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InfraNodus
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Welcome to The Context this week. We are going to look at what is the context for Libra, the digital payment system announced by Facebook that is going to launch in 2020. But of course, this is the first episode of this new weekly video series. So it is important that I explained to you a little bit why it has been born and what is the context for The Context.

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I have always refrained from commenting on current news. On one hand, I didn't believe that I would have necessarily something to say on what was being reported in the media on a daily basis or even a monthly basis. And on the other hand, I felt and still feel that the value of such commentary diminishes very rapidly with time. What is the value of talking about what a given celebrity or a given politician did in a month or a year or 10 years? I always felt that it would be important to talk about the meaning of what was happening in a broader view. So a broader context. The title of this video series came up in a conversation with my friend Massimo Curatella. I really want to use the opportunity to deepen our shared understanding of the implications of what is going on in the world.

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The Context is going to be posted on a weekly basis on Patreon. It is a rhythm that I haven't kept in the past. And this is a really an interesting challenge for me as well as for the Network Society Media Team, Emil Olaru, Graciela Marquez, and all the others that are helping me putting the videos together as I travel. I will try to record the videos either during the week or at a maximum on Friday so that they can be edited on Saturday and posted on Sunday. And I will also, of course, welcome your questions, your feedback, your input, both on what should The Context address and analyze as well as what possibly I should go back to give a further context for.

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So let's talk about Libra. Facebook obviously is the winning social network on the planet would over 2 billion, accounts. There are certainly no precise information available on what is the number of people that are using Facebook and what is the definition of using Facebook even whether somebody's taking a glimpse on their newsfeed once a month counts as a user or, or not compared to others who are obsessively going back many times a day or even more dose who post original content. But these undeniable that compared to other social networks, Facebook has been able to come up ahead. So the announcement that Facebook is going to develop and launch a new digital payment system called Libra, matters. Matters, because what other company can say, "Hey, let's launch something and see what the initial number of users he's going to be”.

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Let's say that a one in a hundred of our users is going to try it. And right out of the bet they can leverage 20 million users. Nobody else is going to be able to do that at least for the moment. So that is a privileged situation, but of course what emerged in the days preceding the announcement and the details that are available today and we need more information available afterward is whether Libra is a blockchain based cryptocurrency and whether If it is, it is going to be competing with Bitcoin, other cryptocurrencies, whether it will be hindering or furthering, their broader acceptance.

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We will see in also other episodes of The Context of how important the transformation of the world economy towards a deeply natively digital system is. Whether it is resource extraction, production, distribution, use, recycling. Whether it is about services that are designed, implemented and delivered. These are all, in some parts already and very decisively and natively going to be done digitally in the modern world.

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The role of money in this context, of course, is going to be digital as well. Whether we are talking about the store of value, a unit of account or a medium of exchange. And Libra occupies this last position. It is a digital solution for representing the modern medium of exchange. It has been compared with Bitcoin but they are actually at the polar opposites of how one could go about realizing the modern digital payment system. Bitcoin is decentralized. It is permissionless. It is borderless. The creator of Bitcoin is pseudonymous as well as the users and the holders of Bitcoin are pseudonymous.

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Libra is literally the opposite of each of these features. Libra is a centralized, even though Facebook established a foundation for its management, the Libra network itself is centered on a few gatekeepers. Libra is censurable and the transactions that are going to happen on Libra are not purely peer-to-peer. And they are disintermediated. The gatekeepers, the nodes of their transaction network, are going to be able to deny certain transactions from happening. And Libra is geographically bound. Necessarily the governance of the foundation, the operations of the Libra wallet and many other components are going to very, very strictly comply with the laws and regulations of each geography where it operates. And while Bitcoin is global, Libra is not going to be able to be global. Certainly, it is not going to operate in Venezuela, in Iran, in North Korea, in Cuba.

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And possibly it is not going to operate in China, which is quite important with 1.5 billion people and the main competitors of Facebook, having been born and having achieved huge success in China. As a matter of fact, one could argue, that the WeChat integrated payment system accepted by hundreds of millions of Chinese consumers and merchants is what Facebook is desperately trying to catch up too.

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So, if bitcoin and Libra are so different, to the point where many people don't even believe it is technically correct to call Libra a cryptocurrency and its network a blockchain (even though these terms are so popular that Facebook and Libra are going to want those terms to be applied to them) the question is: is it going to hinder or further the adoption of bitcoin and other cryptocurrencies or is Libra even going to be successful? And if it is going to be successful: is this going to help in the digital transformation of our economy?

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I think that what is undeniable by even the fiercest critics of bitcoin and cryptocurrencies is that, thanks to this announcement, bitcoin and cryptocurrencies now must be taken into serious consideration by anybody in the financial world, in the entrepreneurial world. There were too many people who even after 10 years and billions and tens and hundreds of billions of dollars of value having been garnered by the blockchain world, we're still pretending that it didn't matter.

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So just through the announcement of Libra, this has been, I would say, an extremely positive consequence that is going to reverberate some of the recent shifts in the bitcoin exchange rate with respect to the US dollar where bitcoin appreciated that quite a lot could be attributed to the positive sentiment generated by this perception.

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The other undeniable information directly attributable to this announcement is that Bitcoin, the blockchain world, and cryptocurrencies left immense opportunities that can be leveraged both by traditional corporations like Facebook as well as by new startups in terms of diffusion, in terms of ease of use in terms of generating a geographical penetration or signing up millions, hundreds and hopefully billions of users. That is the aim of Libra and I am sure that the Libra wallet will be excellent in terms of, for example, ease of use and one of the components of the Libra announcement: the development and the deployment of an ATM. So that will allow the purchase of the Libra coin with fiat money all over the world, where legal, is also going to help a lot of people to come on board.

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So is this an unfettered positive news? Well, for many years, some since a few years more, we have been aware of how deeply flawed Facebook's model and culture is in terms of privacy. So it is deeply worrying that the centralized censorable system is being developed and it is very likely to be successful where the financial transactions of users are going to be scrutinized, collected and exposed to the practices that our social media platforms have accustomed us to suffer. So that is an area where we have to be extremely careful. An underappreciated component of the Libra announcement is that necessarily due to deregulations that Libra will comply with in terms of onboarding people, their identities will be extremely precisely ascertained, and this attribution of a digital identity is also going to be a very important component of what Libra is going to become.

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I will be happy to go back in a few months, but certainly next year to see what other developments we have seen around a Libra in the meantime. And of course, after it is launched and I will be experimenting with it and have possibly a new episode of the context and give further context.

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The next episode of the context next week is going to talk about why Tesla is very likely going to stop selling cars. And also what is the context of the Giga factory, which is in many ways, Tesla’s most important product. So if you want to hear about The Context about Tesla, come back next Sunday to watch the new episode. In the meantime, please send me questions, give me your thoughts about the current episode and how you would like this video series to shape up in the future.

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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:

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+     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):
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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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