text: 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’.
explanation: The provided text extract describes the Right Relevance platform and how it identifies and ranks topical influencers for specific topics. It explains the process of identifying influencers, the use of a pagerank-like algorithm, and the formation of influencer communities. The text does not contain any math questions or answers.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
  Output rules:
  
  1. Briefly describe the document and mention if it contains a question or a question and answer.
  2. Finalize the output with: Label: no-question,question,question-and-answer,multiple-question,multiple-question-and-answer
distilabel_metadata.role: system
distilabel_metadata.content: 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’.
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The provided text extract describes the Right Relevance platform and how it identifies and ranks topical influencers for specific topics. It explains the process of identifying influencers, the use of a pagerank-like algorithm, and the formation of influencer communities. The text does not contain any math questions or answers.
  
  Label: no-question