# Problem Statement

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The advent of online social media platforms has revolutionized our daily lives. Instagram, Twitter, YouTube, and Facebook have transitioned from being mere social interaction spaces to becoming crucial sources of information, business opportunities, and education ([1](https://www.leewayhertz.com/blockchain-social-media-platforms/)).&#x20;

*However, the credibility of information on these platforms is increasingly under scrutiny.*&#x20;

Fake news and reviews have a significant economic impact, with the global economic cost of fake reviews estimated at $152 billion ([2](https://www.weforum.org/agenda/2021/08/fake-online-reviews-are-a-152-billion-problem-heres-how-to-silence-them/)).

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CHEQ, a leading authority in Go-to-Market Security, reports that a staggering 40% of all web traffic is attributed to bots and invalid users ([3](https://cheq.ai/)). This prevalence undermines marketing efficiency, interferes with on-site conversion strategies, and causes inaccuracies in data and analytics. For instance, Google faced a lawsuit over a reviewer who created more than 350 fake business profiles and posted 14,000 fake reviews ([4](https://www.forbes.com/sites/maryroeloffs/2023/06/30/5-star-fines-fake-reviews-could-cost-companies-under-ftc-proposal/?sh=225518e05dc6)). Amazon, despite spending over $500 million and employing over 8000 people to reduce fraud in 2019, was only able to delete 40% of fake reviews ([5](https://hbr.org/2020/11/how-fake-customer-reviews-do-and-dont-work)).

This situation has spurred scientific research aimed at understanding and tracing fake news and its impact ([6](https://experts.umn.edu/en/publications/evaluating-vulnerability-to-fake-news-in-social-networks-a-commun)), leading to the development of bot detection, network analysis, and behavioural analysis in recurring neural networks of suspicious activities in network nodes of social and media platforms ([7](https://link.springer.com/article/10.1007/s13278-022-00890-z), [8](https://link.springer.com/chapter/10.1007/978-3-031-00126-0_18), [9](https://link.springer.com/chapter/10.1007/978-3-031-00126-0_18)).

In this context, there is a growing demand for trust based review platforms. This demand for trust based platforms is driven by the increasing influence of online reviews on consumer behavior and skepticism towards their authenticity. AN Social Authenticity Network addresses the need for reliable social platforms facilitating social authenticity, reviews and provable trust scoring [(10)](https://www.leewayhertz.com/blockchain-social-media-platforms/).

## Global media trust scores fall to all time low Edelman Trust Barometer/2023

Based on the 2023 Edelman Trust Barometer report, the global trust in societal institutions is at an all-time low due to economic anxiety, disinformation, mass-class divide, and a failure of leadership. This has led to deep and dangerous polarization, with 53% of respondents globally saying that their countries are more divided today than in the past.

**Here are some key findings from the report**:

There is an 11-point gap between trust in business and trust in government globally, with 62% of people trusting business while only 51% trust the government. Economic optimism has collapsed, with only 40% of respondents saying they and their families will be better off in five years, a 10-point decline from 2022. There is a significant mass-class divide on trust, with those in the top quartile of income being more trusting than those considered low income. Double-digit trust inequality exists in 21 of the 28 countries surveyed. Businesses are expected to act as the most trusted institution and should leverage their comparative advantage to inform debate and deliver solutions on climate, diversity, equity, and inclusion, and skill training.

**There is a big call for trustful information**&#x20;

1. Digital advertising spend losses to fraud reach $68 billion globally in 2022 Juniper Research ([11](https://www.juniperresearch.com/press/digital-advertising-spend-lost-to-fraud-68-billion)).

A new Juniper Research study has found that the value of digital advertising spend lost to fraud will reach $68 billion globally in 2022; rising from $59 billion in 2021.

In response to the rising threat of ad fraud, the report urges digital advertisers targeting these five markets to form strategic partnerships with ad fraud detection and prevention vendors capable of distinguishing between valid and fraudulent advertising traffic that provides no return on ad spend.

The most successful ad fraud detection tools will harness machine learning algorithms to compare advertising traffic with previously observed, verifiable baseline data. As a result, the report urges advertisers to adopt fraud and mitigation solutions to maximise return on ad spend through the earlier detection of new fraud tactics.

2. The study conducted by Professor Roberto Cavazos of the University of Baltimore, titled "Economic Costs of Fake News: A Worldwide Assessment," provides a comprehensive analysis of the economic impact of fake news ([12](https://s3.amazonaws.com/media.mediapost.com/uploads/EconomicCostOfFakeNews.pdf)).&#x20;

Here are some key findings from the study: The global cost of fake news is estimated to be $78 billion annually. This figure includes the direct costs associated with combating fake news, as well as indirect costs such as reputational damage and reduced trust in legitimate news sources.

The study also highlights the role of social media platforms in the spread of fake news. It argues that these platforms have a responsibility to combat misinformation, but also acknowledges the challenges involved in distinguishing between legitimate news and misinformation.

Finally, the study calls for a multi-faceted approach to combat fake news, involving a combination of technological solutions, regulatory measures, and public education initiatives.

To tackle this problem, it is necessary to develop technologies that prioritize online social authenticity #SocAuth, digital trust and facilitate genuine interactions. Such platforms should implement advanced mechanisms to prove and verify authenticity of users' data, content provided to the network, and social interactions.&#x20;
