InfraNodus -

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

16598647200000000

[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

16598647200000000

[google: coronavirus]

Louisiana Coronavirus | La Dept. of Health COVID-19 Vaccine Boosters · Kids Vaccines · Quarantine and Isolation Calculator · Youth Ambassador Program https://ldh.la.gov/Coronavirus/

16598647200000000

[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/

16598647200000000

[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

16598647200000000

[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

16598647200000000

[google: coronavirus]

COVID-19 Emergency Relief and Federal Student Aid | Federal ... Coronavirus.gov—The Centers for Disease Control and Prevention provides prevention tips; info about common symptoms; current updates on how many cases there https://studentaid.gov/announcements-events/covid-19

16598647200000000

[google: coronavirus]

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

16598647200000000

[google: coronavirus]

Coronavirus (COVID-19) Response | FEMA.gov May 10, 2022 Coronavirus (COVID-19) Response. In alignment with President Biden's COVID-19 response plan, FEMA will work with the CDC and other federal https://www.fema.gov/disaster/coronavirus

16598647200000000

[google: coronavirus]

Coronavirus Research and Commentary | Science | AAAS 604 Results Coronavirus. The Science journals are striving to provide the best and most timely research and analysis of COVID-19 and the coronavirus that causes https://www.science.org/collections/coronavirus

16598647200000000

[google: coronavirus]

COVID-19 | Department of Health If you test positive, stay home and talk to your healthcare provider about treatment. Masks are still required in healthcare facilities, nursing homes, https://coronavirus.health.ny.gov/home

16598647200000000

[google: coronavirus]

COVID-19 | Ohio.gov To learn more visit coronavirus.ohio.gov. 4/15/22. 3:43. For Your Team and Your Community. https://coronavirus.ohio.gov/home

16598647200000000

[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

16598647200000000

[google: coronavirus]

Coronavirus News and updates about the coronavirus pandemic: Cases in the US, death toll, what you need to know about the virus, how to prepare, how to get tested. https://www.washingtonpost.com/coronavirus/

16598647200000000

[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/

16598647200000000

[google: coronavirus]

What Is Coronavirus? | Johns Hopkins Medicine 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

16598647200000000

[google: coronavirus]

Coronavirus Disease 2019 (COVID-19) Jun 17, 2022 A new coronavirus (2019-nCoV) was recently detected in Wuhan City, Hubei Province, China and is causing an outbreak of respiratory illness. https://www.dshs.state.tx.us/coronavirus/

16598647200000000

[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/

16598647200000000

[google: coronavirus]

Coronavirus | United Nations Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases. Find out more about this novel https://www.un.org/en/coronavirus

16598647200000000

[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/

16598647200000000

[google: coronavirus]

16598647200000000

[google: coronavirus]

16598647200000000

[google: coronavirus]

16598647200000000

[google: coronavirus]

Families First Coronavirus Response Act: Questions and Answers ... The requirement that employers provide paid sick leave and expanded family and medical leave under the Families First Coronavirus Response Act (FFCRA) https://www.dol.gov/agencies/whd/pandemic/ffcra-questions

16598647200000000

[google: coronavirus]

COVID-19 | USAGov Get coronavirus health information and learn where to get tested. COVID-19 Small Business Loans and Assistance. COVID-19 loans, debt relief, and grants can help https://www.usa.gov/coronavirus

16598647200000000

[google: coronavirus]

Coronavirus - Delaware's Coronavirus Official Website Find the latest information about Delaware's response to the COVID-19 pandemic, including testing locations near you and lab-confirmed cases by county. https://coronavirus.delaware.gov/

16598647200000000

[google: coronavirus]

Coronavirus FAQs: What Veterans Need To Know | Veterans Affairs Coronavirus FAQs: What Veterans need to know. Read this page to get answers to questions about COVID-19 testing, vaccines, https://www.va.gov/coronavirus-veteran-frequently-asked-questions/

16598647200000000

[google: coronavirus]

COVID-19 - NYC Health Alert Levels. We are re-evaluating the city's COVID Alert system. Check back here for updates in the coming weeks. We remain committed to transparency and https://www1.nyc.gov/site/doh/covid/covid-19-main.page

16598647200000000

[google: coronavirus]

Coronavirus Disease 2019 (COVID-19) | SCDHEC Coronavirus Disease 2019 (COVID-19). English | Español. Help keep yourself and others healthy. Get vaccinated and boosted, wear a mask when needed, https://scdhec.gov/covid19

16598647200000000

[google: coronavirus]

| Washington State Coronavirus Response (COVID-19) For testing inquiries or results, please contact your health care provider. You can also text the word “Coronavirus” to 211-211 to receive information and https://coronavirus.wa.gov/

16598647200000000

[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

16598647200000000

[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

16598647200000000

[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/

16598647200000000

[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

16598647200000000

[google: coronavirus]

Coronavirus COVID-19 (2019-nCoV) Last Updated at (M/D/YYYY). Total Cases. Total Deaths. Total Vaccine Doses Administered. Cases | Deaths by Country/Region/Sovereignty. https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6

16598647200000000

[google: coronavirus]

COVID-19 Map - Johns Hopkins Coronavirus Resource Center Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) https://coronavirus.jhu.edu/map.html

16598647200000000

[google: coronavirus]

Coronavirus 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 replaced with a map https://www.michigan.gov/coronavirus

16598647200000000

[google: coronavirus]

Coronavirus Tax Relief and Economic Impact Payments | Internal ... Coronavirus Tax Relief · Child Tax Credit. The 2021 Child Tax Credit is up to $3,600 for each qualifying child. · IRS Mission-Critical Functions https://www.irs.gov/coronavirus-tax-relief-and-economic-impact-payments

16598647200000000

[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/

16598647200000000

[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

16598647200000000

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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.

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.

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.

Main Topical Groups:

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

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 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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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)

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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 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:
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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

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 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).

Text Statistics:

Word Count	Unique Lemmas	Characters	Lemmas Density
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Text Network Statistics: