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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.

If we believe in the paradigm of jolting technologies, the increasing rate of acceleration of technological change, then we must ask ourselves, what are their consequences. And the most important consequence, for an increasing percentage of people is going to be represented by the fragmentation of their reality. Arthur Clarke, the science fiction author, who wrote the script, and then the book of 2001, Space Odyssey, used to say that any sufficiently advanced technology is indistinguishable from magic. And we tended to interpret that in an uplifting and exhilarating manner, we are telling ourselves, oh, this magic must be beautiful. Turns out, we were wrong about this positive interpretation. Think about it. For the past several hundred years, we trusted our ability to use rational approaches to collect, analyze, and act on data about the world. This is the opposite of magic. In a magical attitude, you do not expect to be able to make testable predictions about your theories of how

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the world works, you are relying on metaphysical assumptions and you are expecting forces beyond your control to take charge, you may be the conduit and you're assumed powers can be important. However, there is no way that you can plan a dependable future on magic. So, this is paradoxical and extremely dangerous, we must be able to reconcile the reality of our world which is becoming more and more complex. And that is frosted for was propelled based on jolting technologies. With the fact that too many of us are throwing in the towel. What are we throwing in the towel about? Well, we are exposed to stimulation, to possibilities to data about the world that we cannot cope with. And we throw in the towel in what is required from us, absorbing this information. acting on that information, adapting to the changes that are not only Coming faster, but they are coming at an accelerating rate great.

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Not only collecting data, analyzing data, but acting on the changes that are not only coming towards us, but they are coming at an accelerating pace, accelerating or even jolting pace. And when we give up, when we decide that it is not possible for us to humanly adapt, that is when we throw in the towel, but we don't throw in the towel, about our own very existence, hopefully, even though depression and suicide are effectively phenomena that could be linked to this existential anguish. When we throw in the towel, what happens is that we stop trusting our ability to interpret the world rationally, but we are still existing as human beings, we need to construct a support system for our reasoning, and whatever we are going to do tomorrow and the day after tomorrow. And that is when we resort to superstition, conspiracy theories, and the way of looking at the world that is the coupled at an increasing degree, from the actions and the plans of those of us who still believe in their ability

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to absorb and to act on the phenomena that define this world.

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This fragmentation of reality, and this resorting to superstition and to conspiracy theories makes an increasing number of people extremely vulnerable, vulnerable to manipulation, vulnerable to exploitation, vulnerable to be enslaved to a set of toxic ideas, that rather than working in their favor, are working to further these stabilize their world. So, the way out of this is twofold. On one hand, we have to recognize and understand that it is not that person's or that community's fault. If they cannot cope, you have to realize there will be a point where you will say the same, you will step back, raise your hand and ask the world to please slow down, stop increasing the acceleration stop jolting and the world The world will look at you and the world will look at you for the briefest of the moments and then shrug and keep jolting. So, realize that the fellow human beings that experience that already are an echo of your future self. And understand that as a community of human beings,

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we must support each other. The second part of the solution of course is to find the right tools to find what can help the people who are already in the process of giving up or have given up and what can allow the people who have not yet given up to postpone, maybe indefinitely that moment and there are tools that are available. in future episodes of the context, we will look at these tools. Let me just give you a glimpse of what I mean. Analyzing, absorbing and acting on data about the world can be done in two complimentary ways, directly through our senses, and our cognitive ability as individuals, or indirectly leveraging the community, both in space and in time, that enables us to enjoy the technological civilization that we have built. So in order to improve our ability to cope with what is happening on an individual level, we must improve our individual cognitive ability. In order to do that, as a community, we must improve what is called our collective intelligence and decision

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making. Both of these are not easy. But we have already done that in the past as well. When we invest in the education of young people, that is in order to provide them with the tools that they can use as individuals. And when we are implementing evolved societies that communicate and aggregate opinions and take decisions, through governance systems, that are well adapted to what is needed at a given moment, we are doing the same on the collective level.

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So the fragmentation of our reality is real. You must not penalize or demonize the people who are more sensitive to this fact. We must work hard in order to support both individuals and our communities. Because as humans are social animals, we cannot exist divided from each other, in our societies per definition, in our entire civilization is not going to survive if we fail in this task.

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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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Influence Distribution
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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.
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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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