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About this Context Graph:

total nodes:  extend
Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

Bitcoin and mining are going to deliver an increased degree of sovereignty, not only to individuals, but to entire nations. Today, we are living in a energy financial system that is unsustainable, it must and will be substituted by a new one that is going to pay an unexpected these dividends. My name is David Orban, and this is the context

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you hear from many different places that Bitcoin mining is wasteful, it is damaging the environment. And this is completely misguided. The current system we live under is based on oil extraction, and the transportation of oil. All across the world. It costs $2 trillion of military subsidies per year on the side of the United States alone. And it is undermining the sovereignty of nations that are exposed to the uneven distribution of energy resources that are the basis of all of their activities and abilities to support themselves and to make their residents thrive. Bitcoin mining represents the start of a new planet wide energy financial network that is radically different. First of all, renewable energy is the cheapest source everywhere, to an increasing degree and unstoppably. So, because it costs are still declining at a rapid pace. The availability of renewable energy is increasing with the deployment of solar farms. So wind farms of hydro and of course batteries. And Bitcoin

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mining can be thought of as a battery transforming energy into direct financial assets, these financial assets can be moved freely across the world and then at their place of arrival potentially be transformed into available energy again, the network that renewable energy coupled with Bitcoin mining designs is evenly distributed. Yes, of course, there are regions that have more insulation more solar radiation eating them. And there are regions that have more wind power available for example, the first are more towards the equatorial regions of the planet and the second are more in the northerly regions of the planet. Bitcoin mining also can connect in this global network energy sources that were previously on economical to develop because they were not connected to the grid. There are many megawatts 1000s of megawatts of hydro power, for example, that are currently math bold, that can be put to use using Bitcoin mining. Individuals can build their wealth and financial independence by

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accumulating Bitcoin and other cryptocurrencies. And many people have been doing this for the past 10 years, and it is not too late. Anyone can still start doing it and do it for the next 10 years. But it is the same for nation states. Nation states can start holding Bitcoin on their balance sheets. The United States famously is doing the opposite, when it is, for example, taking possession of Bitcoin In operations against certain criminal activities, rather than keeping that Bitcoin and putting that on its balance sheet, it is auctioning the Bitcoin off. Famously, Tim Draper acquired the Bitcoin that the United States held as a consequence of dismantling sealed crow. And he made billions of dollars of gains as a consequence of winning that auctions

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which is the smartest nation in the world, well, you could think El Salvador, but actually, it is Bulgaria, Bulgaria have been accumulating and keeping Bitcoin on its balance sheet for some time. And today, it is at a point where it can or could, if it wanted to pay its national debt off. But rather than doing that, Bulgaria is looking at the interest payments in fear in servicing its debt. And it is looking at the progressive appreciation of its Bitcoin holdings. And comparing the two it says, Well, I will just Hodel I will keep Bitcoin. More and more nations are going to do the same. If you think about it, today's world is such that even though theoretically, nations are sovereign, in practice, their sovereignty is severely limited. All of the European nations, for example, gave up control over their own currency. Most of the time, their local laws are mandated by the European Union. But the EU itself is exposed to natural gas supplies from Russia. And in case of increased tensions,

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there's always a conversation about whether Russia is going to shut the gas conducts hurting itself in the process for sure, but hurting Europe even more. And if you pay attention, these conversations around whatever a crisis that appears to be the source of the increased tension, start in winter, because of course, it is in winter, that gas used for heating is most important in Europe. In the new Bitcoin, energy, and financial network, evenly distributed across the world. nations which are smart, are going to improve their own sovereignty, they are going to improve the degrees of freedom of their residents, and they are going to lead the planet towards a sustainable future where people and organizations can sustainably Thrive. Thank you very much for listening for watching this episode of the context. If you like it, you can support my work directly on Patreon at patreon.com/david Ortman.

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Language Processing Settings:

language logic: stop words:
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse 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.
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Main Topical Groups:

please, add your data to display the stats...
+     full stats   ?     show categories

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 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...
+     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:
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 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
(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 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).

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

⤓ Download as CSV  ⤓ Download an Excel File
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:
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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.
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We will build two graphs:
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