How to Boost Your Mind Viral Immunity
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TL;DR Diversity is the key
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In a recent Joe Rogan interview Elon Musk expressed his sympathy for the anti-globalization movement. One of the main reasons, according to Musk, was that the increasingly interconnected world created a fertile ground for all kinds of memes, concepts, and mind viruses that could "infect" a large portion of the population.
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We don't know whether it was an implicit reference to the Coronavirus craze, but this conversation was happening during their discussion of Neuralink — an implantable device Musk is working on, which would enable the humans to wirelessly transmit thoughts and visuals, creating an interconnected network of reality simulation devices. According to Musk, that would happen in about 5 to 10 years and given his history of making the impossible things become the reality, the likelihood of that being realized is quite high.
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In this context the notion of mind viral immunity becomes very important and Elon Musk realizes it himself. It is already difficult enough to deal with the multitude of inputs coming at us from every direction. What happens when their number increases exponentially? How can we protect ourselves from everything being available at any moment of time?
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How do we ensure that our highly interconnected society does not turn totalitarian all at once, accepting a certain truth as the only truth possible?
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Meditation would perhaps be the best option, but most people are too lazy to practice it regularly. Completely refusing the digital connectivity is another option, but we have already become cybernetic beings with all the devices we use on the daily basis, so even if we refuse the Neuralink brain implant, we still need some mechanisms to protect ourselves from the unlimited information flow.
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So, how can this be done? How can one boost their mind viral immunity?
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Immunization as a Network Diversification Strategy
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The approach that we want to propose in this article is based on network diversification as an immunization strategy.
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If you could, at every moment of time, measure the structural properties of your informational landscape and ensure that it has enough diversity, you would be immune to external influence and avoid the "filter bubble" or "echo chamber" effect where you are only exposed to one type of information and can easily be manipulated into believing that there is only one truth.
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Below we will demonstrate how it works, but first we will introduce some of the basic concepts from network science and epidemiology.
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Immunization has long been studied in epidemiology using the framework of networks (House & Keeling 2009). Every person can be represented as a node and the interactions between them are represented as the edges. Based on this approach we can build a social network graph:
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This particular graph, made using InfraNodus, shows a visualization of the users that tweeted to each other (or retweeted each other) using the term "neuralink". We can see that the central nodes, so far, are elonmusk and spacex, but there is also a few other cliques that can spread information amongst their respective communities but not to each other.
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The structure of the network defines how well information can spread through it.
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So, is this network immune? Largely, yes. In our example we still have too many cliques at the periphery separated from the main component dominated by elonmusk and spacex, so they are not as susceptible to the new information as the immediate following of those two nodes.
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If we were to connect several cliques together, we would get a more connected, but still relatively diverse network consisting of multiple communities that are more densely connected together than with the rest of the network. Such networks are called small-world and this is how humans tend to naturally organize their social circles:
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This network consists of one "giant component", so if only a few nodes receive a piece of information (or a mind virus), they can potentially spread it to the whole network. It is, therefore, less immune than the first network that consists of the disconnected clusters. The advantage is that it has a capacity to unify for a collective action.
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Another advantage is that this network consists of several distinct groups, which means that the new information (or a mind virus) will take some time to spread due to the distance it takes to travel from one clique to another and that we might even witness diverse reactions to this information within different groups.
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In the context of network science small-world networks, consisting of several distinct but interconnected communities, are considered to be resilient and adaptable at the same time. Which, perhaps, explains why we as humans evolved to prefer these structures for our social self-organization.
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Let's make one more step and show how this network may become susceptible as it becomes less diverse. If we were to add more connections between the different distinct groups, the differences between the separate cliques would gradually fade away, and we would get a structure that is much more homogeneous than the previous "small-world" one:
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It's much easier for information to travel through this more interconnected network structure, so the network is much less immune (Kuperman & Abramson 2001, Zhou et al 2007). If elonmusk was to tweet something, the message would quickly reach all the parts of the network. The advantage here is that information spreads fast and it is possible to recruit people for a collective action. You want to have a social network like this if you were to start a revolution.
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At the same time, there are also less differences and less diversity in the system, making it less stable and more susceptible to ideology and external influence (as long as the information from the outside is framed in the same terms as the group's agenda) — not a very good model for a sustainable, resilient society.
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So if you want to ensure your mind viral immunity on the social level, you need to have access to a diverse range of social circles.
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Discoursive Network's Diversity as a Measure of Mind Viral Immunity
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We used the social networks example to demonstrate how the structure of a network can influence propagation capacity of a system. This approach can be applied to discoursive networks as well.
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Our language can be seen as a network. There are multiple approaches in the field of text network analysis that use this representation for topic modeling and discourse analysis. The basic methodology represents the words as the nodes and their co-occurrences are the connections between them.
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Once represented this way, any discourse can be visualized as a network graph whose structural properties will reveal the discourse's diversity (Paranyushkin 2018, 2019).
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For example, this very text, up to this point of the narrative, would be represented by this network (using InfraNodus text network analysis tool):
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The structure of this network is quite interconnected, however, if we look at the measure of modularity (community structure) we will see that it still has several topical groups, which are quite distinct from each other:
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Also, the modularity measure is 0.51 (under the Network Structure) and the distribution of the most influential nodes among the communities is quite high, which means that both influence and meaning are evenly distributed across the discourse. Based on those results the network gets "Diversified" network structure sore and a "High" mind viral immunity score.
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Therefore this text — up to the point we analyzed it — touched upon a variety of topics (Elon Musk and SpaceX, Neuralink, network structure, social structures), and so it is still relatively open regarding the number of ways you can make sense of it. It is not trying to sell you a certain agenda or make you think in a certain way.
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It would also be difficult to "infect" this text with a certain ideology (hence, the high "immunity" score). In order to do it, we would have to make it more interconnected by linking the different parts of the narrative or even rewriting it in a way that would push a certain agenda (e.g. omit the text discourse part and focus on the social dangers of Neuralink).
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To boost your mind viral immunity you need to diversify your informational network. Yet, all the distinct parts also need to be interconnected, so that there is an optimal level of coherency in the thought.
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It should also be noted that there may be situations when you want to have less connectivity. For instance, a really disperse network will make it harder for information to travel. Yet, it opens up multiple gaps for imagination to fill in, which can be very generative for creative purposes.
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Alternatively, if you need to make a point fast, to mobilize, or to achieve quick results, you may need to temporarily increase connectivity. The resulting system will not be stable, but it can easily mobilize and drive a certain agenda.
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After a period of mobilization, you can introduce some new data or information into the network, or integrate the periphery and expel central hubs to increase diversity and, thus, make the whole system more resilient and sustainable again (Pastor-Satorras & Vespignani 2000, House & Keeling 2009).
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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.
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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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.
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.
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.
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:
? (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.
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.
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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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.
positive: | negative: | neutral:
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Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.
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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 and are given a distinct color.
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 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.
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.
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Action Advice:
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Structural Gap
(ask a research question that would link these two topics):N/A
?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.
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
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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.
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
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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 frequency of occurrence.
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).
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.
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
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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
The approach is based on AFINN and Emoji Sentiment Ranking
Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.
Network Statistics:
Top Relations / Bigrams
(both directions):
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).
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).