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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.

Dubai is like being on Mars. It is a pretty unique place that you may want to experience and realize how a hostile environment can give rise to a thriving human community. The major difference, it is 1.0 g gravity rather than 1/3. My name is David Orban, and this is the context.

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Now, I'm exaggerating Of course, in Dubai, air is breathable when you leave the enclosed air conditioned hotels or shopping malls. And that difference is not meniscal. It is actually meaningful because Dubai doesn't need to generate its own air. It definitely needs to generate its own water. And it has to generate its own energy. Well, it is doing all of these things in a manner that is going to radically change in the next few years. Because it is doing it unsustainably and it has to become completely sustainable. Like everything else on planet Earth. And the Martian colonies are going to be obviously the same. A Martian colony that is not 100% sustainable, is not going to survive. And 99.999% is not enough either. I have been here in Dubai, speaking at the coin agenda, Blockchain conference, and I am really intrigued by how unique The place is. Existing outside is hard. Maybe I could survive, maybe not how long? I don't know. And definitely there are people who work and live outside

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of the buildings, construction workers, for example. But to be comfortable, to be able to exist and function and work and think here for an extended period of time requires a completely artificial environment, those of hotels, those of office buildings, or apartments or shopping malls. And this environment is human created, artificial and

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fed by the energy of oil.

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Dubai can exist today only because the geographical happenstance of oil being concentrated in the Gulf region. Just to give you one example of the extreme level of unsustainability. Yesterday, I participated in a rooftop party, a big big bar with music and LCD screens and maybe tables for 200 people, maybe more. It is on top of a racetrack for horses. And it is air conditioned, even if it is in the open. Isn't that mind blowing.

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Dubai has been able to change in the past already. It fashioned itself into the business and financial center of the Gulf region. And even though it was hit hard during the 2008 financial crisis, it was able to survive. And today it appears to be thriving. It fashions itself to be fast Thinking Fast adapting welcoming to experiments of different kinds. And of course, then these experiments have the opportunity, at least theoretically, of extending across the United Arab Emirates and beyond in the Gulf region. Dubai experiments with many things with Blockchain technologies, with the internet of things with smart cities, and is planning one of the largest solar installations in the world, generating the cheapest electricity cheaper even than the electricity derived from oil that they get access to. When you fly into Dubai, you fly across the Mesopotamian deserts. And if you land during the night, you will see a large number of fires in the desert. And you can ask yourself, what are

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those fires, they are the flares of methane gate gas that is not captured from the oil wells. But this methane is burned, burned away into co2 obviously, rather than being let go in the atmosphere where mccain is an even more powerful greenhouse gas than than co2. It is impressive to the see these fires. In a few hours, I am going to speak about the power of Bitcoin mining that is going to design a new energy and financial system on a planetary scale that is going to stabilize the world, and it will substitute the Petro dollar. Today we are spending over $2 trillion per year subsidizing the oil industry and protecting with military force, the various supply chains that move oil all across the continents. This is extremely wasteful. When people complain about the wastefulness of Bitcoin, they don't realize that you could, for example, put a container for Bitcoin mining. And rather than burning the methane of these dozens and hundreds of oil fields, for nothing for no gain, you could

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actually run the Bitcoin miners with that methane burning and then having transformed energy into Bitcoin, you could beam that Bitcoin to be transformed into energy somewhere else, because then you could exchange the Bitcoin and pay someone with Bitcoin in order to give you energy where it is needed. And this is true not only for the Gulf area, and methane flares, it is true for hundreds of hydro electric plants winter in Canada or Patagonia, that are unused have been built, but they haven't been economical. And now they can become economical through Bitcoin mining again. When the age of oil is going to be over, which is a process that has already started, the age of a new infrastructure between Bitcoin and solar and Hydro and batteries is going to be born. And this new infrastructure is not going to suffer from geographical accidents, generating geopolitical accidents, because Hydro and sun and batteries and Bitcoin will be globally distributed, and globally available.

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I am looking for work to learn more about Dubai, and how it is going to keep reinventing itself. Maybe developing sustainable technologies that it can commercialize all over the earth and sell to the Martians until they learn even more, and sell it back to us on planet Earth. Thank you very much for following this episode of the context. If you like it, you can become a supporter on patreon@patreon.com slash David Orban. See you next time

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Language Processing Settings:

language logic: stop words:
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse structure:
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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.
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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:
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:
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:
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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
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);
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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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