The SenseMaking service can detect spikes of discussions on certain topics from online chatter and social media. It breaks them down into subtopics and detects sets of similar discussion spikes by using temporal topic similarity graph analysis. Further, it will provide an analysis of the underlying social networks which produced the events, such as news websites, politicians, blogs, web-trolls, and so on, depending on the issue.
A typical use case of the SenseMaking service is the prediction of the stickiness and the spread patterns of a given topic, such as a newly launched ad by a company or a newly started election campaign, expressed by the end user as a set of natural keywords in any language. SenseMaking performs rapid linking of the input data to up-to-date structured representations, and extracts the causal social network communities and events to return a prediction of the upcoming developments of the queried topic in the web chatter to the end-user, as well as recommendations on what could be done to further promote the topic or to contain its spread. Containment might be needed for topics related to emergency response situations, for instance, where the spread of rumours and fake information through web chatters might cause uncontrolled damage.