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total nodes:  extend
 
InfraNodus
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.

Does Bitcoin matter? Does Bitcoin care? What do you think about it? Whether it matters or not? Well, the President of the United States believes that Bitcoin indeed matters. He labeled it a potential threat to the national security of the United States of America.

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I don't think that Bitcoin cares about what the President of the United States thinks about Bitcoin. And this is kind of the point. For 1000s of years, the way that we designed the money was such that walked, people thought about that particular kind of money influenced the money, and the behavior of that money and the rules under which that money would be able to exist. The point of Bitcoin and the design principles of Bitcoin start from being able to decouple from whatever anyone thinks about it. And it doesn't matter if they are the President of the United States. When Bitcoin was born, just a few people believed in the experiment or dedicated time and energy to understand what the Bitcoin experiment would be about. Today, Bitcoin arrived at a point where the heads of the most powerful nations in the world believe that they cannot afford not to take Bitcoin in serious consideration. So, what will be the next step? The next step is that Bitcoin will still not care. Bitcoin will

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still do its own thing, the network that will enable anyone to transfer value in an unfettered manner and without any possibility of nation states, stopping it or organizations interfering. The European Union, for example, is the latest to delude themselves that they will be able to stop Bitcoin. The European Parliament voted a few weeks ago, a resolution that will be implemented in all the member states that will impose reporting requirements on Bitcoin exchanges that are stricter than not those imposed on any bank. A bank is required to report on the source and the destination of a transaction when it is larger than the equivalent of $10,000 or 10,000. Euro. The rule that is now going to be imposed in Europe is for this reporting to apply at any amount being transferred. And it is frankly, ridiculous. And it is not going to achieve whatever supposed desired effect the legislators felt they would need to act in order to achieve why because Bitcoin doesn't care. So these conferences

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like Bitcoin 2022, here in Miami, are an interesting mix of grassroots and Toosi hasm of punditry of technology companies providing different kinds of solutions. And well, yes, Bitcoin doesn't care even about that. These

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conferences are a wonderful way for the community to find each other and recognize each other, and also to distinguish itself from other communities. At Bitcoin 2022, for example, it is frowned upon to talk about anything other than Bitcoin. You are not supposed to mention, Ethereum, you are not supposed to mention cryptocurrencies. And it is very sweet. How the Bitcoin community wants to distance itself from these other communities to, of course important in order to maintain its own identity in order to support its own perceived importance. And guess what? Bitcoin doesn't care. So, this kind of ability of not carrying is rather unique. But it is not unique in the sense that we don't see it in other systems. It's just that we don't see it in systems that are designed by humans. This kind of uncaring, reliable, robust functionality is universal in natural systems. When we look at the documentary, where, for example, a newborn antelope is eaten by a lion, we project our human values

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and our human emotions in what is happening. And we feel bad for the antelope that dies after a few hours or days of being born. But nature actually doesn't care. Nature just does its own thing. So in this sense, Bitcoin is the first human design system that truly resembles this ability of performing its function that we can recognize and attribute to natural assistance. And it will be very important for everyone to understand this, both those who have been in Bitcoin for a long time, those who are now recognizing it for the first time, and regulators and nation states, as powerful as they perceive themselves to be, they will have to come to grips that the performance and the robustness and the ability to keep going in the future of Bitcoin is independent from their desires is going to happen regardless of what they think or what they do. You should care and I am looking forward to understanding that you are caring, but you should care for yourself and be fine with the fact and

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recognize your ability to let go of the feelings that you have towards Bitcoin, because Bitcoin doesn't care.

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show as nodes: double brackets: categories as mentions:
discourse structure:
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Network Structure Insights
 
mind-viral immunity:
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stucture:
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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.

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Narrative Influence Propagation:
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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:
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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:
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Structural Gap
(ask a research question that would link these two topics):
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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):
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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
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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):
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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):
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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
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Text Network Statistics:
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⤓ 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:
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 Google search results as a graph, so you can learn more about this topic:

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