Teddy Web3

Inspiration

Pew Research Center found 68% of Americans say fake news undermines their confidence in government institutions. This is 8% largers than last year and 56% believe the problem will only get worse over time. Pew also found that heavily biased news is accelerating polarization as the more clickbait or eye-catching a headline is, the more views it attracts. With the tremendous rise in the perceived legitimacy of this new source, social media platforms have struggled to ensure credibility. These ideological silos can serve as breeding grounds for fake news, as stories designed to mislead their audience are circulated within the target political community, building outrage and exacerbating ignorance with each new share. Fake News devalues and delegitimizes voices of expertise, authoritative institutions, and real news. In a democratic world, this undermines society’s ability to engage in rational discourse based upon shared facts regarding subjects like health, war, and policy making.

Fake news is a significant problem as the scale of those affected is large because media is in every language and used to communicate information around the world. Teddy web3 plays a part in assessing a system and potential changes to it as they relate to relevant and interested parties with constantly changing information. This information is used to assess how the stake and impact of those stakeholders should be addressed in a project plan, policy, program, or other action in technology.

Defining the Problem of “Fake News”

Mis-information - false information shared with no intention of causing harm Dis-information - false information shared intentionally to cause harm Mal-information - true information shared intentionally to cause harm.

Due to anonymity on the Internet, and the rapid sharing of information over social media, it is difficult to determine who the Media Stakeholders for fake news would be:

Internet service provider; Newspaper; Broadcaster; Telecommunications service provider; An internet intermediary service; A telecommunication service; A service of giving public access to the internet; A computing resource service. Actions include the following

What it does

This proposal recommends policies to decrease misinformation over social media in an internet dominated world. Fake news has the potential to completely divide public opinion, promote violent extremism and hate speech leading to war. In democratic societies, it reduces trust in the democratic processes. A strong democracy relies on everyone having access to credible news so that they can form opinions. The goal of the proposed solution is to actively fight disinformation on social media platforms with the implementation of innovative technology solutions that allow equitable access to real news with three policy recommendations. Teddy Web3 algorithmically identifies fake news over social media to help fight misinformation.

​​Covid19 Example: Facemasks are dangerous are childern, eating bats is going kill a person, vitamin-C is the elixir to Coronavirus, and the anti-vaccination movement. It is Hard for humans to detect fake news. Humans have an unconscious tendency to bias attention towards negative news with examples such as the British press on Price Harry’s wedding. According to Dr. Nyilasy of the University of Melbourne*, it is particularly difficult for people to detect fake news. This is because it resembles real news and we then unconsciously trust it due to pattern recognition (https://pursuit.unimelb.edu.au/articles/fake-news-in-the-age-of-covid-19). Therefore, strong public confidence in science and guidance on how to detect fake news is critical to countering its destructive force. This level of public confidence is crucial for the successful deployment of technologies to combat COVID-19. Furthermore, it is essential to encouraging and ensuring acceptance of difficult policies such as social distancing, personal protection, and many other measures that impact the daily lives of everyone. Why Humans can’t solve this problem on their own? We Need Machine Technology With blockchain AI technology, Teddy empowers citizens to recognise and trust correct scientific information regarding COVID-19 and for future pandemics. Example: President Donald Trump suggested that hydroxychloroquine could be a potential cure for COVID-19. Presidential Elections Example: Research shows elites, mass media play important role in spreading misinformation on mail-in voter fraud with voter fraud are disproportionately held by Republican voters, which is reflective of the deeply asymmetric media ecosystem. For instance voter fraud by mail-in ballots is rare. Yet claims of “mail-in voter fraud” are spreading through mainstream media, cable and local television, and on social media sites in the lead up to the 2020 U.S. presidential election. Case of political instability can lead to cases like the Pizzagate conspiracy theory where it lead to a murder based on fake news and political polaization. Within our current political climate, it is important to limit the spreading of fake news. Deepfake Videos Example: House Speaker Nancy Pelosi video was widely shared among the highest levels of government, giving the impression she was intoxicated in an interview. Deepfakes are a new method to impersonate famous figures saying fictional things, and could be particularly influential in the outcome of this and future elections. Satire Parady videos Example: The Onion, Waterford Whispers, and the Daily Mash re examples of websites and social meia acccounts that publish fake news for entertainments as humours attempts or to satirize the media but some people fall for it. Russia vs Ukraine war Example: Fighting Fake News as the Next Generation of Propaganda in Online Warfare. Worldwide media calls invasion of Ukraine "War" but Russia media calls it a "special operation" with "limited causalities " Misinformation can also be weaponized. Russia’s invasion of Ukraine caused Europe's largest refugee crisis since World War II, with more than 9 million Ukrainians fleeing the country and a third of the population displaced. Russia passed a law that threatens prison time for anyone publishing what authorities consider to be false information about the country’s invasion of Ukraine, which the Kremlin refers to as a special military operation. The invasion also caused global food shortages. Using Social Media data and Teddy Web3’s machine learning algorithm we can analysis how fake news can be used as a form of propaganda in online warfare. Examples contains posts and discussions from a russian-based social media platform VKontakte where a lot of propaganda is spread. Misinformation can also be weaponized, as seen with current Russian propaganda amid their invasion of Ukraine like "Lügenpresse" or "lying press," was invoked by the Nazis in the 1930s

Recommendation Models lead to Echo Chambers Control Your Algorithm with Teddy Web3 : A.I. Recommendation Models such as Facebook’s new feed algorithm can lead to echo chambers. For instance, the situation with Cambridge Analytica had suffered a data breach and that millions of Facebook users’ information was being harvested in order to provide targeted political advertising. Control Your Algorithm with Teddy Web3 : We help people break free from the harmful echo chambers of biased news by intuitively displaying the bias and accuracy of news articles without extra effort from the consumer. They say we’re a reflection of the 5 closest people to us. It’s so easy to get comfortable in our own echo chambers, lost in opinions that seem so real because they’re all around us.

We help people break free from the harmful echo chambers of biased news by intuitively displaying the bias and accuracy of news articles without extra effort from the consumer. They say we’re a reflection of the 5 closest people to us. It’s so easy to get comfortable in our own echo chambers, lost in opinions that seem so real because they’re all around us.

https://docs.google.com/document/d/1AM9vu5xOyLDcLztIJvUMkaXA92t1xuJ78FCrkItKdXg/edit

How we built it

We need a scalable solution that can handle tackling misinformation which is why AI blockchain technology was used to fight fake news accounting for Technical Design and Complexity.

Web = Chrome extension: HTML, CSS, and JavaScript to build a web chrome extension which calls our back-end fake news detection with a manifest. json file. Popup view calls background page.

Mobile = News app A.I. = Fake news algorithm: Artifical Intelligence with Google's Tensorflow and a Kaggle dataset. Top 100 Keyword Features Extracted from the English Fake News Data Set where Teddy empowers citizens to recognise and trust correct scientific information Blockchain = IPFS blockchain: Teddy advocates for news integrity as it builds trust on Ethereum's decentralized, immutable, peer-to-peer blockchainnetworks so that fake news via tampering, deepfakes, and edited/photoshopped files can be easily spotted.

A citizen can then review this log of information, comparing news with a history of similar images that we provide.

Policy Memo To address these challenges, we propose policy measures that address the gaps in existing regulation with the following recommendations and policies:

Mandatory use of Fact-Checking Sites and Plug-Ins (like Teddy) in political realms to to gauge the content of news sites statements by public figures Laws being passed. Example Singapore Fake News Laws: Guide to POFMA (Protection from Online Falsehoods and Manipulation Act) which POFMA prohibits the communication of false statements of fact in Singapore. This is also helpful for spam, frauds and prevent the misuse of online accounts and bots (i.e. computer programmes that run automated tasks). Investigaion once Fake News is reported and verified: For instance once it is verified on the blockchain, it would be immutable and ideally reported to the proper authorities. There are many tools for verification, as well as other helpful links in the fight against disinformation. Funding of resources towards worldwide involvement with NGOs working to solve fake news problem in the current media landscape. Examples include First Draft News Tow Center for Digital Journalism Berkman Klein Center for Internet and Society Poynter Neiman Foundation News Literacy Project

Challenges we ran into

Accomplishments that we're proud of

• The tensorflow model with ranking filtering algorithm, sentiment analysis to putput a NUMBER, multinomial NB algorithm, passive aggressive classifier algorithm

What we learned

• learning about the verification process with fake news style, credibility, progoation, and knowledge

What's next for Teddy Web3

• Deploy on google chrome store, chrome = each article has a % number next to it
• Specalized resouces fact checking for specific social media sites like Facebook, Twitter, or Whatsapp.
• Look into deepfakes -website like snopes, where all the human-reported fake news gets published
• TWITTER BOT twitter feed for fact checking fake news
• add different languages
• more social media monitoring
• AI sentiment analysis
• connect people Meet people outside your echo chamber. -revenue model = no ads, search preference, loyalty points

Built With

• css3
• html5
• ipfs
• javascript
• python
• tensorflow