Key Stories is a feature of the Signal AI platform that identifies and clusters together similar articles (even when they’re not exact syndicates) in real time to help you quickly understand what storylines have been driving trends in data without needing to sift through thousands of individual articles. How does it work?
At its core, this capability relies on entity extraction – one of the fundamental features of Signal AI that powers much of the advanced analysis on the platform.
In this process, Signal AIQ, the AI-powered engine behind the Signal AI platform, comes in to perform Knowledge Extraction. Using AI, unstructured, messy content is transformed in real-time into thousands of searchable Concepts which are made up of Topics like Sustainability and Automation, and Entities such as Organisations, People and Locations.
AIQ then counts the co-mentions between every entity and topic in the platform and monitors the co-mentions over time. Key stories identifies and focuses on spikes in co-mentions. By doing so, we cut through the noise, overlooking any trivial news such as daily stock ticker announcements. This process distills down about 5m news stories a day to about 150k spikes in co-mentions a day instead.
When AIQ identifies a spike in co-mentions it then analyzes the text itself of all of the articles in which an entity and topic have been mentioned together and begins to decipher the underlying story driving this coverage by understanding what other entities and topics are mentioned and how similar the text is for each story.
Once AIQ has clustered together all of the similar coverage and identified the topics and entities, this distilled information can be used to quickly identify the most important coverage and underlying narrative impacting a brand. This information is presented in a variety of ways throughout the web app.