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.
The British government is starting an antibody surveillance program for adults who test positive for the coronavirus in order to develop a better understanding of its vaccine campaign and the immune response to different virus variants. https://t.co/gcX6dGTzD3https://twitter.com/nytimes/status/1429450839719043074
A few years ago, Virginia had a staggering eviction rate. A 2018 report ranked five Virginia cities in the national top 10 for ousting renters. That negative publicity help put the state on a path to stronger tenant protections and falling evictions. https://t.co/BSGUUrwTuzhttps://twitter.com/AP/status/1429453586740695040
🦠 The Delta variant is an ugraded, highly transmissable version of the original coronavirus. 🇩🇪 Germany mandates medical masks indoors. 🇫🇷 France recommends medical masks indoors. 🇺🇸The US has not done either. Per the CDC, homemade & cloth masks are still sufficient. https://twitter.com/kniggem/status/1429467746014728201
Governor Greg Abbott of Texas has tested negative for the coronavirus, four days after testing positive. He said he will continue to quarantine. In a video posted on Twitter, Abbott, 63, credited vaccines with protecting him from serious illness. https://t.co/cqp6U2TD4mhttps://twitter.com/nytimes/status/1429477771110146049
I’ve long compared Facebook to Big Tobacco and here’s the latest parallel: Facebook knew internally that it was facilitating the deaths of thousands of people through misinformation, and not only did nothing about it, but withheld its knowledge from public https://t.co/t6EZdudeNwhttps://twitter.com/AshaRangappa_/status/1429609283788832768
America's intelligence community gave a classified report to President Biden on Tuesday that was "inconclusive about the origins of the novel coronavirus." Do you know what that means? It means that pro-China elements are in charge of America's intelligence community. https://twitter.com/EmeraldRobinson/status/1430944739315097606
Caleb Wallace, a leader in the anti-mask movement in central Texas, became infected with the coronavirus and has been lying in an intensive care unit for the past three weeks, clinging to life, his wife Jessica said on social media. https://t.co/tDn8Cz1Yo6https://twitter.com/nytimes/status/1431421299407130631
PM Modi motivated scientists, doctors to conduct medical research which led to development of anti-coronavirus vaccine in India: Union minister Virendra Kumar Khatik https://twitter.com/PTI_News/status/1431566049539870720
The United States is now reporting 156,242 new coronavirus cases per day, the highest seven-day average since January 29, according to data from @CNN and Johns Hopkins University. https://twitter.com/ryanstruyk/status/1431591569048604674
Caleb Wallace, a leader in the anti-mask movement in central Texas, became infected with the coronavirus and has been lying in an intensive care unit for the past three weeks, clinging to life, his wife Jessica said on social media. https://t.co/LAbbVvCFeAhttps://twitter.com/nytimes/status/1431607512038531072
In May, a California elementary school teacher, who was not vaccinated against the coronavirus, began feeling fatigued with nasal congestion. She powered through and took her mask off to read aloud. Two days later, half her class of 24 had been infected. https://t.co/6EplH1wgRDhttps://twitter.com/washingtonpost/status/1431628162102972417
Rumbles from the Sturgis Motorcycle Rally have hardly cleared from the Black Hills of South Dakota, and the reports of COVID-19 infections are streaming in. Meade County, epicenter of the rally, now has a rate similar to the hardest-hit Southern states. https://t.co/AJlyO2R8kJhttps://twitter.com/AP/status/1431640487136481291
A store doing more than some governors. "In order to combat the skyrocketing coronavirus cases, particularly in the South, Walmart is temporarily closing some locations in order to do some deep cleaning." https://twitter.com/EricG1247/status/1431738812665794561
A silent majority of American workers do want to get back to the office, at least for a few days a week. As coronavirus cases rise and companies keep workplaces closed, these frustrated employees can’t wait to return to their cubicles. https://t.co/ele90OIqRBhttps://twitter.com/nytimes/status/1431768622339444740
I have felt angry, sad, frustrated, resentful and anxious while being trapped in Sydney for this coronavirus outbreak but watching Premier @GladysB go full Pollyanna in her presser on the day infections went over 1200, I am now genuinely *afraid* of the virus. #auspol #nswpol https://twitter.com/vanbadham/status/1431803378154102784
Anti-government protesters escape from a police water cannon with purple dye and tear gas during a protest against the government's handling of the coronavirus disease (COVID-19) pandemic in Din Daeng district Bangkok, Thailand, August 29, 2021. REUTERS/Cory Wright #ม็อบ29สิงหา https://t.co/rk7IB5lqtshttps://twitter.com/jgesilva/status/1431992681547190274
Charlie Kirk, following TPUSA co-founder Bill Montgomery dying of Covid-19, says at a church gathering this evening that if one of his TPUSA employees asks about coronavirus mandates for their workspace, they will be fired. "Such a ridiculous question,” he adds. https://twitter.com/ZTPetrizzo/status/1432121229880741891
Duke University employees have until October 1st, at 10 a.m. ET, to prove they have been vaccinated. Unvaccinated staff who don't get a shot during a seven-day grace period after that will "be terminated and ineligible be rehired at Duke in the future." https://t.co/99y9GTNd53https://twitter.com/davidgura/status/1432132824748990464
Marc Bernier, a conservative Florida radio host who dubbed himself "Mr. Anti-Vax," died after a three-week battle with COVID-19. He was 65. His death was the third this month among conservative talk show hosts outspoken against the coronavirus vaccine. https://t.co/qYBdYvCLE0https://twitter.com/NYDailyNews/status/1432142903493763072
"The industry says this is a consequence of Brexit & the coronavirus pandemic. EU drivers now require a visa to work in the UK, but are not included on the government's shortage occupation list & do not qualify for their definition of skilled workers” https://t.co/Z4mD40MuhDhttps://twitter.com/PeterStefanovi2/status/1432216620282392580
— modulates diversity of the discourse network how it works?
The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:
N/A
We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).
1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage). 2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage). 3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage). 4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).
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.
The topical clusters are comprised of 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.
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.
We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
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.
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.
(concepts with the highest influence / frequency ratio) ?
These nodes have unusually high rate of influence (betweenness centrality) to their frequency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
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).
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.
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.
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.
Concept Relation Analysis:
please, select the node(s) on the graph or in the table below to see their connections...
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 in 4-grams
(bidirectional, for directional bigrams see the CSV table below):
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).
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.
We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
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.
We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.
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.
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):
Please, enter a search query to discover what else people are searching for (from Google search or AdWords suggestions):
Compare informational supply (search results for your query) to informational demand (what people also search for) and find what's missing:
Please, enter your query to visualize Google search results as a graph, so you can learn more about this topic:
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):
Enter a topic or a @user to analyze its social network on Twitter: