×  ⁝⁝ 
Export the Data

Network Graph Images:

The graph images for publishing on the web or in a journal. For embeds and URLs use the share menu.
PNG (Image)  SVG (Hi-Res)

Network Graph Data:

The raw data with all the statistics for further analysis in another software.
JSON  CSV  Gexf (Gephi)

Visible Statements (Tagged):

Export the currently filtered (visible) statements with all the meta-data tags (topics, sentiment).
CSV (Spreadsheet)   TXT (e.g.Obsidian)  

All the Text:

Plain text used to create this graph without any meta-data.
Download Plain Text (All Statements)
Share Graph Image

Share a non-interactive image of the graph only, no text:
Download Image Tweet
Share Interactive Text Graph


Save This Graph View:


Delete This Graph:


About this Context Graph:

total nodes:  extend
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.

Does it happen to you that someone calls your phone and you respond, start talking? And then wonder, why is this person even calling me because we have so many alternatives available today. And using those alternatives can be much better than not resorting to the convenient to the caller, but lazy option of the old fashioned voice conversation. The ability to use take advantage, properly configure, but also upgrade and maintain these options is pretty essential in today's technological world. For everyone, not only for a specific class of people, business professionals, for example, in end, when the person is unable or unwilling to learn the subtleties of the various options and settings, their consequences or their implications. It is really cringe inducing. My name is David Orban. And this is the context. The ability to communicate in convenient, different ways and supported by technology is not new, has been around for at least 100 years. But maybe even more if we want to be

   edit   deselect   + to AI


generous and include, of course, the art of the correspondence, writing letters to each other. I don't want to cover that ancient art that some hopefully still support. But I want to concentrate on what is happening Actually, today. It used to be the case that you would stand in your living room, and the when the phone rang 3040 years ago, oh, it really represented something that you were looking for. You wouldn't know who called there was no caller ID a and it could be hopefully a positive surprise. It could be also negative, you just had no way of knowing. And you answered the call and spoke to the person on the other side. Here is another big difference as well. It wasn't the case that 99% of the calls received unannounced, would be spam or scam. Today, the scenario is very different. When you do receive an unannounced call, yes, at least in my case, 99% of the time, it is someone trying to sell me something that I didn't ask for. Or even worse. They are trying to pull me in a

   edit   deselect   + to AI


scheme that is fraudulent. The failure of the telecommunications providers to stop these spam calls, even diverse variety, robo calls, sway are the other side is just the machine and waiting for you to say something so that it can route the call to a human is indeed very indicative of its lack of understanding of the world and being in sync with the needs of their customers. Traditional companies

   edit   deselect   + to AI


want the on land on one did call phenomenon to last as long as possible. But of course, they are just prolonging the unavoidable death of the phenomenon and potentially also of the platform. So given that, it is definitely the case that if you want to talk to me on the phone, in an abstract fashion, it can be whatever type of platform, you should make sure that I have your number that it is in my contact list. And maybe you should make sure that I'm available. And I know what you want to talk about. And I know how long you are planning to talk. So what I am describing is basically what it looks like a calendar invite, it can be something less formal. And it can be just a couple of messages on WhatsApp or telegram or whatever other chat system to say, hey, I'd like to talk for 15 minutes about x y or z tomorrow at three Are you available it I do like calendar invites my family and friends recognize it and and laugh about it, because I send calendar invites to them for almost

   edit   deselect   + to AI


everything, I just find it convenient and complete, relaxing. managing information flow is important our attention span as well. And that is why these tools are so useful, that is what I mean about them being relaxing, because I can concentrate on what matters, the substance of the communication, the decisions that flow from it, agreeing to go to the movies together or settling on a partnership or a business deal and not having to worry about the details and the mechanics. So, when we take advantage of these tools at their optimum, it really becomes something that can free up your attention and delegating the mundane details to the platforms and tools helps a lot. These assistance these software agents are the substitute literally, of what was only available 100 or 150 years ago to the ultra rich employing servants. For example, in London, the phenomenon of the visiting card was universal, you would send your servant to someone that you wanted to correspond with or to actually visit

   edit   deselect   + to AI


and the the visiting card would have your name actually there was the detail that when the visiting card was left by the servant, one corner would be covered that would be turned and then the other person if welcomed your potential visit would send a servant back with his or her visiting card to your residence. You didn't need to be there your servant would receive the card and the details would be consequently agreed upon. Elaborate slow, complicated with all kinds of superstructures like the turned over corner of the card built in the system and

   edit   deselect   + to AI


evidently only available to very few people today in the visiting card has been substituted by the business card. A, it includes phone numbers and email addresses, and the visiting card has died. Even the business card is on a way of going out, for many reasons, the most important of which is that electronic communications are now even more effective and faster than not juggling, and managing the paper costs that the business costs were. The interesting part is not necessarily or only. What is the support mechanism. The interesting part is what are the conventions around their use. Just like in the past, a, the way that you handed your business card to the other party would be important and needed to be understood, especially in countries like Japan, where there is an entire ritual around them. Today, how you actually approach another person on a platform like LinkedIn, or how you start a chat session is leading to a positive engagement or the abandonment of the session and in the

   edit   deselect   + to AI


closing of have the opportunity. Being able to talk to someone. So directly on a call, or a video conference, which is now becoming universal. I do like to see the other person and encourage the camera to be turned on. Accepting when it cannot, is wonderful. And it must be handled with care. That is why knowing how to develop the tools, adapt, configure them, and how to then take advantage of the opportunity of speaking practically to anyone on the planet is an opportunity that we have to treasure and we have to take advantage of. Do you want to talk to me? You are absolutely welcome. And I am looking forward to be able to do that. As long as you tell me, what would you like to talk about? How long do you believe the conversation will last? Is there any intended outcome and we can proceed from a very lightweight chat to a calendar invite. And then finally, to a video call, wherever you are in the world. Thank you for watching or listening to this episode of the context. If you liked

   edit   deselect   + to AI


it, you can support it on Patreon by becoming a fan a support or sponsor or benefactor on patreon.com slash David Orban.

   edit   deselect   + to AI


Show Nodes with Degree > 0:

0 0

Filter Graphs:

Filter Time Range
from: 0
to: 0

Recalculate Metrics Reset Filters
Show Labels for Nodes > 0 size:

0 0

Default Label Size: 0

0 20

Edges Type:

Layout Type:


Reset to Default
Language Processing Settings:

language logic: stop words:
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse structure:
×  ⁝⁝ 
×  ⁝⁝ 
Network Structure Insights
mind-viral immunity:
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.

Influence Distribution
Topics Nodes in Top Topic Components Nodes in Top Comp
Nodes Av Degree Density Weighed Betweenness

Narrative Influence Propagation:
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.
×  ⁝⁝ 
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:
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:
Structural Gap
(ask a research question that would link these two topics):
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):

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

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):

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):

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 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
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
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):

     advanced settings    add data manually

Sign Up