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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.

Web 1.0 web 2.0 And now web three. Will we ever stop? But probably not. But rather than just being a marketing gimmick or a hype creation machine, web three is going to represent an important evolution of what we can do on the internet. And even if it is going to take some time to fully blossom, you have to start paying attention now. My name is David Orban, and this is the context when the internet was born it was for academic researchers, for technologists, specialists that really cared about the intricate details of protocols, how to connect computers between them. What would be the winning architecture, and everyone outside of their close circle ignored them even with the growth of the internet in the 70s, the 80s When 1000s of computers were already connected, still, it required a lot of knowledge of how to do things even something as simple as decoding the attachment of an email. Something that today, even the less sophisticated the least sophisticated of Internet users does

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every day but then with the introduction of the worldwide web, and especially the first browsers, the Mosaic browser. It had become possible for non specialists to roam the digital superhighway. The first websites were simply the display of information that you would read, and then there would be a hyperlink somewhere when you clicked on it. And you read another page on the same website or maybe magically, you would jump on another website to read the information there. This kind of static connection of web pages and web sites together was already a huge improvement on information retrieval, how information could be gathered. And then at the turn of the millennium, the concept of web 2.0 was born, which was the understanding that web pages and websites could and should display information much more dynamically, and that this information wasn't necessarily only to be absorbed by the users. But the users could indeed enrich this information with their own text and images. And soon

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videos. And this information that was enriching the websites could be correlated displayed to others and others could react to them. And this is how, what is now part of the daily life of many of us in this series of web services was born. Whether social networks or video sharing websites, or the E commerce platforms where for example, we know that we can rely on a product to be fit for the purpose that we want to fulfill because of the reviews that users have submitted. In the each of these services that emerged in the web 2.0 paradigm turned out to be centralized.

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The original architecture of the Internet was peer to peer. But that architecture didn't last because it became very rapidly evident that the users didn't want to maintain their own complex infrastructure. They wanted to take advantage of the features and the benefits that the internet provided without the complications, so this unbalance strengthened the servers and weekend, the clients. Very few normal users keep web servers on premise where they have to maintain it updated, secure it. They have to make sure that it doesn't lose any data that the energy is constantly available through uninterruptible power supplies and the data is backed up and then it can be restored if a hard disk is corrupted. And the original copy is lost, and so on and so forth. No one wants to do that. And that is why a the cloud services that reliably can provide email access for example via G Gmail, or ecommerce services via Amazon, or even the backbones of the virtual servers computers that pretend to

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exist, but in reality they are running on other even more powerful computers have become the dominating paradigm. There have been many, many advantages to this. For example, an internet startup today doesn't have to worry about so many things. When Netscape was born, they were spending millions of dollars in software licenses. They were spending 10s of millions of dollars in bandwidth at a given point. Netscape became inaccessible to the world because some roadwork cut their internet connection. And the backup connection wasn't working either. Well, a startup today has the ability to set up in a few hours or a few days, a server infrastructure on the Amazon cloud, or any other cloud from Microsoft from Google even Alibaba and not having to worry about the security for example of those servers or the provisioning of energy or communication and bandwidth. It can just keep growing with the growth of the demand. From their users. So, this is just one example. But there are so many of

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course, all of us know the attractiveness and the supposed dangers of social media as well. Now, the downside is clear. A complete lack of control of everyone, including those who want to build the next generation of solutions that could disrupt Google and Facebook and Amazon. All of us both users and startups and growing technology companies depend completely on this centralized infrastructure. The next paradigm, web three promises to rebalance this by allowing the execution of scalable solutions that do not be banned on a centralized control, but still promise to hide from the end user. The complexity of the features and provide the benefits in a very easy to use operation

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and provide an exciting set of tools for the startups and the developers who will build in the knowledge that they cannot be exploited the the cannot lose control over their own creation through the shenanigans and the tricks of the technology giants, which today happens very, very often. None of this is yet possible. Web three is mostly a promise is mostly a dream. But it is an important dream. And when enough people dream to achieve something Well, there is no guarantee but oftentimes iterating towards it. Making a lot of mistakes. But improving over the achieve that dream. We have dreamed about the ability of streaming high definition movies in millions of homes. And today it is possible whether it is YouTube or Netflix or so many other streaming services today. Well what has been only a dream is now possible and the abundance of the choices that we have for really good movies or TV series if we want to watch them is almost without limit. The dream of web three have a better

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balance and the distributed and decentralized control of the information of unstoppable services that are both reliable as well as they are a platform for empowerment and emancipation of billions of people is going to take time to fully develop maybe 10 years. But we are already seeing the glimpses of web three in Blockchain technologies in the certainly overhyped NFT fad that is laying the grounds of solutions that will persist and will be important through the metaverse that will definitely not be the reom controlled and owned by by Facebook by so many exciting new projects that are being experimented today. The most important component of web three is the challenge of a digital identity that is reliable, but is also flexible. Our digital identity must have multiple facets, if I need to be identified with my traditional passport, so be it. But if I want to be pseudonymous on some new platform that gathers to one of my passions well I should be able to do so. And finally, I should

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also have the right to be anonymous. This flexible identity system doesn't exist yet. And it is going to be a cornerstone of web three applications. There are other components as well. And that based also your feedback. I will be happy to analyze them and to share my understanding of their evolution with you. Thank you for listening to this episode of the context. It is always a pleasure to think together with you. If you want to support my efforts, you can do so very easily by going to patreon.com/david Orban and joining others who wants to hear more week after week of what is going on in our world, why it matters and why you should pay attention. Thank

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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:
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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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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.
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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):
N/A
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:
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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.
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