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See the dynamic evolution of this graph: scroll or "play" the text entries to see how the text propagated through the network graph over time.

the final graph

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Project Notes:
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.

Are you worried about bots, and spam accounts and fake accounts on the various social media platforms? Well, you must be assured that today is the day that you meet the fewest of them. Tomorrow, there will be more. Now, you could decide that this worry should be translated into some kind of eradication of the bath problem. But I want to argue that the opposite is true. We must welcome bots, and learn to live together with them in a peaceful and constructive coexistence. My name is David Orban. And this is the context.

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It is definitely the case that on Facebook, on Twitter and every other platform, there are a lot of accounts that do not necessarily correspond to a single human. And that single human doesn't necessarily control that single account. Or they're also genuine, automated, construct constructive accounts. Of course, for example, if an Internet of Things sensor uses tweeter in order to communicate the temperature, the humidity or whatever other parameter that it extracts from the environment, and someone else takes advantage of that value incorporating it in an application after it being publicly available on Twitter. Well, that is a completely automated non human account, which is certainly not spam, not fake. And in this example, definitely useful. So, between those accounts that want to defraud people want to scam people want to trick people into doing something that at the end will harm them on one hand. And on the other hand, very clearly the case of those accounts that while non

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human powered, they are useful. What should we use? What should we decide what should we keep the power towards? Can we expect each account to have an ID card to prove their human identity? And can we pretend that each human should only be able to do it once forever? I don't think that it is reasonable to expect that we should do that. And those platforms that will try will find that it is either impossible or undesirable. The value that we will lose by imposing this is too large. On one hand, we must enable anonymous and pseudonymous participation on online platforms for many reasons. I actually articulated these reasons in another episode of the context. On the other hand, a and this is what I want to concentrate on today. It can be argued that the increase of non human powered human like accounts what you may call, at least today, fake accounts can be valuable, they can play a useful role as well. You may already encounter online accounts that are it resembling a human uncannily so

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to the point where they may appear to be human to a lot of observers that are not aware hear of their background, their history, as it were. But these accounts do not actually hide their non human nature. One of the most famous that you can easily look up on Instagram is named lil me Mikaela L I L MIQEU. L. A lil Mikayla. And the account is a digital influencer with over 3 million followers. And she will go out with her friends take selfies, occasionally, of course, that's her job promote a given brand or a given product.

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It is the creation of an LA based collective digital agency. And it is definitely in the sign of things to come. Because the advantages of these kinds of accounts are just too large to ignore. Well, she will never be tired, she will never grow old, she will never rebel and leave the agency. She can be completely controlled and designed and posed and made to behave the way that the agency that created it wants to. Now of course, an account like this is completely like a puppet. It doesn't, at least for today, have any kind of autonomous system where either the poses or what she posts or the way she interacts. Is is achieved. Everything is done by the puppeteers the creators of the digital agency. But of course, we are seeing more and more ability in AI based systems to actually create images create videos create text, that can interact in a manner with humans that will achieve certain goals. There is an app called replica that will keep you company is human company preferable to a

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digital resemblance, of course, are there circumstances in which if human company is not available, if the digital companion is if we want better than nothing? Yes. And just like the wave of Tamagotchi in the 80s, or maybe 90s, these digital animals, living quotation marks in a gadget where kids had to keep them alive by pressing buttons, feeding them petting them, taking care of them, provoked a lot of reactions, even though they have proven to be a fad and the faded. There will be a lot of reactions around those accounts that pretend to be human while not wanting to trick anyone in believing that they truly are, and certainly not trying to scam them to defraud them. Now to stay on the subject of of scamming and defrauding. There are already laws against all of those, and it is up to law enforcement to prosecute those that scam and the fraud, humans or in any other way, harm humans. So we shouldn't pretend that the platform should be by magic absent of these criminal behaviors of

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human origin and we shouldn't move to later platform, decreasing its value by designing it in a manner that, in our opinion, should a priori prevent a large spectrum of human behaviors, just because that spectrum can also include what we believe, and very often rightly so, to be undesirable. So, what is the solution? In my opinion, bots should self declare as such, and platforms that embrace bots and allow self declared bots to exist on the platforms themselves are going to, in my opinion, achieve an important

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competitive advantage. LinkedIn, for example, privates, bots, but it would be quiet advantages. If I could set between myself and the dozens of connection requests I receive every day, moderately intelligent, automated filter. And why not that filter could have some kind of human resemblance. As long as it would declare to be a bot. The people wanting to connect with me could interact with the bot, which would ask them simple questions. Why would you like to connect? And the pretty brutal way that I typically put it, at least on LinkedIn, do you want to buy or do you want to sell? So this is just a simple example of a platform that could completely flip its stance from prohibiting bots to embracing bots and putting them to good use. And I think every platform should ask themselves the same question. Why are we prohibiting bots? Shouldn't we allow them? And if we do, how should they behave on the platform after explicitly declaring their nature? Let's see if there will be platforms

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that understand that this is the future, because I assure you, every year that it passes, the number of bots is going to increase, potentially, and that is definitely going to be the case may be in a decade or two, eclipsing the number of humans and we should rather than feared future, prepare for it, embrace it, and understand how to thrive in it.

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network diversity:
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Network Diversity Phase:
  how it works?
Actionable Insight:

N/A

Generate AI Suggestions
Your Workflow Variability:
 
Shows to what extent you explored all the different states of the graph, from uniform and regular to fractal and complex. Read more in the cognitive variability help article.

You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
Phases to Explore:
AI Suggestions  
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Main Topical Groups:

please, add your data to display the stats...
+     full table   ?     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   ?

AI Paraphrase Graph

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:
N/A
?
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:
N/A
Structural Gap
(ask a research question that would link these two topics):
N/A
Reveal the Gap   ?   Generate an AI 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):
N/A
?

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
N/A

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):
loading...
 ?  

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):
loading...

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. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.

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:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Network Structure Insights
 
mind-viral immunity:
N/A
  ?
stucture:
N/A
  ?
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.

Modularity
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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.
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:

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Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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