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.
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 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.
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?
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?
Meditation would perhaps be the best option, but most people are too lazy to practice it regularly. Completely refusing 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.
So, how can this be done? How can one boost their mind-viral immunity?
Immunization as a Network Diversification Strategy
The approach that we want to propose in this article is based on network diversification as an immunization strategy.
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.
Below we will demonstrate how it works, but first we will introduce some of the basic concepts from network science and epidemiology.
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:
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.
The structure of the network defines how well information can spread through it.
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.
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:
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 disconnected clusters. The advantage is that it has the capacity to unify for collective action.
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.
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.
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:
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 were to tweet something, the message would quickly reach all 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.
At the same time, there are also fewer 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.
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.
Discoursive Network’s Diversity as a Measure of Mind Viral Immunity
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 discursive networks as well.
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.
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).
For example, this very text, up to this point of the narrative, would be represented by this network (using InfraNodus text network analysis tool):
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:
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.
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.
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).
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.
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.
Alternatively, if you need to make a point fast, mobilize, or 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.
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).
Dmitry Paranyushkin is a researcher with Nodus Labs. He is working on open-source tools that help promote ecological dynamics on the cognitive, psychological, and the physical levels. All the visualizations for this article were made using InfraNodus — an open-source tool for network visualization.
References + Reading List
House, T., & Keeling, M. J. (2009). Household structure and infectious disease transmission. Epidemiology and Infection, 137(5), 654-661. Cambridge University Press.
Kuperman, M., & Abramson, G. (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, 86(13), 2909-2912. doi:10.1103/PhysRevLett.86.2909
Paranyushkin, D (2018). Measuring Discourse Bias using Network Analysis. Towards Data Science (link).
Paranyushkin, D (2019). InfraNodus: Generate Insight Using Text Network Analysis. The World Wide Web Conference 2019, 3584–3589, doi:10.1145/3308558.3314123
Pastor-Satorras, R., & Vespignani, A. (2000). Epidemic spreading in scale-free networks, 13. doi: 10.1103/PhysRevLett.86.3200
Zhou, J., Liu, Z., & Li, B. (2007). Influence of network structure on rumor propagation. Physics Letters A, 368(6), 458-463. doi:10.1016/j.physleta.2007.01.094
This article was also published on Hackernoon in 2020.