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Your Project Notes
Interpret graph data, save ideas, AI content, and analytics reports. Add Analytics
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 purchase of Twitter by Elon Musk is a concrete step in understanding how the evolution of ideas can be turned into engineering, a desirable future. Mathematics and mimetic engineering will play an increasingly fundamental role in a world dominated by Artificial Intelligence and advanced technologies. My name is David Orban in this is the context

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technology is feeding on itself, it is the expression of our understanding of the world, and how we can structure the implementation of this understanding in a way that serves our purpose. When technology accelerates, or as it is the case, the rate of acceleration is even increasing. ideas matter even more than before, because ideas stop being just a theoretical, philosophical, academic set of discussions among people who dream of what could be, but never step into what they can do. Either because they don't want to or they are not able to. When technology is available, at an increasing level, to everyone, that is when ideas are the fuel the practical catalyst of what an increasing number of people can do, passing on a practical side and restructuring the world around them. When we talk about free speech, we are not only talking about what is worth talking about, but we are talking about what is worth doing. We have to develop the tools that enable us to better compare ideas that are

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worth implementing, against ideas that are destructive, not only because we disagree with them, but because objectively measurably they decrease the quality of life of millions or billions of people. Ideas that preclude constructive futures to be developed ideas that diminish the probability of the Light of Consciousness, pervading the universe. Does Twitter plays such a fundamental role? Well, in the hands of a systemic thinker, like Elon Musk, it can indeed play such a fundamental role. Think about how many ideas he needs to persuade a large number of people to embrace to adopt the consequences of the implementation of these ideas as they change and restructure the world. Moving civilization to renewable energy is an urgent task. How many people are still under the impression that electric cars are not worth using? Because batteries cost money and provoke co2 emissions as they build them? Because the source of electricity of electric cars can be itself polluting the environment, and

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a number of other arguments that can and are debunked over and over again. But still, they take root in the minds of people who as a consequence, are slower to react and slower to embrace these technologies that can and will transform the world moving us to renewable Energy to sustainable economy. And if it is possible to preempt these false arguments, taking root in them, or to debunk the false arguments more efficiently so that they can change their minds, especially if we are talking about regulators and lawmakers. Well, that is a battle worth fighting. That is a platform for the fight of these ideas that is worth improving and refining.

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What about the desire to make spaceflight, more economic, in order to be able to move a larger mass on orbit in order to build an armada of 1000 starships, each potentially bringing 100 people every two years 100,000 people every two years to Mars? Is that a crazy idea? Well, for now, definitely it is. And there needs to be 1000 steps before this science fiction. dream becomes reality. And then when he does, well, there will have to be 100,000 volunteers, either paying or being sponsored, to pay $100,000 each, for the perilous journey to arrive to a dangerous destination to conduct a period of time, or maybe all their remaining lives in a hostile environment. So talk about big hurdle in persuasively spread an idea. And Twitter will be a platform for spreading this kind of idea. Well, or think about a challenge which may be even greater. When neural link, the company that is developing brain computer interfaces, that are going to be implanted in disabled people first shows that the use

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of their interface is beneficial. And after having helped the lives of 1000s 10s of 1000s, hundreds of 1000s of people radically improve through the overcoming of their disability, when neural link after those steps is going to start offering the brain implant as a better input output interface to communicate with computers and with Artificial Intelligence. That will be a quiet fundamental step in understanding what it means to be human, what it means to merge with the technology at a rather intimate level. And when that is accepted, potentially, through the persuasive power of mimetic engineering, the battlefield of ideas that fight and establish what is a desirable future that we want to build that improves the quality of life and the opportunities of billions of people. There will be another step still. What if at that point, humanoid robots are available, and they potentially enable to live alternative lives. Alternative not only sequential, but potentially parallel lives, where

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one of these robots lives in on Mars and other on the moon and a third, a wealth may be in a form that is not humanoid at all. But bringing a human or human like consciousness goes and explores the stars. How will we relate to the possibility of our own consciousness inhabiting one or more of these bodies? What will be the conversations that we are going to have, when these are going to be real possibilities? Now, some of these will never happen, possibly. But can we afford not talk about them? Can we afford to not debate in a healthy, open,

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energetic, but civilized manner? Can we afford not to build the necessary defenses against those ideas that we establish or harmful instead of being beneficial? Now, is Twitter going to be the final platform where the science of mimetics and mimetic engineering turn into concrete day to day reality? Probably not. But the experiment of more rapidly improving the platforms. They then the current management and the current set of employees have been able, under the relentless drive that Elon Musk has been able to instill in the other companies that he owns as well. And similarly, now he is going to instill in Twitter to is absolutely worth it. There has been a satirical tweet that said, Elon Musk and Tesla have already concluded an agreement with Twitter to use the natural language processing AI model that Twitter has in analyzing conversations to improve Optimus, the forthcoming Tesla robot. And while that tweet was satirical, without any basis in reality, well, that kind of synergy and

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that kind of integration among the various companies that Elon Musk builds is natural. So it may become actually reality. I want to thank BJ Mani, for the online conversations that stimulated this line of thinking. I believe that under Elon Musk's ownership, Twitter will accelerate our ability to understand what kind of debates what kind of platforms, what kind of tools we need, in order to accelerate or even increase the rate of acceleration of our ideas. We must understand what works, what does not work, and with our increased ability to turn ideas into action. Being able to do so is something that everyone should aspire to acquire. So let's hope that Twitter will be a step in the right direction under its new ownership to achieve that goal. Thank you

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semantic variability:
×  ⁝⁝ 
×  ⁝⁝ 
Semantic Variability Score
— modulates diversity of the discourse network  how it works?
The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:

N/A

We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).

  1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
  2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
  3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
  4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).

Read more in the cognitive variability help article.
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 Clusters:

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

The topical clusters are comprised of 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 Summarize Graph   AI Article Outline

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.


Download: TXT Report  CSV Report  More Options
Discourse Structure Advice:
N/A
Structural Gap Insight
(topics that could be better linked):
N/A
Highlight in Network   ↻ Show Another Gap   ?  
AI: Bridge the Gap   AI: Article Outline
 
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 Connectors
(less visible terms that link important topics):
N/A
?   ↻ Undo Selection
AI: Select & Generate Content
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 in 4-grams
(bidirectional, for directional bigrams see the CSV table below):

⤓ 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.

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Influence Distribution
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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.
Please, enter a search query to discover what else people are searching for (from Google search or AdWords suggestions):

 
We will build a graph of the search phrases related to your query (Google's SERP suggestions).
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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Discover the main topics, recurrent themes, and missing connections in any text or an article:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in open survey responses:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in customer product reviews:  
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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Enter a topic or a @user to analyze its social network on Twitter:

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