the first page of Google? What keywords should I use to promote my product or service? As you begin to answer these questions, it will become crystal clear that some keywords are more popular as compared to others. Some find it really hard to attain a high ranking on Google. Here, our objective is to compare word combinations that translate your business model to identify keywords with a balanced volume and keyword difficulty (KD). Utilize tools like Semrush, Ahrefs Keyword Finder, Google Ads Keyword tool, etc. to collect the information about Volume, CPC, KD and leading websites that rank by these keywords. Competitor overview Competitor overview is an extremely important step to undertake before proceeding further and it requires you to gather information about your competitors’ users, business models, revenue, etc. We this guide, we want to draw your attention to three attributes: Your Advantage How Hard to Replicate Your Advantage Why Should Customers Buy from You From these characteristics, it’s clear that you’ll compare your value proposition with your competitors and know what your unique selling proposition is. Learn more about your competitors Usually, you’ll get more insights from a personal interaction with your rivals than from conducting a basic competitor overview. It’s a good idea to reach out to them and know more about how their business is shaping up and the struggles they’re going through. Some effective ways of doing this include: Scheduling a demo as a user. When it comes to small startups, you’re more likely to be able to engage in a personal talk with product owners that are very interested in collecting user feedback. Contact product owners directly as their competitor. Generally, experienced startup owners are not opposed to the idea of talking to competitors and others from within the same industry. In most cases, they’ll frankly talk about their experiences/struggles, caution you about some pitfalls and share some additional information that you may find relevant. Conduct in-depth research on their personal pages and blogs. In some rare cases, founders openly reveal such as conversions, active users, revenue, etc. all the information about the product and its metrics Experimental Plan Definition Until now, you’re likely to have collected several interesting ideas of business models as well as growth assumptions. Now is the time to get them tested. How are you going to test your business and product positioning assumptions? Let’s face it. It’s not viable to build MVPs for every different assumption. Do the smart thing, instead. There are many tricks that you can implement to test ideas without actual development. We covered some of them in our blog post on the : Lean Startup approach Landing page. After using builders to develop simple websites with your value proposition, collect early-bird invites or sign-ups for testing the app. This is an effective way of evaluating the interest of users. Demo video. Several startups received funding only for the video that explained their vision because the product had already been built. Crowdfunding platform. This can be a useful option not only to validate your ideas, but also to identify early adopters and receive payments before you actually develop the product. Concierge MVP. Sometimes, you can test the idea manually rather than having a web platform or application. Where can you find your early adopters? Now you must locate people who’re facing the problem you plan to solve and get them to test your product/solution. They will not only give you valuable feedback, but also spread the word. By now, you should know how to find them because you’ve already talked to them in the Value Proposition stage. Crowdfunding platforms Facebook / Reddit / Telegram groups Current clients (if your business is running offline for now), etc. How are you going to collect feedback? Put differently, how will you communicate with your early adopters? Feedback forms Facebook groups / Telegram channels Email campaigns Experimenting Experimentation time! At this stage, you would have every information you need to test the assumptions and begin data collection to identify your product positioning and go-to-market business model. After launching your demo videos/ landing pages, go ahead and collect some data. Not every idea is explained in the same way, so you may want to quickly run a couple of Google ads to see people’s reaction to various keywords for product positioning. Putting the metrics on the table will let you see what experiment turned out to be most successful. Although we offer to check these metrics, it’s your call on whether or not to make changes in this list: Impressions CTR Visits Signups Conversion rate Cost per Signup Assumed Average Revenue per User You may also want to check our massive, extremely helpful guide on . Startup Metrics MVP Scope Definition If you’ve successfully reached this stage, congratulate yourself for making good progress! You now have a very good reason to believe that your idea is worth developing. Time to define the scope of work by preparing for MVP building: Define your go-to-market business model. Conduct interviews with customers who have signed up. Ask about their expectations and top-3 features they’re looking to implement in the product. Match their expectations with your pricing model. Do a cross-analysis of the interviews in order to find out most desirable features. Consider writing simple User Stories. Predict revenues for 100/1000/10000 paying customers and calculate your business model’s unit-economics Obtain estimates on the numerous features and determine the ones you wish to include in the MVP. Make it a point to only include the most important features that help validate your key assumptions. You can always add the rest at a later stage. Ensure that your budget for MVP is less than 30k-40k. Conclusion Yeah, it does look like a long, tedious process. But here’s the good news for you! You can test your ideas quickly and effectively after gaining some experience. Another piece of good news: if you happen to fail at any stage, there’s no reason to be upset about it, because it basically means that you successfully prevented your money from being wasted on developing a product that was likely to fail in the first place. Sometimes, a back-up option for a not-so-strong idea is to pivot it and repeat the cycle. Besides, we’re always ready to help in whatever way we can with the idea validation process and product development insights. All you need to do is to drop us a line and we’ll be happy to talk! Previously published at https://datarockets.com/blog/business/validate-startup-idea/

A Credible Framework to Validate Startup Ideas

Hong Kong, China, May 22nd, 2025/Chainwire/--Psy (formerly QED Protocol) has developed a trustless bridge connecting Dogecoin to the Solana blockchain. This innovation brings proof-of-work security to Solana while making Dogecoin, the world's largest memecoin, available to Solana's ecosystem of dapps. Psy Psy This innovation allows Solana and Dogecoin to directly 'speak' to each other, with each blockchain able to independently verify the other's transactions and consensus without requiring trust in third parties. The bridge not only enhances security but also creates substantial ecosystem opportunities. Bringing Dogecoin's $36B+ market cap and massive community to Solana opens new possibilities for both networks. Dogecoin users gain access to Solana's DeFi, NFT, and gaming applications, while Solana developers can tap into Dogecoin's extensive user base. Unlike traditional bridges, which often rely on multisig signers or custodians, Psy's next-generation proof-of-work technology validates Dogecoin Proof of Work consensus directly on Solana. This trust-minimized approach helps address a major weakness in crypto infrastructure: bridge hacks, which have caused more than $2.8 billion in losses to date. more than $2.8 billion more than $2.8 billion This positions Psy as the leading proof-of-work innovator for Solana, bridging the security benefits of proof-of-work with Solana’s speed and programmability. This integration demonstrates that new proof-of-work chains, like Psy’s, can interact with high-performance blockchains without sacrificing security or requiring centralized intermediaries. This bridge continues Psy Protocol’s mission to empower developers to build hyper-scalable web3 applications to host the next generation of the decentralized internet. The bridge supports standard Dogecoin wallets and exchange deposits. Quotes “We have been working hard to find ways to better serve the Doge community, and now we get the chance to offer them even greater utility for their Dogecoin,” Carter Feldman, CEO of Psy Protocol, said. “This demonstrates the promise of combining best-in-class security with user demand and an established developer base.” “We have been working hard to find ways to better serve the Doge community, and now we get the chance to offer them even greater utility for their Dogecoin,” Carter Feldman, CEO of Psy Protocol, said. “This demonstrates the promise of combining best-in-class security with user demand and an established developer base.” “We’re thrilled to announce the DOGE bridge to Solana, a big step in welcoming one of crypto’s most iconic communities to the Solana ecosystem,” said Lily Liu, President of the Solana Foundation. “DOGE, Bitcoin’s beloved pet, embodies the fun, irreverent spirit that drives on-chain culture. By bridging DOGE into Solana’s network, we’re inviting the Dogecoin community—and all OG crypto enthusiasts—to join us in marrying on-chain culture and decentralized finance.” “We’re thrilled to announce the DOGE bridge to Solana, a big step in welcoming one of crypto’s most iconic communities to the Solana ecosystem,” said Lily Liu, President of the Solana Foundation. “DOGE, Bitcoin’s beloved pet, embodies the fun, irreverent spirit that drives on-chain culture. By bridging DOGE into Solana’s network, we’re inviting the Dogecoin community—and all OG crypto enthusiasts—to join us in marrying on-chain culture and decentralized finance.” "Trustless verification has always been the holy grail of interoperability, but achieving it at scale has remained elusive,” said Robinson Burkey, co-founder of Wormhole. “Seeing Psy and Wormhole come together to build this around an asset like DOGE captures the true cyberpunk spirit of crypto. We’re excited to help bring a $36B asset to Solana—soon to be powered by Wormhole." "Trustless verification has always been the holy grail of interoperability, but achieving it at scale has remained elusive,” said Robinson Burkey, co-founder of Wormhole. “Seeing Psy and Wormhole come together to build this around an asset like DOGE captures the true cyberpunk spirit of crypto. We’re excited to help bring a $36B asset to Solana—soon to be powered by Wormhole." How the Bridge Works The bridge captures and verifies each Dogecoin block header on Solana. Block headers contain essential blockchain data, including the previous block hash, timestamp, difficulty target, the Merkle root of all transactions in the block, and the Proof of Work consensus algorithm. By verifying these headers directly on Solana, the system cryptographically confirms the validity of Dogecoin transactions without intermediaries. When users send DOGE to the bridge, the system verifies the deposit on the Dogecoin blockchain and mints an equivalent amount of QDOGE tokens on Solana. To convert back, QDOGE tokens are burned on Solana, with withdrawal messages securely transmitted through Wormhole’s cross-chain messaging protocol, triggering the release of the original DOGE to the user's Dogecoin address. To enable this bridge, Psy has created a suite of infrastructure for developers: txindex: a fully-featured indexer for Dogecoin with effortless handling of forking behavior
electrs-doge: the first open-source block explorer for Dogecoin
doge-sdk: the first JavaScript SDK for Dogecoin
forkr: an easy-to-use tool for simulating forks/re-orgs on Bitcoin and Dogecoin. txindex: a fully-featured indexer for Dogecoin with effortless handling of forking behavior electrs-doge: the first open-source block explorer for Dogecoin doge-sdk: the first JavaScript SDK for Dogecoin forkr: an easy-to-use tool for simulating forks/re-orgs on Bitcoin and Dogecoin. About Psy Protocol Psy is the leading innovator in next-generation proof-of-work technology, on a mission to restore the security and decentralization principles of blockchain while enabling modern scalability. Psy is bridging the utility gap between Proof of Work and Proof of Stake chains, empowering developers to build hyper-scalable web3 applications, to provide a credible alternative to a centralized internet controlled by a handful of tech monopolies. Psy Psy Contact Mr Josh Adams Serotonin josh@serotonin.co This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. Program Program

Psy Develops First Trustless Bridge from Dogecoin To Solana

Psy تطوير أول جسر دون ثقة من Dogecoin إلى Solana

“Psy” “Dogecoin”dan “Solana”ya ilk güvənimsiz köprüyü yaradıb

Psy desenvolupa el primer pont sense confiança de Dogecoin a Solana

Psy desarrolla el primer puente sin confianza de Dogecoin a Solana

Psy desenvolve a primeira ponte sen confianza de Dogecoin a Solana

PsyがDogecoinからSolanaへの最初の信頼のない橋を開発

Psy შექმნა პირველი Trustless Bridge from Dogecoin to Solana

Psy разрабатывает первый недоверчивый мост от Dogecoin до Solana

Psy, Dogecoin ல் Solana ல் முதன்முதலாக நம்பிக்கை இல்லாத Bridge உருவாக்கப்பட்டது

Psy ay nagtatrabaho ng unang Trustless Bridge mula sa Dogecoin sa Solana

Psy розробляє перший недовірливий міст від Dogecoin до Solana

I-Psy Izakhiwo I-Primary Trustless Bridge kusuka ku-Dogecoin kuya ku-Solana

Public relations just changed. Public relations just changed Google I/O 2025 confirmed what many of us in communications already suspected. Generative AI is no longer emerging. It is now embedded in how people discover, evaluate, and trust brands. Sundar Pichai’s keynote at Google I/O marked a definitive shift. The introduction of Google’s AI Overviews changes how information is surfaced. The search engine results page as we know it is evolving. Blue links are becoming footnotes. The answers users see are now synthesized by generative models, and those models are pulling from earned media, owned content, and structured brand assets to generate results. If you are in PR, or rely on PR to drive visibility, your playbook is already outdated. What used to work is no longer enough. What Generative AI Means for PR and Marketing Firms Generative AI tools like ChatGPT, Gemini, Perplexity, and Claude are now active participants in how the public perceives your brand. They do not just link to sources. They generate responses. This means your brand’s visibility depends on whether these systems recognize your content, trust your sources, and understand your positioning. active participants in how the public perceives your brand Here is what has changed and what PR leaders must account for: 1. PR Strategy Must Now Be Built for AI-First Discovery Google’s AI Overviews do not simply reward keyword optimization. They favor semantic clarity, structured insights, and reputational weight. Press releases, contributed articles, and media mentions must be AI-readable and semantically rich to surface in these overviews. semantic clarity, structured insights, and reputational weight Traditional SEO is no longer enough. If your content is not interpreted correctly by generative models, it may never be seen in response to key brand-related queries. 2. Trusted Media Coverage Matters More Than Ever AI models prioritize content from authoritative, verified sources. That means earned media coverage in credible publications is now a critical input into how your brand is summarized and presented in generative search. earned media coverage in credible publications is now a critical input into how your brand is summarized and presented Building and maintaining strong media relationships is no longer optional. It is essential to brand survival in AI-driven environments. 3. PR, Content, and Search Must Now Be Synchronized Most companies still treat PR, content marketing, and SEO as separate functions. In the generative AI era, those lines are gone. You need: Earned media coverage to build authority
Owned content (like blogs, whitepapers, and executive POVs) to reinforce key topics
Search-aware structuring so AI models can connect the dots Earned media coverage to build authority Earned media coverage Owned content (like blogs, whitepapers, and executive POVs) to reinforce key topics Owned content Search-aware structuring so AI models can connect the dots Search-aware structuring When done well, this ecosystem creates compounding visibility across search engines, AI tools, social media, and dark social environments. compounding visibility 4. Key Channels That Now Shape AI Visibility **Trade media \ Niche industry publications carry significant weight in AI systems. Their subject-matter depth signals authority. Being quoted in a trade outlet may now be more valuable than broader mainstream coverage. **Press releases \ AI systems treat press releases as source material. Structured, scannable releases with clear headlines and summaries help these systems understand what your brand stands for. **Owned content \ Long-form articles, FAQs, and resource pages provide persistent signals about your brand’s expertise. These are often what AI pulls from to generate summaries and recommendations. **Social media \ Engagement patterns on platforms like LinkedIn and X (Twitter) are increasingly reflected in generative search responses. Consistent thought leadership strengthens topical authority. What CMOs and Communications Leaders Must Do Now Shift from measuring PR by impressions to measuring by visibility in generative environments
Invest in media placements that AI models reference, not just what makes headlines
Create content with semantic structure and persistent topical focus
Ensure messaging is consistent across earned media, owned platforms, and social content Shift from measuring PR by impressions to measuring by visibility in generative environments measuring by visibility in generative environments Invest in media placements that AI models reference, not just what makes headlines media placements that AI models reference Create content with semantic structure and persistent topical focus semantic structure and persistent topical focus Ensure messaging is consistent across earned media, owned platforms, and social content earned media, owned platforms, and social content Your brand may be trusted by customers and partners. But if it is not trusted or understood by generative AI systems, you are losing critical surface area in the discovery journey. Actions PR and Marketing Teams Should Take Immediately Audit existing earned media and owned content to assess how it shows up in generative search tools
Align content calendars with high-impact search themes, not just seasonal moments
Build relationships with media outlets that are frequently cited by AI platforms
Introduce AI visibility scoring into your reporting workflows to benchmark progress Audit existing earned media and owned content to assess how it shows up in generative search tools Align content calendars with high-impact search themes, not just seasonal moments Build relationships with media outlets that are frequently cited by AI platforms Introduce AI visibility scoring into your reporting workflows to benchmark progress This is not a future-state recommendation. These systems are already shaping purchasing decisions, investor research, and media due diligence today. Generative AI is not a threat to PR. It is a signal that strategic communications must evolve to meet the way audiences now discover and evaluate brands. strategic communications must evolve The firms and leaders who adapt early will not just protect their visibility. They will dominate it. If you lead marketing, PR, or executive visibility for a brand and want to better understand how to build for AI-powered discovery, I welcome the conversation. You can connect with me on LinkedIn or reach out directly with questions.

How Generative AI Is Reshaping PR Strategy, Brand Visibility, and Media Influence in 2025

কিভাবে জেনারেটিভ এআই ২০২৫ সালে প্রাইভেট কৌশল, ব্র্যান্ড দৃশ্যমানতা এবং মিডিয়া প্রভাব পুনর্নির্মাণ করছে

Wie Generative AI die PR-Strategie, die Markensichtbarkeit und den Einfluss der Medien im Jahr 2025 neu gestaltet

Cómo la IA generativa está remodelando la estrategia de relaciones públicas, la visibilidad de la marca y la influencia de los medios en 2025

Comment l’intelligence artificielle générative remodèle la stratégie des relations publiques, la visibilité de la marque et l’influence des médias en 2025

कैसे जनरेटिव एआई 2025 में पीआर रणनीति, ब्रांड दृश्यता और मीडिया प्रभाव को फिर से आकार दे रहा है

Generative AIが2025年にPR戦略、ブランドの可視性、メディアの影響力をどのように再構築しているか

Generative AI가 2025년 PR 전략, 브랜드 가시성 및 미디어 영향력을 어떻게 재구성하는가

Como a IA geradora está reformulando a estratégia de relações públicas, a visibilidade da marca e a influência da mídia em 2025

Как генеративный ИИ переоформляет стратегию PR, видимость бренда и влияние СМИ в 2025 году

Generative AI, 2025'te PR Stratejisini, Marka Görünürlüğünü ve Medya Etkilerini Nasıl Yeniden Tasarlıyor

Làm thế nào Generative AI đang định hình lại chiến lược PR, khả năng hiển thị thương hiệu và ảnh hưởng của phương tiện truyền thông vào năm 2025

生成型人工智能如何在2025年重新塑造公共关系战略、品牌知名度和媒体影响力

We all hear the “reasons why.” Industries run on them. “We prioritize X because it’s efficient.” “Our media covers Y because it’s newsworthy.” “This is the path to success because it’s proven.” For years, I nodded along, trying to fit my own entrepreneurial journey into these accepted narratives. But something always felt… off. The stated reasons rarely told the whole story. It wasn’t until I cultivated a different kind of habit — not just asking “why,” but engaging in Deep Dialogue — that I began to understand the unspoken logic, the invisible rules, that truly govern how industries operate. This wasn’t about aggressive questioning; it was about a genuine, almost anthropological curiosity explored through conversation. And it’s a habit that not only built my resilience but fundamentally shaped how I built my business, MotivBase. Many of us are taught to be skeptical, to question. But often, that just leads to a surface-level debate or makes us “that annoying person.” The real magic happens when you move beyond simple interrogation and into a deeper conversational inquiry. It’s about discussing the stated reasons to such an extent that the unspoken drivers, the basic human or systemic building blocks, naturally begin to surface. Consider the world of market research. It’s common for businesses to prioritize neat statistics over nuanced human stories when trying to understand their customers. The stated reason? ‘Stats are quantifiable and scalable.’ But if you engage in deep dialogue with market researchers and the executives they report to, you start to uncover a different layer of truth. You move past the surface justification to understand that this preference is often about the intense pressure for defensible certainty in high-stakes presentations. It’s about the severely limited time decision-makers have, and an organizational culture where easily digestible numbers are equated with analytical rigor. The unspoken driver isn’t purely about the best data; it’s deeply intertwined with professional safety, navigating internal power structures, and the need to communicate in a way that feels efficient and ‘safe’ to those in charge. Or consider tech media’s fascination with who raised how much money, often overshadowing businesses achieving steady, profitable growth. A deep dialogue reveals it’s not just about “news.” It’s about the media’s role in an ecosystem that craves unicorn narratives, their need to “call it first” for status, and a definition of success heavily influenced by venture capital’s rhythms. My own journey is littered with moments where this habit was crucial. When I was building MotivBase, an AI-powered cultural intelligence platform, the venture capital world repeatedly told me it wouldn’t scale. Their stated reasons were often variations of: “It’s too niche,” “Anthropology isn’t tech,” or “The market isn’t ready for this.” If I had just accepted those statements, or argued against them directly, I wouldn’t have gotten far. Instead, I engaged in (sometimes painful) dialogues. I’d ask, “Help me understand what ‘scalable’ looks like from your perspective?” or “What are the markers you typically see in a business you believe will achieve massive growth?” Through these conversations, the unspoken drivers began to emerge. It wasn’t just about my business model; it was about their models. It was about pattern recognition based on their past successes (and failures), the pressure they faced from their LPs to find billion-dollar outcomes, and an industry built on a certain archetype of “tech founder” and “tech solution.” The invisible rule wasn’t “your idea is bad,” but “your idea doesn’t fit the template that minimizes our risk and maximizes our specific kind of reward.” Understanding this unspoken logic didn’t necessarily make them invest. But it did something far more valuable: it gave me clarity and resilience. It helped me understand that their “no” wasn’t always a reflection of my venture’s potential, but often a reflection of the invisible rules governing their world. This understanding allowed me to seek alternative paths, to bootstrap, to build credibility through evidence and client success, rather than solely through external validation that wasn’t designed for a business like mine. It allowed us to focus on what truly mattered: solving real problems for clients who did see the value, eventually leading to a successful exit. This habit of deep dialogue isn’t about winning an argument. It’s about understanding the terrain. It’s about seeing the hidden currents that shape what people believe is possible, valuable, or credible. It’s a cornerstone of navigating the often-unspoken, invisible rules I explore in my upcoming book, “Invisible Rules: How to Outsmart the Entrepreneurial Game.” It’s a practice that allows you to move with empathy, to build strategies grounded in a deeper reality, and ultimately, to carve your own path, even when the well-trodden ones don’t seem to lead where you want to go. Want early access to my book? Join the Inner Circle Join the Inner Circle

The Startup Playbook Is a Lie. Ask Better Questions.

Die start-up spelboek is 'n leuen. vra beter vrae.

“The Startup Playbook Is a Lie” – Daha yaxşı soruşma.

Das Startup Playbook ist eine Lüge, stellen Sie bessere Fragen.

El Playbook de Startup es una mentira, plantee mejores preguntas.

O Playbook de Startup é unha mentira, fai mellores preguntas.

The Startup Playbook Is a Lie – שאל שאלות טובות יותר.

Startup Playbook se yon minis. Ask pi bon kesyon.

The Startup Playbook Is a Lie. より良い質問をしてください。

The Startup Playbook Is a Lie. د ښه پوښتنو پوهیږئ.

Startup Playbook este o minciună; pune întrebări mai bune.

Mchezo wa Startup ni uongo. kuuliza maswali bora.

O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z.

Win SEO in the Age of LLMs: Strategies for AI Visibility

Authors:
(1) Muhammad Zia Hydari, Katz Graduate School of Business, University of Pittsburgh and Corresponding author;
(2) Idris Adjerid, Pamplin College of Business;
(3) AAaron D. Striegel, Department of Computer Science and Engineering, University of Notre Dame. Authors: Authors: (1) Muhammad Zia Hydari, Katz Graduate School of Business, University of Pittsburgh and Corresponding author; (2) Idris Adjerid, Pamplin College of Business; (3) AAaron D. Striegel, Department of Computer Science and Engineering, University of Notre Dame. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Background and 2.1. Leaderboards 2. Background and 2.1. Leaderboards 3. Effect of Leaderboards on Healthful Physical Activity and 3.1. Competition 3. Effect of Leaderboards on Healthful Physical Activity and 3.1. Competition 3.2. Social Influence 3.2. Social Influence 3.3. Moderating Effects of Prior Activity Levels and Leaderboard Size 3.3. Moderating Effects of Prior Activity Levels and Leaderboard Size 4. Data and Model 4.1. Data 4.1. Data 4.2. Model 4.2. Model 5. Estimation and Robustness of the Main Effects of Leaderboards 5. Estimation and Robustness of the Main Effects of Leaderboards 5.2. Robustness Check for Leaderboard Initiation 5.2. Robustness Check for Leaderboard Initiation 5.3. Fitbit Compliance 5.3. Fitbit Compliance 5.4. Fitbit Attrition, Leaderboard De-Adoption, and Additional Robustness Checks 5.4. Fitbit Attrition, Leaderboard De-Adoption, and Additional Robustness Checks 6. Heterogeneous Effect of Leaderboards 6. Heterogeneous Effect of Leaderboards 6.1. Heterogeneity by Prior Activity Levels 6.1. Heterogeneity by Prior Activity Levels 6.2. Interaction of Leaderboard Size, Rank, and Prior Activity Levels 6.2. Interaction of Leaderboard Size, Rank, and Prior Activity Levels 6.3. Summary of Findings from Heterogeneous Effect Analysis 6.3. Summary of Findings from Heterogeneous Effect Analysis 7. Conclusions and Discussion, Endnotes, and References 7. Conclusions and Discussion, Endnotes, and References 3.3. Moderating Effects of Prior Activity Levels and Leaderboard Size The contradictory effects of competition and social influence not only make the direction of the average effect uncertain, they also point to the presence of heterogeneity in the effects. To untangle this heterogeneity, we consider factors that can impact the propensity for observing the positive vs. negative dynamics of leaderboards on physical activity. 3.3.1. Leaderboard Size. First, we consider whether leaderboard size, that is, the number of other active participants on the leaderboard, is an important potential moderator of leaderboard impacts. Garcia et al. (2013) suggest that an important situational factor impacting comparison concerns and competitiveness is the number of competitors. On the one hand, increasing the number of active participants is likely to increase the likelihood of the positive dynamics that leaderboards introduce. Clearly, the mechanisms of competition, mutual accountability, and changes in perceived ability are nonexistent if there are no other active users on a leaderboard. More so, competitive motives may be stronger on larger leaderboards because ranking highly on larger leaderboards can be more motivational than dominating smaller leaderboards. That said, the effect of increasing leaderboard size is likely more nuanced. For instance, it is likely that some benefits of additional leaderboard participants are diminishing at the margin. Too many participants can make the leaderboard less effective because participants get lost in the crowd, weakening the positive impacts of competition or mutual accountability (Garcia and Tor 2009, Garcia et al. 2013). The diminishing marginal benefit of an additional leaderboard member implies nonlinearity in the benefit of more leaderboard members and may even lead to harmful effects of leaderboards if they become too large. 3.3.1. Leaderboard Size. 3.3.2. Prior Activity Levels. Second, we consider the physical activity level of an individual prior to leaderboard adoption. 3.3.2. Prior Activity Levels. 3.3.2.1. Competition. If we consider only the role of competition (vis-a`-vis prior activity levels), the expectation in the literature that highly active individuals should benefit disproportionately from leaderboards is most plausible (Patel et al. 2015, Wu et al. 2015, Shameli et al. 2017). Individuals with high activity levels prior to leaderboard adoption gain high utility from healthful activity and thus are likely to perform well on leaderboards. This positive performance on leaderboards can be motivational for them and encourage increases in future physical activity. The impact of competitive dynamics on relatively more sedentary individuals may be more nebulous. On the one hand, these individuals may benefit most from extrinsic motivators such as competition and ranking themselves against others. On the other hand, the value of leaderboards for such individuals may be limited by their lower intrinsic aptitude and motivation for physical activity. This leaves them prone to de-motivational impacts of lackluster performance on leaderboards. 3.3.2.1. Competition. 3.3.2.1. Competition. 3.3.2.2. Accountability and Reference Points. If we also consider theorized mechanisms related to social influence, the expectation ex ante is more uncertain. Because individuals on the low end of the physical activity distribution are more likely to have other leaderboard participants who are more active than they are, there is increased potential for the leaderboard to act as a tool that keeps them accountable; individuals who are more active than the focal user may be more credible in their attempts to hold the focal user accountable. More so, individuals at the lower end of the physical activity distribution are more likely to encounter other users who facilitate upward comparisons and positively impact their reference point for exercise and their perceived ability to engage in physical activity. In addition, individuals with low activity levels prior to leaderboard adoption may benefit most from leaderboards because they have more room for improvement and a higher need for external motivation. The dynamics around social influence are somewhat reversed for those who are highly active prior to leaderboard adoption. Following the same rationale, individuals who are already highly active may be less likely to join leaderboards where other users can hold them accountable (i.e., few others on their leaderboard can match their physical activity levels). In addition, these individuals are at elevated risk of leaderboards facilitating downward comparisons that negatively impact their exercise reference points. These comparisons can induce sluggishness if they highlight the focal user’s disproportionate level of activity compared with others. Finally, highly active individuals may suffer from ceiling effects, that is, any extrinsic intervention is not likely to increase their willingness or ability to increase physical activity. 3.3.2.2. Accountability and Reference Points. 3.3.2.2. Accountability and Reference Points. 3.3.3. Leaderboard Mechanisms, Prior Activity Levels, and Leaderboard Size. The theorized effects of prior activity levels and leaderboard size can also intersect in ways that have implications for the diverse mechanisms through which leaderboards can impact behavior. First, our theorized mechanisms point to highly active individuals being most likely to be harmed by smaller leaderboards. Garcia et al. (2013) suggest that competitiveness emerges when there is a potential for comparisons, up or down, that credibly threaten the individual’s rank. With smaller leaderboards (e.g., one other individual), these high achievers are less likely to interact with another individual who can credibly compete with them or hold them accountable (thus nullifying two key mechanisms for leaderboard value). At the same time, they are more likely to be presented with a salient individual who facilitates downward comparisons that negatively impact their exercise reference point, induce sluggishness, and diminish their physical activity levels. As leaderboard size increases, there is increased potential value for the highly active because the likelihood increases of at least one individual joining who can provide a credible threat to their rank, mutual accountability, and positive impacts on their reference point for exercise. Furthermore, it is plausible that individuals who are highly active are buoyed to perform even better when part of a relatively large leaderboard. This phenomenon would be akin to the idea in some sports of a “big match player,” someone who performs above their average on big occasions and in front of big crowds. 3.3.3. Leaderboard Mechanisms, Prior Activity Levels, and Leaderboard Size. In contrast, our theorized mechanisms have different implications for leaderboard size when individuals were sedentary prior to adoption. Unlike highly active individuals, these individuals can still benefit from adopting small leaderboards because they are still likely to encounter other users who are either at a comparable or a higher level of physical activity. Thus, even small leaderboards may often provide these individuals with an additional degree of accountability and the potential for positive impacts on their exercise reference points. Whether these individuals benefit from competition with small leaderboards is less certain, as they may still be dominated on small leaderboards, leading to de-motivational effects of competition. Increasing the size of the leaderboards for lower activity users may still provide some of the benefits described previously but it is likely that these benefits diminish faster for this group. Unlike for highly active individuals, benefits of mutual accountability may be reduced for these individuals as leaderboard size increases (via the “getting lost in the crowd” phenomenon described previously). More so, these users, who are at the lower end of the distribution of physical activity, may be more likely to get stuck toward the bottom of larger leaderboards and this may be more salient with more users participating. Overall, we conjecture that sedentary individuals can significantly benefit even when leaderboards are small. However, increases in leaderboard size may have diminishing marginal benefit for them. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Power of Prior Activity and Group Size in Digital Health Leaderboards

Authors:
(1) Filipo Sharevski, DePaul University;
(2) Benjamin Kessell, DePaul University. Authors: Authors: (1) Filipo Sharevski, DePaul University; DePaul University (2) Benjamin Kessell, DePaul University. DePaul University. Table of Links Abstract and Introduction Abstract and Introduction 2 Internet Activism and Social Media 2.1 Hashtag Activism 2.1 Hashtag Activism 2.2 Hacktivism 2.2 Hacktivism 3 Internet Activism and Misinformation 3.1 Grassroots Misinformation Operations 3.1 Grassroots Misinformation Operations 3.2 Mainstream Misinformation Operations 3.2 Mainstream Misinformation Operations 4 Hacktivism and Misinformation 4 Hacktivism and Misinformation 4.1 Research Questions and 4.2 Sample 4.1 Research Questions and 4.2 Sample 4.3 Methods and Instrumentation 4.3 Methods and Instrumentation 4.4 Hacktivists’ Profiles 4.4 Hacktivists’ Profiles 5 Misinformation Conceptualization and 5.1 Antecedents to Misinformation 5 Misinformation Conceptualization and 5.1 Antecedents to Misinformation 5.2 Mental Models of Misinformation 5.2 Mental Models of Misinformation 6 Active Countering of Misinformation and 6.1 Leaking, Doxing, and Deplatforming 6 Active Countering of Misinformation and 6.1 Leaking, Doxing, and Deplatforming 6.2 Anti-Misinformation “Ops” 6.2 Anti-Misinformation “Ops” 7 Misinformation Evolution and 7.1 Counter-Misinformation Tactics 7 Misinformation Evolution and 7.1 Counter-Misinformation Tactics 7.2 Misinformation Literacy 7.2 Misinformation Literacy 7.3 Misinformation hacktivism 7.3 Misinformation hacktivism 8 Discussion 8.1 Implications 8.1 Implications 8.2 Ethical Considerations 8.2 Ethical Considerations 8.3 Limitations and 8.4 Future Work 8.3 Limitations and 8.4 Future Work 9 Conclusion and References 9 Conclusion and References 6 Active Countering of Misinformation Literature on misinformation focuses on helping the social media users discern falsehoods with strategies for “prebunking” i.e. forewarning and preemptive refutation of the falsehoods [68] or “debunking” i.e. providing users verifiable corrections of the falsehoods from credible sources to break the illusion of truth [30, 93]. An algorithmic is also available for the mainstream social media platforms (the alternative ones do not deem misinformation as a problem [107]) that leverages natural language processing, image analysis, or metadata to detect trolling and memes [50,51,122]. Platforms also have the option for algorithmic “soft” moderation by either obscuring trolling and memes with warnings covers or attaching warning labels [108, 125] and “hard” moderation for removing or suspending misinformer accounts [63]. None of these solutions, however fends of troll farms and meme disseminators effectively, so we wanted to know what hacktivists have to propose instead in the second research question. 6.1 Leaking, Doxing, and Deplatforming Suspending user accounts by social media platforms for breach of their code of conduct is referred to as “deplatforming” [1]. In the context of hacktivism, it takes a border meaning as hacktivists do investigative work that entails leaking and doxing but also confrontation with the misinformers that, in their subjective view, breaches the vision of democratic Internet. For example, hacktivists did a massive API scrapping of the alt-platform Parler to leak data that tied users to the Capitol Riots and the QAnon conspiracy [94], which in turn resulted in a massive account deplatforming on Twitter [15]. These activities spur operations to confront and expose the QAnon conspirators on social media (e.g. @QAnonAnonymous [22]), amongst which some of our hacktivists have a direct role in “dismantling the Qanon infrastructure” [P2]. “dismantling the Qanon infrastructure” P2 The deplatforming targets political misinformation campaigns where our hacktivists “compiled and leaked dossiers on individuals spreading hateful propaganda and those who seek to sow the seeds of violence” [P1] on social media. These operations were targeted both on “individual spreaders, nation-states, even companies with murky records” [P2]. Several mentioned their direct operations for exposing disinformation relative to the “Ukrainian conflict” [P5], praising the work of the Ukrainian IT Army outfit for dispelling the myth that Ukraine is committing genocide against Russians in the Donbas region [23]. Hacktivists were dedicated in “doxing companies and governmental agencies in response to the political meddling in the US internal affairs from places like Russia, Iran, and China” [P8]. Misinformation “sanctioned by the governments” was targeted by hackers in attempts to deplatform prominent “disinformation front agents on social media, like Irina Tsukerman, for example” [P3]. “compiled and leaked dossiers on individuals spreading hateful propaganda and those who seek to sow the seeds of violence” P1 “individual spreaders, nation-states, even companies with murky records” P2 “Ukrainian conflict” P5 “doxing companies and governmental agencies in response to the political meddling in the US internal affairs from places like Russia, Iran, and China” “sanctioned by the governments” “disinformation front agents on social media, like Irina Tsukerman, for example” P3 Leaks and doxing were equally utilized for misinformation beyond political counter(argumentation) and external propaganda. One of the hacktivists has dedicated considerable time on exposing cryptocurrency scammers on social media and elsewhere, deeming the feeling of it as “better than sex” [P5]. Another was focused leaking personal details about predators on social media that spread misinformation to cover their sexual harassment and cyberstalking towards women, “exposing both their sock puppet accounts and their real name on Twitter” [P3]. Another pushed back against criminal misinformation by doxing “bullies, liars, and fraudsters” [P20] and one “anti-cancel culture in case of minors” hacktivist noted that they “successfully deplatformed major participants in hate campaigns and stalking of minors” on social media [P5]. P5 “exposing both their sock puppet accounts and their real name on Twitter” P3 P20 “anti-cancel culture in case of minors” “successfully deplatformed major participants in hate campaigns and stalking of minors” P5 6.2 Anti-Misinformation “Ops” The hacktivists in our sample engaged in misinformation saturation ops, true to the their commitment to “fight misinformation with more information.” One of the hacktivists stated that it is “expected from the hacktivist community to combat misinformation in such a way” and noted that “it is the sole reason they maintain a Twitter account” [P3]. Another one seconded this posture noting that “it is frustrating to see misinformation from others and other creators but that is the main reason I continue to post on TikTok” [P17]. In the words of P2, “there is more ideological aspect of it when I am fighting disinformation,” directly invoking the mission of the true hacktivists to become reflexively “loud and determined” to speak true information in response to the “general assholery of misinformation on internet” [P9]. “fight misinformation with more information.” “expected from the hacktivist community to combat misinformation in such a way” P3 “it is frustrating to see misinformation from others and other creators but that is the main reason I continue to post on TikTok” P17 P2 “there is more ideological aspect of it when I am fighting disinformation,” “loud and determined” “general assholery of misinformation on internet” P9 Partaking in operation #NAFO (North Atlantic Fellas Organization) dedicated to countering Russian propaganda and disinformation in Ukraine by weaponizing memes [103], our participants materialized a combination of saturation and doxing to “curtail misinformers’ ability to gain followers” [P1]. They extended their work to counter “extremists and fascists and their toxic conspiracy theories” [P1] by disrupting their funding and deplatforming prominent followers, true to the spirit of the “Antifa” hacktivist counterculture [131]. In a similar vein, one of the hacktivists proclaimed that they “greatly contributed in the #OpJane operation” [P10]. #OpJane is the latest operation launched by Anonymous against Texas for enacting the anti-abortion Bill 8 that allows “abortion bounty” for anyone who will investigate and report abortion in the state of Texas [38]. Interestingly, in the announcement of the operation, Anonymous calls for “fighting misinformation with enough plausible and difficult to disprove misinformation” to make any data these bounty hunters gather as useless [5]. “curtail misinformers’ ability to gain followers” P1 “extremists and fascists and their toxic conspiracy theories” “greatly contributed in the #OpJane operation” P10 “abortion bounty” fighting misinformation with enough plausible and difficult to disprove misinformation” This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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