The Virality Score

Understand the Virality Score and the virality scores of the trends.

Calculate The Virality Score

The "Virality Score" is a novel concept that intertwines the dynamics of social media engagement with the functionality of the Bancor Bonding Curve in the realm of cryptocurrencies and blockchain technology. This score is specifically designed to adjust the Bancor Bonding Curve based on the popularity and engagement levels of specific hashtags on social media platforms, such as Twitter.

The Virality Score is calculated by analyzing and weighing various forms of social media interactions, including the number of retweets, comments, and favorites a particular hashtag receives in comparison to all other hashtags on SAX. By incorporating this metric, the Bancor Bonding Curve can dynamically respond to real-time social media trends and engagement, adding a layer of social sentiment analysis to the pricing mechanism of trends.

The integration of the Virality Score into the Bancor Bonding Curve represents an innovative approach in aligning cryptocurrency token values with social media trends and public sentiment. In this model, the more a specific hashtag related to a trend is engaged with on social media (through retweets, comments, and likes), the higher the Virality Score becomes. This, in turn, influences the Bancor Bonding Curve, leading to an exaggerated increase and decrease in the trend's price as its social media presence grows.

This methodology creates a direct correlation between social media virality and token economics, allowing for a more responsive market that reflects the changing perceptions and interests of the public. It's a unique blend of social media analytics and financial algorithms, aiming to create a more integrated crypto market ecosystem.

For example, if the following weights are applied:

Retweets: 0.6

Comments: 0.25

Favorites: 0.15

And there are:

Retweets: 500

Comments: 1000

Favorites: 2000

The weighted sum would be 850. Let’s say there is only one other hashtag with a weighted sum of 2000. The average is then 1425. We then divide 850 by 1425, resulting in a Virality Score of 0.5965 and 1.4035, respectively.

Deriving the Virality Score

Each trend has a Virality Score attached to it. A trend that has a Virality Score between 0-1 means that it's underperforming the average trend, while a trend with a Virality Score above 1 means it's outperforming the average trend.