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Project Notes:
Top keywords (global influence):
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Explore the main topics and terms outlined above or see them in the excerpts from this text below.
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Tip: here are the keyword queries that people search for but don't actually find in the search results.

David Orban 5:34

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The context season four episode one, and Bitcoin nado.

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When European explorers.

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When European explorers were conquering the South American continent, they were looking for the mythical city of El Dorado. Now, l Bitcoin Natto is going to be the real opportunity for a new kind of economic revolution, after the adoption of Bitcoin as legal tender, by the country of El Salvador,

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El Salvador, approved legislation to adopt Bitcoin as legal tender. This will come into force, 90 days after the signing of the legislation. And during these 90 days, There will be a lot of analysis of what are the implications of this, whether it is a momentous change in economic policy, not only for the country, but potentially representing a domino effect, or it is going to be just an experiment that will fizzle out. Legal tender is the definition of a monetary instrument that can be used to distinguish debt in different jurisdictions, there are different implementations of this concept, for example, whether a business is obliged to accept cash, or if you go and pay for something that is 1000 or $10,000, and you want to use pennies, they can actually legally refuse. Now, it, the countries that use their own money, of course, do so after hundreds of years of evolution in monetary policy, how money is managed, is very important for the economy of a country, and the well being of its

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residents. If money is not used well, then it is going to be inflating away the store of welfare that the residents use when for example, their income goes to zero, or as the inheritance for future generations. In the inflation has been the main cause of important economic upheavals in many different areas of the world, and it is strongly correlated with social unrest, leading up to including revolutions and wars. Now, the economic policy around money is not only, of course, how much you print and how do you distribute it to main banks and then in turn those to commercial banks that lend it out. Typically on our fractional basis to businesses and individuals. Monetary policy is setting expected inflation is via interest rates, and these interest rates are the main lever for central banks to influence what is happening in the economy, major economic players like the United States or the European Union. Invest considerable amounts of a thought in designing good economic policy. However,

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it is now becoming more and more widely understood that for the past dozen years or so. It is as if they completely abandoned. The ability of exercising, actually this influence because of how they handled the financial crisis of 2008 and successively what has been called quantitative easing. The reason is because now, interest rates are too close to zero in some regions are actually negative. You are punished by banks, if you hold too much money on your account.

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So, what is the situation of El Salvador. First of all, El Salvador, already belongs before this recent decision to a small set of countries that do not have a sovereign currency of their own. The economy of El Salvador, went through what is called dollarization it adopted the United States dollar as its own legal tender. So, that is what circulates, that is what you use to pay taxes that is what you use. If you are a business to express the prices of the goods or the services that you are selling. The reason why a country would do this, and El Salvador is not alone. There are countries, big and small that decided to abandon their own individual sovereign currency can be very diverse. A, for example, Germany or France, or Italy decided to do the same when they created and then joined the group of countries using the euro. Other countries, like, Liechtenstein may decide that it is not worth it, because of their size, to have their own currency so they use the Swiss franc. In the case

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of El Salvador. the original decision was taken, because they wanted to combat corruption, economic mismanagement, and in many ways, the current decision about Bitcoin is similar, at least in the words of the President of El Salvador. This will provide greater transparency, but also importantly, it will give ability to the vast majority of the population who do not have bank accounts, and as a consequence, don't participate in the modern financial economy to instead do so through Bitcoin. If everything goes according to plan. In, 90 days, so around September, a 2021. If you are a resident of El Salvador, you will be able to pay your taxes in Bitcoin to receive your salaries in Bitcoin. And if you are a business you will be required to both expose prices in Bitcoin, and accept payments for goods or services in Bitcoin. There are huge technical hurdles, around this and winter 90 days is going to be enough to implement the solutions to these horrible hurdles is definitely to be seen. How

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will businesses, adapt the prices of the goods, given that Bitcoin is very volatile. It is already said that for example, they will be allowed to immediately sell Bitcoin for dollars, and the state will guarantee this ability. Will the individuals who haven't been able to open bank accounts, adopt the digital wallets that are needed in order to then be able to pay in Bitcoin or receive first Bitcoin, one would hope, and then successively pay for products and services in Bitcoin or will they be hesitant reticent, or just not sufficiently prepared, either from the point of view of the technologies that they use, or the equipment that they have mobile phones, Internet connections, and so on.

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Regardless of whether 90 days is enough, or 180 days is going to be needed before all the wrinkles are ironed out. The experiment in El Salvador is very important. Other countries will definitely look at it. And this will hugely influence, and I expect benefit. The Bitcoin ecosystem on a worldwide basis. There will be other countries that will do the same and adopt Bitcoin as legal tender. And, of course, there will be countries that will start there will be countries that will laggards and come later. For sure, the countries in the Eurozone, or China, or Japan or the United States itself will be the last ones to surrender, if ever, but the dam has opened, and there will be interesting implications in terms, even of how Bitcoin is classified in other jurisdictions, rather than for example being a property that is taxed in a certain way when you buy it and sell it, it could be moving into being categorized from an accounting point of view as foreign currency, which, for example, under

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certain conditions. In the United Kingdom, doesn't pay capital gains taxes when treated. Now, the opportunity is definitely there. for El Salvador, to be a trailblazing jurisdiction to become, maybe not the Eldorado that attracted the conquistadores and was never found. But much better to be the L Bitcoin Addo. In reality, a heaven of experimentation, and of economic flourishing inclusively, with its population of 6 million people, giving rise to new classes of entrepreneurs of startups that further aim, provide interesting features both locally and why not, globally, to the Bitcoin echo system. Now, if this is the first episode of season four of the context. I hope you enjoyed listening to it. And if that is the case, I invite you to become a supporter on slash David Orban, I also have an online where I talk about technologies that develop with an increasing rate of acceleration. Artificial Intelligence quantum computing blockchain technologies,

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and many others. So, I hope you enjoyed this episode, And I will see you next week with episode two of season four of the context about a topic that I don't know yet because I pick it. In order to provide value, but also to be current weed what is going on in our world.

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semantic variability:
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Semantic Variability Score
— modulates diversity of the discourse network  how it works?
The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:


We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).

  1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
  2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
  3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
  4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).

Read more in the cognitive variability help article.
Generate AI Suggestions
Your Workflow Variability:
Shows to what extent you explored all the different states of the graph, from uniform and regular to fractal and complex. Read more in the cognitive variability help article.

You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
Phases to Explore:
AI Suggestions  
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Main Topical Clusters:

please, add your data to display the stats...
+     full table   ?     Show Categories

The topical clusters are comprised of 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 using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
Most Influential Elements:
please, add your data to display the stats...
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AI Paraphrase Graph

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:
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.

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Action Advice:
Structural Gap
(ask a research question that would link these two topics):
Reveal the Gap   ?   Generate an AI 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
(less visible terms that link important topics):

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
(number of occurrences per text segment) ?
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 number of occurrences.

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:
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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

Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.

Keyword Relations Analysis:

please, select the node(s) on the graph see their connections...
+   ⤓ download CSV   ?

Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
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. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.

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).

Text Statistics:
Word Count Unique Lemmas Characters Lemmas Density
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
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.
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.
Please, enter a search query to discover what else people are searching for (from Google search or AdWords suggestions):

We will build a graph of the search phrases related to your query (Google's SERP suggestions).
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 Google search results as a graph, so you can learn more about this topic:

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