How much does it cost to get tested for the coronavirus? Unfortunately the answer could depend on how good your insurance is - MarketWatch
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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.
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Coronavirus: Spain nationalizes all private hospitals, enters lockdown - Business Insider
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Americans Are Excessively Eating, Drinking, Smoking Pot, Playing Video Games And Watching Porn While Quarantined
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The Virtual Work Skills You Need — Even If You Never Work Remotely
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Toyota to Build Prototype City of the Future | Corporate | Global Newsroom | Toyota Motor Corporation Official Global Website
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Coronavirus compassion: The kindness postcard and other heartwarming gestures
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How to use Houseparty to see friends during coronavirus outbreak - Business Insider
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Data Visualization Example
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Chinese WeChat Users Are Sharing A Censored Post About COVID-19 By Filling It With Emojis And Writing It In Other Languages
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Builder.ai launches pre-packaged apps for small businesses hit by COVID-19 | TechCrunch
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Coronavirus: Louis Vuitton owner to make hand sanitiser for hospitals | Business News | Sky News
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Class and COVID: How the less affluent face double risks
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Impossible Foods raises $500 million, mostly from Asia investors - CNN
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Coronavirus: Spain to become first country in Europe to roll out universal basic income | The Independent
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Coronavirus: Angela Merkel urges Germans to stay at home over Easter
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US accused of 'modern piracy' after diversion of masks meant for Europe | World news | The Guardian
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Calibrating the COVID-19 Crisis Response to the SDGs | United Nations
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Brazil gangs impose strict curfews to slow coronavirus spread | World news | The Guardian
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The Weird Stuff We’re Buying On Amazon Gives Insight Into ‘The Quarantined Consumer’
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The conversation | Seth's Blog
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What are countries doing to combat coronavirus? | World Economic Forum
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"Pandemics: Lessons Looking Back From 2050” by Fritjof Capra and Hazel Henderson | EthicalMarkets.com
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Mapping Impacts and Implications - Phase Change - Medium
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Coronavirus will bankrupt more people than it kills — and that's the real global emergency | The Independent
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Self-care Apps for Remote Work
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Declaração de Eduardo Bolsonaro sobre coronavírus provoca crise diplomática com a China - Jornal O Globo
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Coronavirus: How the deadly epidemic sparked a global emergency | Four Corners - YouTube
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Coronavírus gera censura na web da China, que teme transparecer descontrole - 07/02/2020 - UOL Notícias
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Worst-case coronavirus scenario: 214 million Americans infected, 1.7 million dead | MIT Technology Review
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Li Edelkoort says that the coronavirus will save the planet — Quartz
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Coronavírus: MP 927 de Bolsonaro permite que empresas fiquem 4 meses sem pagar salário a trabalhadores
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Fernando Reinach: Governo precisa levar em conta custo psicológico do isolamento
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Renata Cafardo: Sem aulas por causa da doença, desigualdade no ensino aumenta
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The Coronavirus Pandemic Will Change the World Forever
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Is It Time to Rethink Globalized Supply Chains?
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Yuval Noah Harari: the world after coronavirus | Free to read | Financial Times
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Zeina Latif: Em tempos de coronavírus, ter plano de voo na economia será essencial
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Eight Emerging Lessons: From Coronavirus to Climate Action
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Celso Ming: Epidemia reforça o caminho da globalização
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Lourival Sant'Anna: Oportunista, doença acelera jogo geopolítico
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Carlos Pereira: Governo corre o risco de ser visto como responsável pelos efeitos da pandemia
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Daniel Martins de Barros: Precisamos nos esforçar coletivamente contra a epidemia
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Robson Morelli: Com o coronavírus, o esporte para no mundo todo e o torcedor perde a sua alegria
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Leandro Karnal: Medo é uma das coisas mais lucrativas inventadas pelo ser humano
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Why Are People Panic Buying Toilet Paper? | IFLScience
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Norte da Itália reduz poluição depois de isolamento causado pelo coronavírus - 17/03/2020 - Mercado - Folha
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Futuro incerto? O que pensam os futuristas em tempos de crise social - Comportamento, Identidades - Consumidor Moderno
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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:
⤓ ? ⤓
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:
N/A
+ ⤓ ? ⤓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:
N/A
+ ⤓ ↻ ?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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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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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.
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
(frequency / time) ?
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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
positive: | negative: | neutral:
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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).