Twitterverse Exploration

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McNair Project
Twitterverse Exploration
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Copyright © 2016 All Rights Reserved.

Field Notes


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
    • Developed open-source by the Social Media Research Foundation, with help from academics from Cornell to Cambridge.
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Features and Review


    • This being a clean-up process for the input data before analysis and display in the form of a graph
    • 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 - as above

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 calculation
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Inspiration, or the "Dream Case"

    • 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.
      • Our question will be: What is going on with startup XYZ?
      • Empirically, and in a micro way, I have observed that a new startup known as Aminohealth @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 'huge launch' but is relatively unknown in the bigger twitter picture. There is also nothing conclusive about what this launch entailed, and what kind of funding it received. Using the NodeXL tool, we can conceivably find out everyone that's involved in @aminohealth's recent activities, and systematically mine knowledge from this network.
      • @aminohealth itself possesses only around 1,000 followers, despite having 700+ tweets. Delta is far more important than what-is for rising startups as such.
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      • Empirically, the twitterverse is populated by important organizations as well as, we often forget, their staff. @jflomenb is constantly mentioned by @redpointvc and @accel, and has interesting exposes information about the entrepreneur scene, as shown. Again, delta is crucial.
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    • 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 follow-up events that occur after the events themselves conclude.


    • A input query is 'necessary'. I don't think the user can simply ask for a graph of all the followers of @xxx, for instance.
    • 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


    • In my recent days of interacting with the twitterverse, it has come to pass that Twitter is spectacular because of its malleability, flexibility and decentralized nature. All forms of social organization on Twitter is explicitly time-contingent and user-contingent. This is the why it is such an important hotbed for sociological research - it provides wonderful material for the study of social dynamics and social organization
    • In this vein, what we think of as the "Entrepreneurship Twitterverse" can be, more clearly, thought of as a time-contingent and very specific community shaped by its own trends, influencers, and cultural values, all of which are in turn shaped by the very specific people that are interested and involved in the same ideas/things. In our case, investments, foundings, IPOs, acquisitions etc
    • In light of this, does it make more sense for us to study deltas instead of things as-they-are?


    • Test case by
      • User attempted to graph the community activity regarding the topic "pew internet"
      • User used search string "pew internet" over a fixed period of 58 days
      • Output graph nodes are created for each @shortname on the broadcasting or receiving end of tweets that include "pew internet". Output graph edges are created for each mention and reply that appeared over the course of the time bracket.
        • Graph 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..
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R Packages Galore

Herein lies a great introduction to R for programmers already familiar with OOP


In a nutshell

  • Many R packages include social media analysis functionality
  • The advantage of using R, instead of a blackbox nice-UI, is R's portability and flexibility. Data can move easily between packages and into other software such as MSExcel or SPSS (Statistical Package for the Social Sciences).
  • According to the R community, it is widely held that despite their difference in specific functionalities, one can achieve all basic operations and visualization needs with any one of these R packages
  • For all R-based analysis, we have to use our in-house Twitter Webcrawler (Tool) to grab raw data and convert them into appropriate structures for R consumption (unsure)
  • Typically, they are all OOP with graphs, nodes and edges as objects

Features and Review


  • Powerful, feature-rich library
  • igraph on Github]
  • igraph on its own domain
  • Also available for Py and C
  • Known for ease of calculating basic graph metrics such as:
    • g.edge_betweeness()
    • g.pagerank()
    • g.betweenness()
    • to enable easy node/edge selection
  • Known for possessing community detection algorithm (e.g. Newman-Girvan)


  • Implements recent advances in statistical modelling of networks - unsure if we need such high levels of sophistication in graph theory implementation.
  • Focuses on statistical modelling of network data
  • Includes libraries network, sna which stands for naturally, Social Media Analysis
    • 3-D graph plot
    • Subgraph census routines, including component information, paths/cycles/cliques, removing isolates
    • Positional Analysis
  • Unlike igraph, statnet is developed by a team of statisticians from the University of Washington. It is thus heavy on the statistical analysis side.
    • ERGMs model
      • Exponential family Random Graph Models
      • Advanced technique associated with analyzing data esp. in social networks
      • Statistical model operates on the premise that all alternative networks are to be considered as much as the observed one. Alternative networks are, for e.g., generated through the Degree Preserving Randomization method.
    • Includes tools for model estimation, model evaluation, model-based network simulation, and network visualization.
      • Broad functionalities powered by central MCMC (Markov Chain Monte Carlo) algorithm


  • tnet
    • Two-mode networks (i.e. rows and columns of a two-mode matrix are different entities; e.g. persons vs. organizations)
  • RSiena
    • Actor-oriented model of network dynamics
      • Extremely theoretical and, presently, academic discipline.
      • Addresses the very realistic question of networks as an evolving system driven by actors (nodes of twitter users, in our case).
      • Stochastic; statistical modelling, Markov Chain
      • Could we use this modelling technique to predict future twitter trends of a the entrepreneurship interest group?