InfraNodus -

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Every word will be converted to its lemma (e.g. bricks > brick, taken > take) and will be shown on the graph as a node. Set to your language for more precise results. Switch off to turn off lemmatization and add your custom stop words list below.

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Example: is,the,as,to,in

Synonym Nodes: ? unmerge all

If you'd like some words to appear as one node on the graph, in addition to your default global synonyms list, list the synonyms, one per line.
Example:
machine:machine learning
learning:machine learning

Dynamic Graph Settings

See the dynamic evolution of this graph: scroll or "play" the text entries to see how the text propagated through the network graph over time.

the final graph

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Project Notes:

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.

Tip: here are the keyword queries that people search for but don't actually find in the search results.

[google: coronavirus]

| coronavirus Coronavirus Homepage Welcome New DC Government sponsored vaccination clinics are posted daily, or visit one of the District's 8 COVID Centers. Infants and https://coronavirus.dc.gov/

16705594200000000

[google: coronavirus]

Connecticut COVID-19 Portal: Information, Resources and Assistance Picture of a coronavirus. Hospital Capacity Data. Enter your hospital name to see current patient capacity numbers. See the Numbers. "" CT Schools PreK-12. https://portal.ct.gov/coronavirus

16705594200000000

[google: coronavirus]

COVID-19 | USAGov Learn about what the U.S. government is doing in response to coronavirus (COVID-19). Passports and Travel During the COVID-19 Pandemic. Learn rules and tips for https://www.usa.gov/coronavirus

16705594200000000

[google: coronavirus]

Families First Coronavirus Response Act: Employee Paid Leave ... The Families First Coronavirus Response Act (FFCRA or Act) requires certain employers to provide employees with paid sick leave or expanded family and https://www.dol.gov/agencies/whd/pandemic/ffcra-employee-paid-leave

16705594200000000

[google: coronavirus]

Coronavirus disease 2019 (COVID-19) - Symptoms and causes ... Oct 29, 2022 Coronaviruses are a family of viruses that can cause illnesses such as the common cold, severe acute respiratory syndrome (SARS) and Middle https://www.mayoclinic.org/diseases-conditions/coronavirus/symptoms-causes/syc-20479963

16705594200000000

[google: coronavirus]

Coronaviruses | NIH: National Institute of Allergy and Infectious ... Coronaviruses are a large family of viruses that usually cause mild to moderate upper-respiratory tract illnesses in humans. Building on previous research on https://www.niaid.nih.gov/diseases-conditions/coronaviruses

16705594200000000

[google: coronavirus]

Sign In Esri publishes a set of ready-to-use maps and apps that are available as part of ArcGIS. ArcGIS is a mapping platform that enables you to create interactive https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd

16705594200000000

[google: coronavirus]

H.R.6201 - 116th Congress (2019-2020): Families First Coronavirus ... Families First Coronavirus Response Act. This bill responds to the COVID-19 (i.e., coronavirus disease 2019) outbreak by providing paid sick leave, https://www.congress.gov/bill/116th-congress/house-bill/6201

16705594200000000

[google: coronavirus]

Coronavirus Response | Coronavirus Response Combating Coronavirus Fraud. Report COVID-19 Fraud Contact the National for Disaster Fraud Hotline: 866-720. Fraud Alert: Be aware that criminals exploit https://www.justice.gov/coronavirus

16705594200000000

[google: coronavirus]

Coronavirus (COVID-19) | US EPA View the latest information from EPA and find resources related to Coronavirus (COVID-19). https://www.epa.gov/coronavirus

16705594200000000

[google: coronavirus]

Coronavirus Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will experience mild to moderate https://www.who.int/health-topics/coronavirus

16705594200000000

[google: coronavirus]

Coronavirus Disease 2019 (COVID-19) | CDC Find links to guidance and information on all topics related to COVID-19, including the COVID-19 vaccine, symptom self-check, data, and other topics. https://www.cdc.gov/coronavirus/2019-ncov/index.html

16705594200000000

[google: coronavirus]

Coronavirus disease (COVID-19) Information on COVID-19, the infectious disease caused by the most recently discovered coronavirus. https://www.who.int/emergencies/diseases/novel-coronavirus-2019

16705594200000000

[google: coronavirus]

COVID Live - Coronavirus Statistics - Worldometer Live statistics and coronavirus news tracking the number of confirmed cases, recovered patients, tests, and death toll due to the COVID-19 coronavirus from https://www.worldometers.info/coronavirus/

16705594200000000

[google: coronavirus]

Coronavirus COVID-19 (2019-nCoV) Visit the Johns Hopkins Coronavirus Resource Center where our experts help to advance understanding of the virus, inform the public, and brief policymakers https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6

16705594200000000

[google: coronavirus]

Coronavirus Tax Relief and Economic Impact Payments | Internal ... families, businesses, tax-exempt organizations and others – including health plans – affected by coronavirus (COVID-19). https://www.irs.gov/coronavirus-tax-relief-and-economic-impact-payments

16705594200000000

[google: coronavirus]

Coronavirus Nov 24, 2022 Case and death data will be inclusive of both confirmed and probable cases and deaths. The cumulative county COVID-19 case rate map has been https://www.michigan.gov/coronavirus

16705594200000000

[google: coronavirus]

Johns Hopkins Coronavirus Resource Center: Home Johns Hopkins experts in global public health, infectious disease, and emergency preparedness have been at the forefront of the international response to https://coronavirus.jhu.edu/

16705594200000000

[google: coronavirus]

WHO Coronavirus (COVID-19) Dashboard World Health Organization Coronavirus disease situation dashboard presents official daily counts of COVID-19 cases and deaths worldwide, https://covid19.who.int/

16705594200000000

[google: coronavirus]

Coronavirus in Pennsylvania Get all the latest, up-to-date information on COVID-19 from the Pennsylvania Department of Health. https://www.health.pa.gov/topics/disease/coronavirus/Pages/Coronavirus.aspx

16705594200000000

[google: coronavirus]

16705594200000000

[google: coronavirus]

What Is Coronavirus? | Johns Hopkins Medicine Jul 29, 2022 Coronaviruses are a type of virus. There are many different kinds, and some cause disease. A coronavirus identified in 2019, SARS-CoV-2, https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus

16705594200000000

[google: coronavirus]

COVID-19 | Department of Health Masks are still required in healthcare facilities. Masks are optional in correctional facilities, homeless shelters, airports and on public transit. All https://coronavirus.health.ny.gov/home

16705594200000000

[google: coronavirus]

COVID-19 | Ohio.gov To learn more visit coronavirus.ohio.gov. 4/15/22. 3:43. Make History. Progress and https://coronavirus.ohio.gov/home

16705594200000000

[google: coronavirus]

Coronavirus: Latest news and breaking stories | NBC News A deadly coronavirus, which causes respiratory illness and pneumonia, is spreading around the world. https://www.nbcnews.com/health/coronavirus

16705594200000000

[google: coronavirus]

Covid in the U.S.: Latest Maps, Case and Death Counts - The New ... Coronavirus in the U.S.: Latest Map and Case Count. Updated Dec. 8, 2022. New reported cases. All time. Last 90 days. https://www.nytimes.com/interactive/2021/us/covid-cases.html

16705594200000000

[google: coronavirus]

COVID-19 Response | United Nations What is a Coronavirus? Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases. Find out https://www.un.org/en/coronavirus

16705594200000000

[google: coronavirus]

Assistance for State, Local, and Tribal Governments | U.S. ... Through the Coronavirus Relief Fund, the CARES Act provides for payments to State, Local, and Tribal governments navigating the impact of the COVID-19 outbreak. https://home.treasury.gov/policy-issues/coronavirus/assistance-for-state-local-and-tribal-governments

16705594200000000

[google: coronavirus]

Coronavirus Disease (COVID-19) | SSA About Coronavirus Disease (COVID-19) and Social Security services and help during the pandemic. https://www.ssa.gov/coronavirus/

16705594200000000

[google: coronavirus]

Coronavirus Disease 2019 (COVID-19) The Texas Department of State Health Services (DSHS) is working closely with local, state, and federal partners in responding to COVID-19 (coronavirus https://www.dshs.state.tx.us/coronavirus/

16705594200000000

[google: coronavirus]

Coronavirus Disease (COVID-19) | Occupational Safety and Health ... Coronavirus Disease (COVID-19). Workers from various industries wearing masks. OSHA Requirements. https://www.osha.gov/coronavirus

16705594200000000

[google: coronavirus]

USDA Announces Coronavirus Food Assistance Program | USDA Apr 17, 2020 (Washington, D.C., April 17, 2020) – U.S. Secretary of Agriculture Sonny Perdue today announced the Coronavirus Food Assistance Program https://www.usda.gov/media/press-releases/2020/04/17/usda-announces-coronavirus-food-assistance-program

16705594200000000

[google: coronavirus]

Novel Coronavirus (COVID-19): Home Indiana's Novel Coronavirus Response. On March 6, 2020, the state Department of Health confirmed Indiana's first case of COVID-19, a novel respiratory virus https://www.coronavirus.in.gov/

16705594200000000

[google: coronavirus]

Coronavirus Rumor Control | FEMA.gov Coronavirus Rumor Control. FEMA helps the public distinguish between rumors and facts regarding the response to the Coronavirus (COVID-19) pandemic. https://www.fema.gov/disaster/coronavirus/rumor-control

16705594200000000

[google: coronavirus]

Coronavirus - Maryland Department of Health Visit the Maryland Department of Health's official resource for the Coronavirus Disease 2019 (COVID-19) outbreak. https://coronavirus.maryland.gov/

16705594200000000

[google: coronavirus]

Louisiana Coronavirus | La Dept. of Health The Louisiana Department of Health protects and promotes health and ensures access to medical, preventive and rehabilitative services for all citizens of https://ldh.la.gov/Coronavirus/

16705594200000000

[google: coronavirus]

Coronavirus Disease 2019 County of San Diego public health information on COVID-19. Case data, testing sites, health order, guidance on reopening and gatherings. https://www.sandiegocounty.gov/coronavirus.html

16705594200000000

[google: coronavirus]

COVID-19 in Virginia - Coronavirus Official website for Virginia Coronavirus (COVID-19) Data, information and resources. Find out how to get your free COVID-19 vaccination. https://www.vdh.virginia.gov/coronavirus/

16705594200000000

[google: coronavirus]

COVID-19 Media Page - LA County Department of Public Health Newsletter Signup: Receive updates about COVID-19, Health Officer Orders, vaccines, and more. http://publichealth.lacounty.gov/media/coronavirus/

16705594200000000

[google: coronavirus]

Coronavirus (COVID-19) | HHS.gov Nov 22, 2022 Coronavirus (COVID-19). HHS and our federal partners are working together with state, local, tribal and territorial governments, https://www.hhs.gov/coronavirus/index.html

16705594200000000

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Semantic Variability Score

— 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).

Read more in the cognitive variability help article.

Your Workflow Variability:

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.

Phases to Explore:

Main Topical Clusters:

please, add your data to display the stats...

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.

Most Influential Elements:

please, add your data to display the stats...

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.

Network Structure:

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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.

Reset Graph Export: Show Options

Action Advice:

N/A

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.

Latent Topical Brokers

(less visible terms that link important topics):

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.

Emerging Keywords

N/A

Evolution of Topics

(number of occurrences per text segment)

↻

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).

Main Topics

(according to Latent Dirichlet Allocation):

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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):

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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.

Sentiment Analysis

positive: | negative: | neutral:
reset filter

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

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.

Keyword Relations Analysis:

please, select the node(s) on the graph 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 / 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. 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).

Text Statistics:

Word Count	Unique Lemmas	Characters	Lemmas Density
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Text Network Statistics:

Network Structure Insights

mind-viral immunity:

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stucture:

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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.

The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.

Modularity	0	Influence Distribution	0 %

Topics	Nodes in Top Topic	Components	Nodes in Top Comp
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Nodes	Av Degree	Density	Weighed Betweenness
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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.

propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence)

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.

Degree Distribution:

(based on kolmogorov-smirnov test)

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.