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The context, season three episode 32 Season Finale. When I started recording the context. Several years ago, based on an idea and a conversation with my friend. MASSIMO Curatola in Rome. I didn't imagine that I would keep going with the determination, and the frequency that I found myself being able to deliver. It has been a wonderful experience, and I want to keep continuing. Well, I don't know for how long. And the reason is because contrary to a lot of things that rightly we do in a manner that is designed and analyzed upfront. The way that together with my team, I today at least go about recording the context. Each week is if we want to be generous haphazard, meaning that I just stand up in front of the camera. I decide what I want to talk about, and I do it. It is a wonderful way, at least for me, because it allows me. On one hand, to really take advantage of the impressions, and the ideas and the themes that in my opinion matter in that particular moment. And it enables me to be

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spontaneous. It enables me to connect it enables me to develop relationships that it looks like you appreciate as well. Every week I receive a lot of positive feedback to keep going and how much an episode or an other resonated with one of you who are either subscribing to the podcast version, or following it on YouTube, or our app, and has become patreon supporters of the context and the rest of the work that I do this kind of spontaneity is seen by some as negative, because they feel, especially when they apply the criteria to themselves that they shouldn't allow themselves and as a consequence, others shouldn't allow themselves to be spontaneous. To that degree, that a very deep preparation is necessary before you can make the first step in many episodes, I expressed my point of view that this shouldn't be the case in a lot of fields. There are exceptions. But, strangely enough, even those exceptions are potentially shrinking in the area of human endeavor that they are covering so

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for example, a few years ago I would have said, well, unless you have all the answers, you cannot attempt to create a rocket. And now we are seeing a rocket being built with the same kind of AGI, and iterative approaches that we have applied to internet software development for the past couple of decades. SpaceX is in front of our eyes, designing, experimenting, exploding and then trying again and keeping at it with their spaceship rocket in a manner that is surprisingly refreshingly interactive.

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Nice. Bye.

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What matters is that you have the mission in mind, you know where you are going you know what you want to say, in my case, but the details will emerge. And you can afford to make mistakes, and the people supporting you will be tolerant of these mistakes because they understand that they are not only a cost that we can afford. They are essentially part of the journey. This is the season finale of the context, and we will take a few weeks of pause and then restart with season four. It may be that we will introduce some new components. Refresh the thumbnails, which could be the easiest and less intrusive or profound innovation. Maybe we will do something else. And, yeah, I don't know yet. We don't know yet. But I am planning to restart the new season of the context with as much curiosity as much dedication and dependable creation of hopefully useful and valuable thoughts, expressed in my own way. To all of you, as I have done with season three and two and one. No spoilers because there

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is no punchline. It's not that there is a big reveal. Like in the Netflix series that we are all now watching it. The reason for for stopping is just to have a little bit of a breather is not even coinciding with some vacation time because I haven't been vaccinated yet so I cannot afford to go to a vacation in any exotic or less exotic Beach, where I would, I guarantee you, be very happy to go. And I am looking forward going. As of right now, by the way, it looks like that they will be able to start signing up to get the vaccine mid May, then I will have a little bit to wait. I don't know, two weeks, one month, we'll see. So by the end of June, more or less, I should be vaccinated. At that point, I think I will hop on a plane to go and visit my daughter in Seoul, Korea. I haven't seen her for almost a year, and spend a month or two with her. And then, depending also on how the rest of the world is gonna be organizing itself. I am looking forward to meet so many of you restart of

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course my speeches at conferences which I greatly enjoy having enriched my tool set with so many things that over the course of this year, and that point, year and a half. I have been able to do and share with you as well. The live streams have been just fantastic and I will definitely keep doing them, so many interesting guests joined in, from the founder of Ted, Richard Saul Wurman to the creator of Mathematica, and now the physics project. Stephen wall from it

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rap singer Baba Brinkman to a so many other people whom I enjoyed having a conversation sweat. So, doing these episodes of searching for the question live is something that I will definitely be doing as well. There is of course also the Italian version, quite a lot domanda. So one thing that I am still considering and I really want to find an excuse that forces me to, to take the jump is to invent a format for Tik Tok. It's where it is or it is not my audience and target really doesn't matter. I already have a tick tock account. It is not the usual David Orban handle because there are many people code the same and someone was faster than me grabbing it, fine. My tic tock account is, what is the queue. What is the question but it's too long so what is the queue. You can check it out. As of this recording, there are not a lot of videos, and so am I going to be doing. Tick tock haphazardly as I am doing the context. Probably not, at least what I want. If not, video by video but design or

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or or understand what the format should be, and doing a 15 second or 32nd video is hard. You cannot afford to waste a fraction of a second, you have to be in the size of as a sharp knife, and really hit the center of the target as an arrow, and any other analogy that I could come up with. So, we'll see in the conclusion is that you will keep receiving content in many different forms. As we restart the context you will find some hopefully positive surprises around it. And I am very much looking forward in continuing creating content receiving feedback from you on this journey that gave me and hopefully you as well, a lot of satisfaction. See you in the new season of the context,

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      network structure:
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      Network Structure Insights
      mind-viral immunity:
      The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

      In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
      The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
      The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

      Influence Distribution
      Topics Nodes in Top Topic Components Nodes in Top Comp
      Nodes Av Degree Density Weighed Betweenness

      Narrative Influence Propagation:
      The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

      The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

      The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
      propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
      We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

      Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

      For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

      Degree Distribution:
        calculate & show   ?
      (based on kolmogorov-smirnov test) ?   switch to linear
      Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

      This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
      If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

      Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
      ×  ⁝⁝ 
      Main Topical Groups:

      +     full stats   ?  

      The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

      We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
      Most Influential Elements:
      +     Reveal Non-obvious   ?

      We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

      Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

      The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
      Network Structure:

      Reset Graph   Export: Show Options
      Action Advice:
      Structural Gap
      (ask a research question that would link these two topics):
      Reveal the Gap   Generate a Question   ?
      A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

      This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

      Latent Topical Brokers

      These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

      These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

      Emerging Keywords

      Evolution of Topics
      (frequency / time) ?
      The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.

      Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

      Main Topics
      (according to Latent Dirichlet Allocation):

      LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

      We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
      Most Influential Words
      (main topics and words according to LDA):

      We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

      Sentiment Analysis

      positive: | negative: | neutral:
      reset filter    ?  

      We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

      The approach is based on AFINN and Emoji Sentiment Ranking

      Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

      Network Statistics:
      Show Overlapping Nodes Only

      ⤓ Download as CSV  ⤓ Download an Excel File

      Top Relations / Bigrams
      (both directions):

      ⤓ Download   ⤓ Directed Bigrams CSV   ?

      The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

      As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
      Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

      We will build two graphs:
      1) Google search results for your query;
      2) Related searches for your query (Google's SERP);
      Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
      Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

      We will build two graphs:
      1) the content that already exists when you make this search query (informational supply);
      2) what else people are searching for when they make this query (informational demand);
      You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
      Please, enter your query to visualize the search results as a graph, so you can learn more about this topic:

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      Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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