Healthcare Social
Graph

The Healthcare Social Graph is the world’s largest database of its kind with billions of datapoints and a decade of history.
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Healthcare Data
Classification

Symplur Signals uses a combination of human curation and automated processes such as Natural Language Processing (NLP) and our proprietary algorithms to organize, structure, and appropriately categorize raw social media data, creating order out of chaos. We classify the data by type and by subject matter.
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Healthcare Trending
Terms Analysis

Using Natural Language Processing (NLP), we identify the most significant and trending terms in any healthcare conversation. Significance is assessed by a combination of elements including word frequency and level of influence of interlocutors, in other words, word bubble size is determined by algorithm score rather than frequency count. What vocabulary are patients with diabetes using to describe their experience, and will understanding it lead to better doctor-patient communication?
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Healthcare Engagement
Analysis

Healthcare engagement analysis helps you easily grasp what resonated the most and determine who and what is driving the healthcare conversations. How much interest did the latest conference elicit? Which stakeholder group tweeted the most? Which tweets went viral? Which tweets sparked true back-and-forth discussions? Not all tweets are equal. Symplur Signals evaluates the importance of every conversation based on who shared it and who engaged with it, as well as their stakeholder status and influence in the healthcare industry. The Engagement Analysis highlights the most impactful tweets and discussion threads for any clinical trial.
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Healthcare Network
Analysis

Healthcare network analysis returns an interactive map that reveals conversation and engagement patterns between the most central Twitter users of a community or a healthcare topic. Quickly identify influencers by the size of their node—nodes grow in importance and size based on the amount of engagement they are receiving—and the character of the conversation flows between them and others.
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Healthcare Stakeholder
Segmentation

In any digital conversation, it’s critical to know and understand who’s speaking – as well as their specific role in healthcare - to extract meaningful and actionable intelligence. With this knowledge, conversations can be contextualized, and authority and weight can be attributed with confidence.
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Healthcare Influencer
Analysis

SymplurRank –the only influencer algorithm developed specifically for healthcare and trusted by academic researchers, government agencies and pharmaceutical companies.
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Healthcare Sentiment
Analysis

Healthcare sentiment analysis is powered by a natural language processing (NLP) algorithm that we’ve optimized for healthcare. It extracts subjective information from social media healthcare conversations to determine what is known as the "polarity"—expressed as positive, negative, or neutral—of specific healthcare topics. As a result, we are better able to identify varying attitudes around conversations and provide you with a deeper understanding of the associated sentiments.
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