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InfraNodus
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Explore the main topics and terms outlined above or see them in the excerpts from this text below.
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Welcome to the context. My Name is David Orban and I want to talk to you today about the breathtaking ambition of our memetic evolution. Memetics has been introduced as a concept by Richard Dawkins in his book, the selfish gene written and published in 1973 a few years ago means in this book, in one of the last chapters where the analogs of genes in a cultural context, just like genes are the units of biological evolution. Richard Dawkins ask himself, could there be a unit of cultural evolution? Now, today, 40 years later and more, we have associated the word meme to funny images on the Internet with a silly captions or maybe more serious ones, but still this punchy, extremely compact transmission of an idea in picture form. But memes are not only the transmission of ideas via pictures, it can be anything, a song, a political message, a patent.

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It can be any expression of our intellectual production. And as the production, storage and transmission of our intellectual production has become ever more evolved and effective over the course of millennia, we have been able to collect and deploy ever more powerful applications of our culture, of our technology to build societies weed, larger and larger impact. Now, it turns out that genetics has been, eh, um, through the effort of scientists that, um, started to understand how DNA and proteins and the other components of our biology interact and how genes, um, units of a biological expression can be activated or can be these activated. Well, we have been able to develop a technology we call genetic engineering that aims to improve how the genes work and the applications of a genetic engineering have appeared in many different places from genetically modified organisms. GMOs that, for example, are widely used in the United States of America, Eh, both as a animal feed as well as, um,

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food sold in supermarkets for people.

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Humans are blogged in Europe where GMOs are not permitted to be sold for human consumption. But since, for example, almost the totality of the soy that has grown in the world is genetically modified animals feed in Europe as well as genetically modified as well as other applications of genetic engineering, even more ambitious to be able to overcome certain genetic defects in our own bodies that lead to illnesses, often a very, very grave illnesses. So genetics and genetic engineering have been successful in tracing a scientific exploration of theories and experiments and applications through technologies that is far from being a fully blossomed because we have so much still to understand. The same has not happened with mathematics and memetic engineering. As a matter of fact, a, a quiet it tight illustration of this scientific failure is that, eh, the main avenue of scientific progress.

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The publication of scientific results in academic journals didn't happen with a, the science of memetics. There used to be a scientific journal and Academic Journal of, of, of papers called the journal of mathematics, but it closed. It wasn't able to, to keep publishing the academic findings that peer reviewed papers that would support the publishing of, of this journal. Does that mean that uh, memetics as a scientific theory is a wrong, and or that a, the concept of means is a useless, it may simply be that we didn't still have at the time, 20 years ago after the publishing of the Dawkins a initial concept and the attempt of creating around it a science and applications. We didn't have the support and the homeless, the infrastructure, the live ecosystem of participants on which to develop theories and on which to apply our experiments.

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And it could very well be that we now have it. We have social networks with the billions of participants. We have all kinds of applications, the highlights of which are the abuses of privacy squandering applications that we have seen highlighted uh, recently as well. Whether our Facebook can or cannot reform itself in order to find a business model that is not based on the exploitation of the personal data of its users to feed advertisers that give money to Facebook in order to target these users in a a very fine grained manner that wasn't possible before and finding this new business model, Facebook is going to be able to anchor itself towards a future that is more respectful of individual privacy or whether Facebook is going to be unable to achieve these kinds of reform and some new platform is going to emerge.

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It is my opinion that memetics as a science and memetic engineering as the application, hopefully in positive directions of how we can nurture the evolution of culture and the application of this evolution in our technologies, in our societies in many different ways. Well, I hope that this is going to evolve and emerge rapidly and that we will have the ability to fully exploit what is now available to us because today the rich ecosystem of social networks and participation by billions of individuals gives the opportunity to establish the validating of these theories and to test them through experiments and improve the theories through better and better experiments that wasn't possible before.

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This kind of hypothesis can be potentially tested. We can ask ourselves after Cambridge Analytica that famously to get advantage of certain loopholes in the handling of Facebook data with or without active support by Facebook itself. Still potentially to be established from a judiciary point of view through the various lawsuits that are still ongoing, but pretty likely that since that is the business model of Facebook itself through at least an implicit support and availability of that data to be exploited well beyond Cambridge Analytica. Are we going to have a new generation of applications that we can then analyze and we can connect to our theories of how networks of people can generate a support infrastructure that can lead to positive outcomes?

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From my point of view, it is undeniable that our ideas evolve and that our ideas evolve it under pressure from reality, we'd, which they have to confront themselves and through this confrontation only the fittest ideas survive. These ideas are about the world. But these are ideas are about ideas themselves. How to think better, how to model better theories about the world, how to evaluate theories and how to compare theories so that the better theories can be applied and lead our successive experiments. The fact that we need better tools for thinking is I think evident given the challenges that we are facing. Challenges of a complex society, Challenges of an environment that is ever more defined and designed by humans and our need to both support ourselves as well as our curiosity and ambition to go beyond and explored the world and explore beyond the world

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to look how we can fulfill the promise of knowledge and a promise of curiosity that is driving human behavior and gives embodiment to our dreams. A wonderful book that supports this kind of thinking is intuition pumps by Daniel Dennett intuition pumps, eh, and other tools for thinking is a, a toolkit, a toolkit to be a modern thinker that doesn't reinvent the wheel and doesn't stop at emulating blindly, Eh, the tools for thinking that ancient philosophers devise the thousands of years ago, but upgrades and updates itself with tools that are more modern and more effective and have proven themselves to be useful in designing and evaluating fit theories about the world. And not only eh, theories that are static, but theories that are leading to fruitful conversations, dialectic and dynamic confrontations, debates and consensus that can be applied to decisions that are political, technological, and that drive society for what? Because whether it is arms control, climate change, economic

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inequality, application of novel technologies like Genetically Modified Organisms, advanced research in healthcare for potentially extending the human lifespan or many, many, many other areas. We need clarity of thinking. We need inclusive understanding of the way for what we need, a clear leading light that can achieve actionable results. We cannot run in circles. We cannot repeat dogmatic positions that Harken back to relatively primitive understanding of the world and of the human condition. Our ambitions have grown. Think about it.

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The life of a person 200 years ago or in in older times was short marrying after puberty was universal. Having a lot of children because a lot of them would die potentially losing your wife at childbirth statistically, almost certainly losing half of your children or more before the age of five and certainly mathematically before the age of reproduction, this kind of life could not be seen, cannot be seen as ideal by us today. It is so far from the way that we live our lives today that we can hardly imagine how it was. I personally wouldn't be able to understand the psychological condition of somebody who sees half of their children die. It's just unfathomable to me. That is the distance that we have come and that is the basis from which we are now building our ambitions and we have been able to achieve what we have achieved because of the tools for thinking that we accumulated. We have applied scientific theories to agriculture, to home building, to our economies and to the way that

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we understand eh, microbes and bacteria and viruses and how we can counter their deadly effects.

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All of these memetically evolved components of our accumulated knowledge created the society we have today. So for me, science and reason and improvement of how we work together in order to understand the world and face and solve and overcome our challenges is the way I had it is not abandoning how we look at the world. It is not throwing away the tools that we have found through millennia. It is not resorting once again to blind, ignorant dog man. It is not conflictual. It is not exclusive. It is not blind to the suffering of other people. It is enlightened. It is inclusive. It is empathetic, and very importantly, for those who look at this attitude skeptically, this attitude is not in conflict with emotions. Beauty being in all with the incredible complexity of the universe, feeling both empowered and at the same time very small. When you look at the mysteries that we still have to unravel, feeling love for other human beings, for nature in one thing, the flourishing of human beings

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and nature, appreciating art and poetry. These are old capabilities of the human life and the human existence that are not in contrast with being rational and being scientifically minded. I feel a little bit embarrassed having to highlight this and having to remark this, but it is the case that those of us who look at the world and want to find a rational,

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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:
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(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:

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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:
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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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Structural Gap
(ask a research question that would link these two topics):
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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


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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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