The Right Relevance score provides a measure of the social capital earned by an influencer in a topic.
Given a topic, say ‘ux design’, Right Relevance identifies the topical influencers for ‘ux design’ in the social web along with the network of links between these influencers. The interconnections are used in a pagerank-like algorithm to find the relative rank of each ‘ux design’ influencer and a normalized score is represented as the Right Relevance ‘score’ in that topical community.
Topical influencers are the social experts for a topic in the Right Relevance platform.
Given a topic, for e.g. ‘surfing’, Right Relevance mines the social web, esp. Twitter, to find the people, blogs etc. sources that hold the most influence for ‘surfing’. We designate them as ‘topical influencers’ for the topic, in this case for ‘surfing’.
The interconnected influencers for a topic form an influencer community in the Right Relevance platform.
Given a topic, for e.g. big data, Right Relevance identifies the influencers for ‘big data’, understands the interconnections of the ‘big data’ influencers to identify the most active and vibrant members. We call this group of influencers as the ‘influencer community’ for ‘big data’.
The text passage describes the Right Relevance platform and how it identifies and ranks topical influencers and influencer communities. It does not contain any mathematical questions or answers.
no_question
Given a text passage, determine:
    1. If it contains a mathematical question (including word problems or calculations)
    2. If an answer to that mathematical question is provided in the text

First, describe the content structure you found in 1-2 sentences.

Then provide your analysis in the following format:
TYPE: <one of the following values>
- NO_QUESTION
- SINGLE_QUESTION
- SINGLE_QUESTION_WITH_ANSWER
- MULTIPLE_QUESTIONS
- MULTIPLE_QUESTIONS_WITH_ANSWERS
system
The Right Relevance score provides a measure of the social capital earned by an influencer in a topic.
Given a topic, say ‘ux design’, Right Relevance identifies the topical influencers for ‘ux design’ in the social web along with the network of links between these influencers. The interconnections are used in a pagerank-like algorithm to find the relative rank of each ‘ux design’ influencer and a normalized score is represented as the Right Relevance ‘score’ in that topical community.
Topical influencers are the social experts for a topic in the Right Relevance platform.
Given a topic, for e.g. ‘surfing’, Right Relevance mines the social web, esp. Twitter, to find the people, blogs etc. sources that hold the most influence for ‘surfing’. We designate them as ‘topical influencers’ for the topic, in this case for ‘surfing’.
The interconnected influencers for a topic form an influencer community in the Right Relevance platform.
Given a topic, for e.g. big data, Right Relevance identifies the influencers for ‘big data’, understands the interconnections of the ‘big data’ influencers to identify the most active and vibrant members. We call this group of influencers as the ‘influencer community’ for ‘big data’.
user
The text passage describes the Right Relevance platform and how it identifies and ranks topical influencers and influencer communities. It does not contain any mathematical questions or answers.

TYPE: NO_QUESTION