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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.

serendipity is our ability to take advantage of opportunities that present themselves in front of us, mistakenly serendipity is associated to luck. But that is not the case. Our senses, our ability to perceive the world have evolved in a very precise manner to find the fine balance between the maximum amount of information that they can bring to the nervous system for analysis and the capacity of that nervous system to actually come to conclusions, around the information and decide on the next steps, information in order to be useful must be actionable and non actionable information is a burden that evolution has trimmed away from our process of acquiring knowledge about the world. This means that our senses, actively collude in order to make us blind to what is going on. And the fact that this is true, can be fairly easily measured in simple experiments. Not only animals we label more primitive, but humans as well become blind. Both concretely with their eyesight but also more

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abstractly in terms of cognitively ignoring information that is constant, the flow of a scene where nothing changes will become kind of a blur. And only when something changes, our attention will focus on it. Our brain will perk up. A famous scene in Jurassic Park. When the tie of Tyrannosaurus rex is menacing Glee walking by a flipped over, rover and to frighten kids are about to be eaten. They remember the lesson to stay still. Because the dinosaur is not going to see them know whether he's completely true or not, or actually the act of walking presents a scene that is sufficiently variable to eyesight, or more likely in the dark of that particular shot to the I presumed quiet, excellent ability to smell of the dinosaur, and as a consequence, the children would have been eaten is an open question. But, you know, that in a unending Li. A similar and self similar scenario, you become a little bit doll you tone down your state of alertness, your state of consciousness. So when

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something new comes into play. Our senses light up our brain enters a new gear, we are ready to evaluate the new information. Is it positive, is it negative, is it conductive to our future well being, or does it represent the danger to put it. Most bluntly possible. Is it going to eat us, or are we going to eat it, and

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through many, many layers of abstraction that is still definitely the case, whether we are talking about a news item on television or whether we are reading about some technology being developed, we are looking at evaluating our professional skills and asking ourselves, If they match what the world of employment, or enterpreneurship actually needs. Are we a good match. These are all constant states of analysis and evaluation as well as self evaluation that incorporate the new pieces of information. And when this new piece of information becomes available. It is almost like, upgrading the immune system with the types of antibodies that recognize from then onwards, a specific stimulation. There is a scene from a science fiction movie from the 50s of steel robot with laser eyes. And for me, installing that new piece of information about the world is as if you installed laser eyes that are able to scan. With this new ability to recognize and serendipity is laser eyes for analyzing

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reality, you are equipped with a new sense and it is not a question of luck. It is a question of a better evolved, pattern recognition mechanism that. The next day, It looks like everybody's talking about that new thing that you discovered or heard about, while the day before, no one was. This process is, at least in potential bi directional, because you are not a passive receiver, you will talk about that new thing you will share it with your friends and colleagues, you will write about it or record a YouTube video about it. And as a consequence, the world will recognize your new ability to focus on that new thing, your laser eyes will shine through and pierce in a fog of otherwise Undistinguishable. If information ocean.

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The

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coevolution of who you are. Visa V the world, and how the world perceives you. And how you change the world, because of how and what you have become is of fundamental importance. Serendipity. In this sense, is strongly related to your fitness to your being, well adapted, and to your adaptability. And in turn, being adapted and adaptability are related as well, because the excessive degree of adaptiveness may diminish your capacity to change. And as a consequence may cancel your adaptability. So, exploring new things, which increases, stimulating it, the opportunities for serendipitous discovery is, in turn, improving your adaptability, starting from a given state of adaptiveness. And when you live in an environment like ours today that is rapidly changing proactively seeking out novelty proactively stimulating your laser eyes have serendipity is the right thing to do. That is why I keep insisting, go and experiment, try out this, get your hands dirty, Make sure you have first hand

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experience. The alternative is to wither away. It's okay. People are free to choose that. I just hope you want, because the likelihood of a life that you live, that you can evaluate after the fact or Well, in the final moments, let's say, to have been have achievement and satisfaction and completion is, in my opinion lower than not the life, while still full of risks that include the adventures. The Explorations. The discoveries, and the sense of all that serendipity, will bring you

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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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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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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);
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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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