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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.

When you want to understand the world you never start from scratch. As a child, you learn the language from your parents. Then you learn to read and write, you acquire the tools that have been developed and evolved and experimented with and improved, generation after generation in order to be as efficient and effective in your ability to distinguish fact from fiction. To compare reality with the results of your experiments and the ability that we all have to share the tools that we use in order to understand the world is what constitutes our civilization. In this past year we have learned how important is to be resilient and the tools that we can apply in order to build the skills that he prove our resilience that improve our adaptability, that improve the probability of being able to cope with the various conditions and the situations that surround us disability is both very practical most mechanical, but it is also, of course, psychological, emotional, and in a 360 degree evaluation

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of whether we are fit in a particular situation. We cannot concentrate only on just one aspect in knock on the other, they complement each other, both as individuals, as well as the communities that we form. So, the beauty of the world today is that indeed. We have the basis to make sure that as many people as possible can leverage the opportunity of improving their adaptability their resilience, and to stay fit in a complex world. These tools, of course, are those traditional, whether they are books or television, but they are in an ever increasing rate, more modern tools that allow many to many communications over the internet ways to learn to teach to learn to teach in a cycle where knowledge and understanding are immediately available. Now the architecture of these tools matters, as well. Because yes, there is a lot that we can gain from the efficiency of centralized distribution, and the ability of intermediaries in aggregating ranking filtrating and presenting the sources of

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information and understanding is very attractive. But we don't have to stop there we cannot become lazy because of the ease of these platforms, and the immediate availability of the tools and the information. Just a few days ago. There have been a scary handful of hours were in very large part of the world at all, and the various platforms, provided by Google, became unavailable, whether it was Gmail for sending and receiving email messages, or it was YouTube for uploading or watching videos, and many, many others. And we have realized how

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strongly we are relying on those platforms and tools and their unavailability can cripple our ability to be resilient to adapt and to be fit in a complex situation. So we must keep evolving our tools, especially peer to peer distributed and decentralized platforms are a must. We cannot afford to put our individual, and social survival at risk by relying on an excessive degree on decentralized tools regardless of how rich, they are, regardless of how their features correspond to our needs, regardless of how they have provided us a stepping stone towards a deeper understanding of the world. It is one of the reasons why it is so irresponsible to pretend that peer to peer tools are equivalent to infringing or even criminal purposes. It is ridiculous to paint with a wide brush and just pull, every possible use to get her completely excluding fundamental, legal, and even vital uses that do not rely on the sanctioned permitted compliant regulated platforms. There have every interest of

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slowing down the evolution of the alternatives. So, you owe it to yourself. In order to proceed in your learning in your ability to cope. In you're becoming more and more resilient. In order to make sure that the path, on which you have started is not stymied by inferior centralized technology to both learn, teach adapt and practice what these tools allow. And the next several episodes of the context, will be dedicated in at some degree or other, to the power of these centralized peer to peer technologies, such as BitTorrent, such as Pirate Bay, which is an interface to beat torrent, such as free 3d printing and digital manufacturing blockchain, and Bitcoin and the applications of these centralized payment mechanisms and decentralized finance. Each of these have been labeled to be either borderline criminal or explicitly criminal in various phases by various incumbent interests, often successfully. I am recording this in Italy, for example, where not only network neutrality is non

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existent. But the Italian internet is heavily censored. You cannot access Pirate Bay under the assumption that every use of Pirate Bay is unlawful. Well, surprise. I published a book and copies of this book are available on quotation marks. Pirate websites available via BitTorrent. And I am proud of that. I can assure you.

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I am losing no copies in sales of my book, because of the availability on those platforms. If anything, as Cory Doctorow says the biggest madness against me. Is obscurity. Not piracy. So I want to encourage you and I will keep illustrating. What are the tools that when push comes to shove, and the pressure. A of a context world increases will show to be tools that are more reliable than the centralized one that make us lazy. I hope that you will follow me in this journey. And if you want to understand at depth. What are the mechanisms that technology in our complex world is leveraging, but also expressing in paradigms that can be understood adopted. And, which can drive your decisions. I invite you to become a subscriber of the jolting technologies courses, go to jolting.co and sign up to enjoy both pre recorded videos as well as live sessions where we go in depth, answering the questions that you ask, having conversations about the implications. And the jolting technologies that are

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based on an understanding of an increasing rate of acceleration in the world around us are going to be. As a matter of fact, already are the driving force of the transformations that we see around us. In the 21st century. We are now entering the third decade of the 21st century. It is time that we step up to the challenges as powerfully, but also in a sure footed manner that we can claim. The possibility, the ability, the desire to reach our full potential. There are huge challenges that we have to address. And we have the skills, the talent and the tools that we need in order to address, and to solve them. Thank you, and good luck.

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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:
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

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:
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.
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