OpinioNect

Let's get your views to the world

Comment

Inspiration

Biased media can always tamper the real events of a story, creating inflammatory messaging to attract more voters for the benefit of a specific faction.

Media and news channel have always been encouraged to display an entire report through

Group Debating and discussions.

Media D-App is the solution to conquer this problem using Blockchain and Machine Learning.

What it does

  1. An unbiased report will be written as an article and we require the public to come forward and propose their questions int the app comment section below the article.
  2. The identity of each commentor will not be kept anonymous. Each question will then be analysed by machine learning and required number of categories will be formed to separate similar questions of public.
  3. Since the app is based upon blockchain, it ensures that the data is tamper-resistant.
  4. Media will obliged to refer the questions suggested by Media D-App since the queries are based on majority of the public.

How we built it

Jaskirat, as our team leader, brings a wealth of experience in backend development and Blockchain technologies to the table. He has adeptly integrated Node.js with Blockchain and IPFS, while also leveraging the News API to efficiently gather data for our projects.

Prabal, with his expertise in frontend development and machine learning, has developed a sophisticated model for comment summarization. He has also taken the lead in crafting an engaging and user-friendly website interface, ensuring that our users have a seamless and enjoyable experience.

Challenges we ran into

How to validate the users and upgrade them to admin level ?

  1. Require the user to provide additional information to verify their identity. This could include asking them to provide a government-issued ID or other identifying information that can be cross-referenced with their web3 account.

  2. Use a reputation system to evaluate the user's past behavior on the platform. This can include looking at their past contributions, such as comments, posts, and other reports, to determine their reliability and credibility.

  3. Implement a feedback system that allows other users to rate the quality and credibility of the report. This can help to establish a user's reputation and provide feedback to the moderation team.

  4. Implement a moderation system to review the user's report before it is published. This can involve having a team of moderators review the report for accuracy and consistency with the platform's guidelines and policies. ( This is an advance idea and could be used after a full fledged model is developed).

Accomplishments that we're proud of

we have completed the project and on time

What we learned

teamwork and new tech stack

What's next for OpinioNect

will launch it soon for use of everyone :)

Built With

Share this project:

Updates