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*'''Network Visualization'''**Fundamentally, one ought to think of Twitter as an interest group, not a bona fide social network. Consider this: Twitter represents the degree of interest in, for instance, '''#ycombinator''', not the stable business and personal connections made to and fro '''@ycombinator'''. It also houses everyone from the very important @barackobama to the fictional and frivolous @homersimpson. At the outset, Twitter represent trends than material fact. The following-follower relationship is mono-directional and voyeuristic, representing what people care and think about, instead of who they really are and what they really do. Twitter activity happens at the speed of thought (140 chars) and represents our rapidly-changing minds and perceptions. <blockquote>At this level, let's consider the classical aspect of Twitting mining: ''Network Visualization''. This is sociological and concerned with the self-organization of interest-based communities. It primarily provides us with a sense of social roles in an interest group; broadcastor vs receiver, influencer vs influenced. We can also learn about the quantity of interest in a social group, and, when measured over time, the delta/changes in this quantity within the group. We gain knowledge about trends that rise and fall, people that move in and out of the interest group, and community structures of a given interest group.</blockquote>

*'''Tweet Analytics'''**Digging a little deeper beyond this superficial exchange, we come to a point where we need to think qualitatively about tweets. What people care about reflects some material facts about their material selves. Tweets containing hashtags such as #kpceoworkshop, for instance, tells us which people are attending the event physically and which people are passing commentary on it. When a startup has an IPO/Acquisition, it will attract a tremendous volume of mentions. When the presidential candidates talk about their technology policies, the entrepreneurship twitterverse responds. <blockquote>This is the next level of Twitter mining, often associated with Natural Language Processing techniques: '''Tweet Analytics'''. From tweetsCombined with the '''Network Visualization''', we can learn about events that are unfolding in different parts of the entrepreneurship world, as well as new organizations and topics that appear in the conversation. These new organizations and topics will, in turn, generate the beginnings of new interest networks. When measured over time, we can get a handle on the up-and-coming stars in the field, and emerging trends that are of note.</blockquote>

*'''Geo Visualization'''**On a even more physical level, tweets contain geo-information such as @user's home location and the tweet-from location. Through this, we stand to learn about the people's interests stratified by location. When combined with the former two forms of twitter mining, it can enhance what we know about physically-bound social dynamics and physically-bound shifts in interest and opinions.<blockquote>'''Geo Visualization''' is the process of mapping tweets to a real map of the Earth. Applying '''tweet analytics''' and '''network visualization''' to it, we stand to have a better picture an understanding of the ongoings geographical dimension of entrepreneurship activities in terms of peoeple, organizations and events in particular places, for instance Palo Alto, CA or Austin, TX. When measured over time, we can observe the crests and troughs of activity in these places. This would be extremely promising especially for the '''HUBS''' research project.</blockquote> For simplicity, I will refer to the above aggregate as Viz&Ana ===Key Ideas===*'''Visualization and Analytics: DaaS'''**While exploring the web, I realized that DaaS firms focus on providing Twitter visualization and analytics services to businesses and individuals to enable data-driven decision-making. In other words, the twitter data they mine offer an user interface for the client to interpret Twitter as an observable phenomenon. Clients exercise their own judgment as to whether a marketing campaign or event organization is successful, and make decisions based on these visualization and analytics.**To contribute to the research work at McNair, I would propose that we assemble tools and software in the spirit of a DaaS. In other words, Twitter Mining per se is not meaningful. Constructing a working system where researchers can observe the twitterverse, as if interpreting a primary source of data, is meaningful. For data scientists, running statistical analyses on outputs from this working system is meaningful.  *'''Portability & Flexibility'''**This is the '''bit''' where we distinguish ourselves dream bigger than a run-of-the-mill SAAS, whose work ends when the viz/analy is delivered to the hands of the clients. **Since the Viz&Ana is for research consumption, further research and analysis must be carried out on the graphs, maps and tables produced by the Viz&Ana. We therefore should do well to avoid blackbox scenarios where beautiful but inflexible graphs are produced but cannot proceed further in the hands of the researchers. Open-source tools, a stronger backend and a good data management system is therefore important considerations when building our Viz&Ana system.**In other words, I want data structures that can move between softwares, not just a poster to hang on the walls. *'''"When measured over time...'''**Since twitter represents the movement of trends, it is best interpreted as an organic body of knowledge that is contingent on the passage of time. Any Viz&Ana that we conduct on the twitterverse must be able to be viewed as function of '''time'''.