As media consumption continues to increase at an accelerating rate, companies struggle to understand audiences and deliver relevant content. Surface-level user-generated content such as likes and follower counts can be misleading because of bots and excessive self-promotion. In fact, not all likes and followers are created equally.
Supernova continuously processes and contextualizes massive amounts of social chatter from platforms such as TikTok, Instagram, Facebook, Discord, Reddit, and blogs to gauge viral influence and authentic engagement. Supernova autonomously extracts, normalizes, and maps entities without user labeling to create trustworthy profiles of talent and the audiences they are trying to reach.
Supernova empowers tech, media, and telecom companies to optimize ROI in talent acquisition, prevent churn, optimize ad spend, tailor programming, and boost platform engagement by spotting grassroots trends and narratives before competitors.
User behaviors are dynamic, non-repeating, and constantly changing, which hides the patterns we’re interested in. Authentic engagement cannot be fully captured using statistical approaches. To solve the problem, you need dynamic learning. Supernova helps you find the signal in the noise.
*UGC - User-Generated Content
Insights are rendered via a dashboard where users can search, filter, set alerts, receive notifications, and visualize data. It’s a one-stop platform designed to boost your productivity.
Spend your time more wisely versus spending hours digging and searching for the next ‘undiscovered’ creators. With Accrete AI and predictive viral influence capabilities, you can scale your expertise, efficiently monitor an ever-expanding universe of data, and discover trends and emerging creators before your competitors even know they exist.
Engagement fraud is well known within the influence and entertainment industry. Knowing the true reality of an individual's audience and how engaged they are is a decisive factor when considering suitability. Using semi-supervised learning models you can reduce the likelihood of collaborating with dishonest representatives and maximize a better return on investment.
Supernova monitors an unbounded universe of social media finding useful insights buried in mountains of noise. By monitoring everything, we capture trends earlier, capture more data, and model more accurately so decisions are made with the best historical and predictive data possible.
Supernova continuously monitors and ingests user-generated and official content from a myriad of digital channels, using bottoms-up processes, not predefined lists, digging deep into the data when high-level filter criteria are met.
Supernova understands not just high-level information such as followers, comments, and likes, but also more complex, derived features such as influence, authenticity, retention, and impact. These complex features quantify the value of user-generated content, enabling talent scouts to see beyond the high-level statistics.
Supernova features are designed with a differentiated approach:
- Distributed Graph Algorithms
- Deep Reinforcement Learning Algorithms
- Spectral Graph Analysis
- Contextual Comment Analysis
- Velocity of Nodes & Spectral Information
- Graph Structure with User Vertices & Activity Edge
- Normalized multiple variants into single entities
- Cross-platform connected entities & engagement tracking
Knowing the true clout of an individual and their audience are features that can help predict the value of an aspiring talent. By correlating the quality of traffic with the level of engagement over time, you can use these features along with others to map the collective psyche of the respective market. This hidden information can help you uncover future stars, project growth rates, and make better commercial decisions.