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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.

The Aesthetics of Tension and Release

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Tension and release are the basic building blocks of a wave. A wave transfers an energy impulse through a medium using a succession of tension and release.

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The only difference between an impulse and nothingness is the tension that the impulse brings with itself. Therefore, a release that follows is an important backdrop as it is this backdrop that makes it possible for us to perceive tension.

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Tension and release are also the basic building blocks of any communication. The basic way to communicate something is to be able to say "yes” and for every "yes” (1) there is also a "no” (0).

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Emotions are also based on tension and release. They build up (towards the crest of the wave), and come down (towards the trough). They have an amplitude and they tend to dampen (with time or as the energy dissipates). Music is based on tension and release and so is any form of art.

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Most interaction is based on tension and release. In a complementary interaction, we oppose each other (left); in a symmetric interaction, we escalate (right). This sort of pattern can be clearly seen during negotiations, conflicts, and arguments, but also in arms races, stock markets, and trade wars.

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Tension Release through the Body

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If tension and release are so omnipresent, what would it look like on the level of the body?

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A body that gets tense and that releases tension. A collective body that does the same.

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On the level of a singular body we can invoke ritualistic dances (tension = possessed by a spirit), representation of states or concrete forms in a classic form (ballet), boogaloo, locking, and popping.

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Possessed by technology — a robotic dance:

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The tension between the liquidity of the body and the rigidness of technology. Embracing the progress or fighting against it. Flowing around the 1s and 0s, curves versus squares.

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Releasing the tension against the society norms, finding one’s own truth — butoh and the new forms of breakdance:

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Dance as a release of tension. Escalation / deescalation technique.

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Generating the tension and then finding a way to flow out of it, to transform oneself or a situation into something else. Encountering a new obstacle, new tension, releasing it, letting go, fighting against it, winning or losing, moving on.

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Collective Tension / Release and the Catharsis of Violence

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A collective body that dances will also consist of a succession of tension and release.

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Traditional stories play out dramatic events, generating a build-up to produce a catharsis (release). Through that re~lease we re~solve a situation (an archetypical or an imaginary event) and discover more options or at least a faint idea of hope.

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Some situations can also be resolved through ongoing practice. Martial arts offer an outlet to channel this flow.

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A continued meditation on violence, modulated escalations and deescalation of tension in order to know how to attack and how to respond.

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A succession of tension and release is used to process a situation. To create an inner discussion, to communicate, to pass information, to agitate a medium, to be heard, to speak, to ex~press, to tune in a frequency, to receive a transmission, to start a tsunami, to prevent it from happening.

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The Underlying Dynamics

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A dance of tension is a dance that is ultimately about the existence of a system. If the tension is too high, the system will eventually break (and this may be a desired outcome — how to escalate the tension, so that there’s a change). An inner riot.

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The absence of tension, on the other side, is entropy, randomness, death. No tension, no release, no in~formation.

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There are also ways to modulate the dynamics of tension, a dance of pulsating tension and release, waves, oscillations.

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Physical movement allows exploring this dynamics to the fullest extent using the most basic means. Body as a laboratory. Pain is a very efficient control mechanism. It is the most immediate indicator of the state of a system. Dance is a way to perform this exploration in a way where the outcome is not necessarily practical and maybe aesthetically pleasing. Martial art is also a formalized way to research and explore this continued interplay between escalation and deescalation.

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To maintain non-equilibrium stability where a change is welcome, a system must be adaptable. If there is tension, it can respond with tension (escalation) or release (deescalation). A general meta-view is necessary to modulate the dynamics.

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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:
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(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:

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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:
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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
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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
(frequency / time) ?
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 frequency of occurrence.

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


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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 better results (slower). Standard model is based on a fixed AFINN dictionary and works faster, but for English only.

Text Statistics:
Word Count Unique Lemmas Characters Lemmas Density
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Network Statistics:
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Top Relations / Bigrams
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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).
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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