Twitterverse Exploration

McNair Project

Twitterverse Exploration
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Copyright © 2016 edegan.com. All Rights Reserved.

Exploration Notes

NodeXL

• In a nutshell: Enclosed system that auto-pulls, auto-cleans and auto-graphs Twitter networks revolving around input SEARCH TERM (read: this is important).
  • MSExcel-based (thus unsure of its portability, i.e. can we port the graph and its data structure to other softwares and development environments for further processing/analysis?
  • Highly mathematical, formal graph theory
  • Highly customizable
  • Vertices being (@twitterhandles) and edges being (follower/following relationship, mentions, replies, favroites, etc).
  • Operates on Twitter's Streaming API, requires user authentication
  • GUI; very user-friendly and accessible to even
  • Requires background in graph theory to understand mathematical concepts

• Limitations
  • It's a black box - this tool is designed for end-users that want to study contingent trends and discrete events, instead of a comprehensive and stable picture of a certain "scene" (i.e. the entrepreneur scene, in our case).
    • We can, of course, run the tool continuously for all trends that we identify. But would we be able to join them all up in an aggregate fashion?
  • Unsure of the usefulness of output
    • Sure, it will be nice to generate graphs and knowledge about upcoming events and organizations, but will we be able harness this information and use it to do other stuff?
    • In other words, it's unclear how portable our output data is

• Automation - clean-up before analysis and display
  • Group vertices by cluster (e.g. the Clauset-Newman-Moore algorithm to identify community structures) and calculate clustering coefficient
  • Count and merge duplicate edges (and therefore scale the resultant edge by width proportional to the number of edges merged)
  • Layout method - e.g. the Harel-Koren Fast Multiscale Layout algorithm

• Centrality measures
  • Betweenness centrality - identification of corridor/ambassador nodes that are important links between adjacent network communities. In other words, identification of the most BROADLY CONNECTED nodes in the network. Think: few friends in high places, as opposed to an abundance of low-level friends
  • Closeness centrality - related to clustering coefficient. Identification of strong communities within a larger network
  • Eigenvector centrality - unclear
  • Clustering coefficient

• Overall graph metrics
  • In a nutshell: Highly customizable
  • Vertices and edge count
  • Unique edges
  • Edge width - can be a function of number of merged edges, etc
  • Node size/color - can be a function of node's degree, centrality measures, etc
  • Egonet - user can look at each node as the "center of the network universe"
    • Pagerank - useful google coefficient that measures how good one node's IN-FLOW is, i.e. the tendency to end up at subject node as agent travels around its neighborhood
    • Number of tweets ever created
    • Number of tweets favorited
    • Other common "user data"
    • User can view egonets in a matrix, and apply "sort by" such that he can easily identify those nodes with the highest e.g. in/out-degree, centrality, pagerank etc)
    • Graph density - 2*|E|/(|V|*(|V|-1))
    • Connected Components information

• Inspiration, or "dream case" as Ed will
  • What if we tap on NLP capabilities to monitor twitter handles that are known to be important, and have a constant feed of important rising new words, rising new mentions and

rising new hashtags. Using this feed, we can populate and update graphs constantly, measuring delta instead of using graph data per se, and thus develop a good grasp of rising organizations, events and startups in the twitterverse. We would know things before other people do. Value.

• • • Empirically, and in a micro way, I have observed that a new startup known as Aminohealth (enables end-users to shop around for doctors based on price range; seems very novel and in-demand) has been appearing very constantly on important feeds such as @techcrunch, @redpointvc and @accel. It has just received a funding round (I'm writing this in 7/27) but is relatively unknown in the bigger twitter picture. In fact, Aminohealth does not have a twitter handle, nor is its hashtag populated. Delta is far more important than what-is for rising startups as such.
    • Empirically, the twitterverse is populated by important organizations as well as, we often forget, their staff. @jflomenb has useful tweets but has came into activity from a 6-year twitter hiatus. His name has been constantly mentioned by @redpointvc and @accel too. Again, delta is crucial.
  • What if we compare social networks against themselves over time?
    • If we generate useful network graphs and data OVER TIME that revolves around a single entity e.g. @redpointvc, we would be able to do a few pretty amazing statistical analyses:
      • The mean number of mentions before a startup gets signed to a VC
      • What are the quantitative tweet indicators that a startup is succeeding/failing?
      • All the startups a VC has signed since the VC obtained a twitter handle
      • The average pace at which a VC signs startups
      • What are the qualitatively trendy topics that are mentioned in the history of a VC? Does this influence their activity, if at all?
      • Any regression for the above, and more
  • What if we track ongoing events such as #kpceoworkshop
    • It'll be easy to find out who are the people that are attending the workshop, and add them to our watchlist of important people
    • Also, how important or impactful are these events? We can track their mentioners and likers and followers to identify and think about events that happen after the events conclude. (hmm..)

• Demo: In the following test case done by www.pewinternet.org, where user attempted to graph the community activity regarding the topic "pew internet", he entered a list of search strings all including the keywords "pew internet" over a fixed period of 58 days and some misc hours. His edges are created for each mention and reply that appeared in the time bracket. His edge colors and widths are proportional to the number of mentions/replies that occurred between two nodes (users). The color and transparency of his nodes are related to follower values, i.e. how many followers does each node have..