Tip: here are the keyword queries that people search for but don't actually find in the search results.
Have been on several video calls since the morning, from interiors of UP and Bihar The fall in #COVID19 cases is dramatic and does not seem to be clearly linked to the stringency of control measures We don't quite understand the behaviour of the #coronavirus 🤔 #CoronaPandemic https://twitter.com/DrAmbrishMithal/status/1402222535912026112
NEWS (of the personal or personnel sort): Our @JenniferJJacobs has won the 2020 Gerald Ford Prize for Distinguished Reporting for chronicling the spread of coronavirus within Donald Trump's White House. Couldn't be prouder of her. An amazing reporter on an amazing team. https://twitter.com/aawayne/status/1402263978420617220
Actress Aisha Sultana said that Govt of India deployed coronavirus as a bioweapon in Lakshadweep. 6 days ago, Samajwadi Party MP ST Hasan said that India is facing second wave of Covid-19 & cyclones because govt interfered in Sharia law. Kon hai ye log? Kaha se aate hai ye log? https://twitter.com/AskAnshul/status/1402285184628625412
Liberals are telling & fooling you that actress Aisha Sultana criticized Praful Patel. That's all they are telling. But, liberals won't tell you that Aisha Sultana actually said that Govt of India deployed coronavirus as a bioweapon in Lakshadweep. She gave this statement twice. https://twitter.com/AskAnshul/status/1403717970535145479
My dad, a few hours north of New Delhi, tested + for coronavirus with a high viral load 4 weeks ago. He was fully vaxed, had mild symptoms, didn’t progress. He’s ~ 80 Two of our relatives, not yet vaxed, otherwise healthy, < 50 yrs, were also + and sadly just passed Get vaxed! https://twitter.com/VinGuptaMD/status/1404182150228811776
As we emerge from the virus, elites at institutions across society are being forced to reckon with the truth: the coronavirus likely came from a lab in China. This is a truth they, along with legacy media and social media outlets, helped cover-up for over a year. https://twitter.com/GOPLeader/status/1405131567136129027
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.
narrative fractality: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?
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.
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify the fractality of this time series (using alpha exponent, closely related to Hurst exponent): uniform (pulsating | alpha <= 0.65), regular (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 and plurality, the narrative should be close to "fractal". For poetry — "complex". For ideological texts — "uniform".
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 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.
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.
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.
Evolution of Topics
(frequency over time)
(according to Latent Dirichlet Allocation):
Most Influential Words
(main topics and words according to LDA):
LDA works only for English-language texts at the moment. More support is coming soon, subscribe @noduslabs to be informed.