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Project Notes:
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.

Many of us barely realize, but the abundance that our society is able to provide is creating problems that we have to address, similarly to how we have addressed problems of scarcity in the past. My name is David Orban, and this is the context season four episode 10 The most fundamental scarcity or abundance that we know and recognize readily is that of food humanity's history has been characterized for ever, by a scarcity of food and famines where common population couldn't grow or declined, or even went extinct in certain geographical areas only. More recently, in the 20th century, we have been able to apply new tools that provided ample food for everyone. Today, when people don't have enough to eat is not a question of not being able to produce enough. It is a question of not being able to distribute it to them. And those of us who luckily, live in an environment where there is plenty food available have realized, new challenges, like, in my own case. Eating too much, not being

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able to face and address, a world of abundance, just as we would have been ill equipped to address a world of scarcity. This is just one example, And there are others, have it as. Has it ever happened to you to sit down, tired after a day of work and spend a disproportionate amount of time going through the plentiful options of what to watch. Whether to watch a short series, an episode, whether to watch a movie in particular, what movie. Did it ever happen, that you ended up spending so much time, maybe 30 minutes or more, that you didn't watch anything. This is also an example of abundance and our inability to efficiently and effectively address the challenge of abundance and other is represented by knowledge and learning, incredible amounts of knowledge are available readily and we sometimes don't know what to pick, what direction to go, where to apply our passion, wants to support and nurture our talents with, and we feel almost paralyzed by this abundance of choice. The tools that

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we must use and perfect are starting to be available. These tools have been available in the past as well. If you go in a supermarket, you are being manipulated by the physical display of those goods that the various producers, together with the manager of the supermarket, want you to buy. And the way to help you

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quotation marks, address the abundance of choice may or may not be to your ultimate advantage. Maybe the supermarket will let you buy something that is more expensive, or they have a higher margin on. Maybe the choices that you will make are not going to be the healthiest, if we are talking about buying food for example in the supermarket itself. Now, a lot of commerce happens online. And of course, when you go to Amazon, the algorithm will show you the results of your search based on all kinds of parameters. Not all of them working in your favor. When you pick a movie to watch when you want to listen to the next song in an automatically generated playlist. Similarly, there are algorithms and filters that will mediate in this super abundance of choices that is now available to you. So the tools that I am talking about all those the tools that these algorithms, implement themselves the filters that are necessarily there. They are not. I believe that we have to go to at least one level

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beyond. We have to learn and understand how the filters work, we have to learn and understand how the ranking algorithms are presenting the choices in order to induce us to make certain once rather than others. Only then we will be able to evaluate if the results of those filters and rankings and recommendations are going in the direction that we share, and understand and accept and adopt. Only then, as it is likely. When we realize that some of the rankings and some of the filters are suboptimal, we will be able to intervene and move them and nudge them in the right direction. Ideally, we should have the possibility of alternative filters that we could apply and compare, objectively, the results of one against the other, simultaneously, so that we can then make a judgement. Now, obviously, if there are many of these filters and algorithms, then comparing them manually, is cumbersome. So, yes, it is natural for an algorithm to compare the rankings, and the fielders and present the

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list that it believes is the best for us, based on the criteria that we have to be able to discuss and to specify and present to the algorithm ranking the algorithms. This is a kind of evolutionary process. And we have to understand that the cost of living in a world of abundance choices is the investment we have to make in learning about and managing the algorithms that manage the algorithms that manage our choices. Yes, there is an alternative of monastic discipline, renouncing the rich variety Wittering food, whether in travel and environments, whether in culture and nurturing curiosity and interest in learning about the world. And yes, some people aim to embrace this kind of letting go, and voluntarily restrict oneself, to something that is manageable. however, poor. It is, indeed,

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but how many after aiming to do that actually are able to sustainably, implement that. Well, by definition, the monks. And if you look at a society and the percentage of any society that is made up of monks, you realize that this alternative is not going to be ever embraced by a sizable amount of people. So yes, a world of abundance is now here, and it is going to be available to more and more people in every part of the world. And we have to learn how to live together with this abundance in many areas. If we don't, well, the quality of our life will decline. Paradoxically, not only when we live in a world of scarcity, but also when we live in a world of abundance. We want quality to increase. So, learning about this world is the only way to make sure that we progress, rather than regress.

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Thank you for following this episode of the context. If you like it, I invite you to support on patreon.com at the address patreon.com Thank you for following this episode of the context, if you like it, I invite you to become a fan, a supporter, a benefactor, on patreon@patreon.com, slash David Orban

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network diversity:
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Network Diversity Phase:
  how it works?
Actionable Insight:

N/A

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 Groups:

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

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:
N/A
?
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.


Reset Graph   Export: Show Options
Action Advice:
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Structural Gap
(ask a research question that would link these two topics):
N/A
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):
N/A
?

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
N/A

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):
loading...
 ?  

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):
loading...

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. 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
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Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Network Structure Insights
 
mind-viral immunity:
N/A
  ?
stucture:
N/A
  ?
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.

Modularity
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Topics Nodes in Top Topic Components Nodes in Top Comp
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Nodes Av Degree Density Weighed Betweenness
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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.
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:

   advanced settings    add data manually
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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