We thank Abhay for taking the time to provide his point of view.
I'm not an expert on Buzzsumo but RightRelevance is not a Twitter username search/lookup platform so the example below doesn't apply. Twitter usernames are not structured topics in our platform so they may work via free-form search but YMMY.
RightRelevance is a topical information search and relevance platform. Content marketing is an application that many of our users are leveraging us for so the question is very relevant. We built a Hootsuite app (http://appdirectory.hootsuite.co...) to cater to this particular scenario even more specifically and are working with other social media platforms to integrate our content via our APIs.
To answer your question, I haven't looked at what Buzzsumo does wrt discovering influencers but I can explain what we do.
RightRelevance is primarily graph based and performs a 2-level people rank:
1. Global to reduce a ~200M graph to ~3M (for now) ranked influencers
2. Per topic to find ranked influencers for every structured topic in our system (~40K). This is accomplished via graph partitioning at scale.
This is more akin to google pagerank but for people (instead of links/webpages) with many important differences.
Points to note:
- Other, non structured, topics work via free-form search but the relevance may not be of the same quality.
- Each influencer can be part of multiple topical communities and have a different rank within each. This is exposed in our apps via scored tags.
- Both our topics and graph are mined and built algorithmically at scale and increasing in number with higher quality with every iteration.
Once we have calculated and ranked the influencers' community/graph for a particular topic (e.g. machine learning, behavioral science, big data, emergency medicine, oil and gas, angularjs, social media marketing etc.) we mine the web (Twitter is especially useful here) for content.
High Level Features:
1. Influencers View: Ranked influencers' list for a topic with export functionality, articles, conversations and other areas of expertise with scores. We also show 'related topics' for discovery and locations as facets. Currently working on type of entity for e.g. person/organization along with much deeper locations tech.
2. Articles: This is where we expose highly topic relevant, verifiable and near real-time articles, videos etc. with sharing across Twitter, Facebook and LinkedIn for now. It leverages the influencers' graph for that topic along with several other parameters to surface the most relevant articles/videos via dynamic re-ranking. Associated influencer tweets for an article are displayed here. There is a time-based view too as an option.
3. Conversations: Provides an 'outlook-like view' of the conversations on Twitter that topical influencers are engaging in right now. Unlike articles, the relevance isn't guaranteed here and conversations, though interesting, could be off-topic.
4. Insights: This is a new section and we aim to provide tons of graph and location based analytics here. Some are already online.
Our FAQ is woefully inadequate and we'll work on updating it but some answers are here: Frequently Asked Questions
Thanx,
Vishal
I'm not an expert on Buzzsumo but RightRelevance is not a Twitter username search/lookup platform so the example below doesn't apply. Twitter usernames are not structured topics in our platform so they may work via free-form search but YMMY.
RightRelevance is a topical information search and relevance platform. Content marketing is an application that many of our users are leveraging us for so the question is very relevant. We built a Hootsuite app (http://appdirectory.hootsuite.co...) to cater to this particular scenario even more specifically and are working with other social media platforms to integrate our content via our APIs.
To answer your question, I haven't looked at what Buzzsumo does wrt discovering influencers but I can explain what we do.
RightRelevance is primarily graph based and performs a 2-level people rank:
1. Global to reduce a ~200M graph to ~3M (for now) ranked influencers
2. Per topic to find ranked influencers for every structured topic in our system (~40K). This is accomplished via graph partitioning at scale.
This is more akin to google pagerank but for people (instead of links/webpages) with many important differences.
Points to note:
- Other, non structured, topics work via free-form search but the relevance may not be of the same quality.
- Each influencer can be part of multiple topical communities and have a different rank within each. This is exposed in our apps via scored tags.
- Both our topics and graph are mined and built algorithmically at scale and increasing in number with higher quality with every iteration.
Once we have calculated and ranked the influencers' community/graph for a particular topic (e.g. machine learning, behavioral science, big data, emergency medicine, oil and gas, angularjs, social media marketing etc.) we mine the web (Twitter is especially useful here) for content.
High Level Features:
1. Influencers View: Ranked influencers' list for a topic with export functionality, articles, conversations and other areas of expertise with scores. We also show 'related topics' for discovery and locations as facets. Currently working on type of entity for e.g. person/organization along with much deeper locations tech.
2. Articles: This is where we expose highly topic relevant, verifiable and near real-time articles, videos etc. with sharing across Twitter, Facebook and LinkedIn for now. It leverages the influencers' graph for that topic along with several other parameters to surface the most relevant articles/videos via dynamic re-ranking. Associated influencer tweets for an article are displayed here. There is a time-based view too as an option.
3. Conversations: Provides an 'outlook-like view' of the conversations on Twitter that topical influencers are engaging in right now. Unlike articles, the relevance isn't guaranteed here and conversations, though interesting, could be off-topic.
4. Insights: This is a new section and we aim to provide tons of graph and location based analytics here. Some are already online.
Our FAQ is woefully inadequate and we'll work on updating it but some answers are here: Frequently Asked Questions
Thanx,
Vishal
About the Author
Vishal Mishra
CoFounder & CEO, RightRelevanceFormer SDE at Microsoft2002-2013
Studied at The University of Texas at Austin
Lives in San Francisco, CA
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