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Your Project Notes
Interpret graph data, save ideas, AI content, and analytics reports. Add Analytics
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.

landscape model of reading, priming, cooccurrence graphs

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This model captures both on-line comprehension processes during reading and the off-line memory representation after reading is completed, incorporating both memory-based and coherence-based mechanisms of comprehension. http://www.brainandeducationlab.nl/downloads

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A central component of successful reading comprehension is the construction of a coherent memory representation of the text. https://www.questia.com/library/journal/1P3-440581011/a-landscape-model-of-reading-comprehension-inferential

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The model is based on the premise that, during reading, the ideas and concepts associated with the text fluctuate in their activation. The result is a dynamically shifting landscape of activations. Two factors contribute to the shape of this landscape: readers' limited attentional resources and their attempts to maintain standards for coherence. https://www.questia.com/library/journal/1P3-440581011/a-landscape-model-of-reading-comprehension-inferential

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Priming is an implicit memory effect in which exposure to one stimulus influences a response to another stimulus. http://en.wikipedia.org/wiki/Priming_%28psychology%29

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Priming can occur following perceptual, semantic, or conceptual stimulus repetition. For example, if a person reads a list of words including the word table, and is later asked to complete a word starting with tab, the probability that he or she will answer table is greater than if they are not primed. http://en.wikipedia.org/wiki/Priming_%28psychology%29

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Semantic priming is theorized to work because of spreading neural networks.[9] When a person thinks of one item in a category, similar items are stimulated by the brain. Even if they are not words, morphemes can prime for complete words that include them.[16] An example of this would be that the morpheme 'psych' can prime for the word 'psychology'. http://en.wikipedia.org/wiki/Priming_%28psychology%29

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Context priming works by using a context to speed up processing for stimuli that are likely to occur in that context. A useful application of this effect is reading written text.[18] The grammar and vocabulary of the sentence provide contextual clues for words that will occur later in the sentence. These later words are processed more quickly than if they had been read alone, and the effect is greater for more difficult or uncommon words http://en.wikipedia.org/wiki/Priming_%28psychology%29

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Priming is a nonconscious form of human memory concerned with perceptual identification of words and objects. It refers to activating particular representations or associations in memory just before carrying out an action or task. For example, a person who sees the word "yellow" will be slightly faster to recognize the word "banana." This happens because yellow and banana are closely associated in memory. Additionally, priming can also refer to a technique in psychology used to train a person's memory in both positive and negative ways. https://www.psychologytoday.com/basics/priming

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In computational linguistics, word-sense induction (WSI) or discrimination is an open problem of natural language processing, which concerns the automatic identification of the senses of a word (i.e. meanings). http://en.wikipedia.org/wiki/Word-sense_induction

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The main hypothesis of co-occurrence graphs is assuming that the semantic of a word is represented by means of co-occurrence graph, whose vertices are co-occurrences and edges are co-occurrence relations. These approaches are related to word clustering methods, where co-occurrences between words can be obtained on the basis of grammatical [8] or collocational relations.[9] HyperLex is the successful approaches of a graph algorithm, based on the identification of hubs in co-occurrence graphs, which have to cope with the need to tune a large number of parameters. http://en.wikipedia.org/wiki/Word-sense_induction

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Visualize co-occurrence graph from document occurrence input using R package 'igraph' http://planspace.org/2013/01/30/visualize-co_occurrence/

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java - large-scale document co-occurrence analysis - Stack Overflow http://stackoverflow.com/questions/21090020/large-scale-document-co-occurrence-analysis

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This study used graph analysis to investigate how age differences modify the structure of semantic word association networks of children and adults and if the networks present a small-world structure and a scale-free distribution which are typical of natural languages. All networks presented a small-world structure, but they did not show entirely scale-free distributions. These results suggest that from childhood to adulthood, there is an increase not only in the number of words semantically linked to a target but also an increase in the connectivity of the network. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-79722014000100011

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Syntactic ambiguity resolution in discourse: Modeling the effects of referential context and lexical frequency. Regression analyses demonstrated that the effects of discourse context at the point of ambiguity (e.g., selected) interacted with the past participle frequency of the ambiguous verb. Reading times were modeled using a constraint-based competition framework in which multiple constraints are immediately integrated during parsing and interpretation.

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http://psycnet.apa.org/buy/1998-12790-010

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The Empirical Approach to the Study of Media Framing Like other concepts of mass communication research, the term framing has also appeared in popular discourse-particularly that of media critics, politicians, and campaign insiders. Noam Chomsky https://www.taylorfrancis.com/books/e/9781135655921/chapters/10.4324%2F9781410605689-12

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Framing as a theory of media effects In this paper I systematize the fragmented approaches to framing in political communication and integrate them into a comprehensive model. I classify previous approaches to framing research along two dimensions: the type of frame examined (media frames vs. audience frames) and the way frames are operationalized (independent variable or dependent variable). https://academic.oup.com/joc/article-abstract/49/1/103/4110088

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Discourse and Text: Linguistic and Intertextual Analysis within Discourse Analysis diverse approaches to discourse analysis can be enhanced through systematic use of these two forms of analysis, even those which claim a concern with the content rather than the form of texts. It is suggested that textual analysis needs to be based upon a multifunctional theory of language such as systematic-functional linguistics http://journals.sagepub.com/doi/abs/10.1177/0957926592003002004

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Strength of Discourse Context as a Determinant of the Subordinate Bias Effect an immediate influence of prior discourse information on lexical processing; and (2) that the strength of discourse constraints can play a governing role in lexical ambiguity resolution https://www.tandfonline.com/doi/abs/10.1080/713755861

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Identifying policy frames through semantic network analysis: an examination of nuclear energy policy across six countries The discourse of top-level decision-makers is analyzed to highlight similarities and differences in policy frames and to identify the key policy arguments in the integrated network of all six countries. https://link.springer.com/article/10.1007/s11077-015-9211-3

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Discourse Network Analysis https://books.google.de/books?hl=en&lr=&id=sWEwDwAAQBAJ&oi=fnd&pg=PA301&dq=text+network+analysis+discourse+bias&ots=brkYG_ejxd&sig=oKSQq2UtN_Sg8okdgRmh2GZU0QM&redir_esc=y#v=onepage&q=text%20network%20analysis%20discourse%20bias&f=false

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Collocations in context: A new perspective on collocation networks The idea that text in a particular field of discourse is organized into lexical patterns, which can be visualized as networks of words that collocate with each other, was originally proposed by Phillips (1983). This idea has important theoretical implications for our understanding of the relationship between the lexis and the text and (ultimately) between the text and the discourse community/the mind of the speaker.

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http://www.jbe-platform.com/content/journals/10.1075/ijcl.20.2.01bre

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Language and Discourse Analysis with Coh-Metrix: Applications from Educational Material to Learning Environments at Scale Coh-Metrix including methodological and practical implications for the learning analytics (LA) and educational data mining (EDM) community. http://lak13.learning-analytics.info/journals/index.php/JLA/article/view/4330

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Analyzing Discourse and Text Complexity for Learning and Collaborating https://link.springer.com/book/10.1007%2F978-3-319-03419-5

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A semantic network analysis of associative frames associative frames and the method of semantic network analysis to the PR research field. By building on a more advanced understanding of communication as process of social meaning construction that is embedded in networks of differential relations between different actors, https://www.sciencedirect.com/science/article/abs/pii/S0363811111000944

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Rhetorical structure theory studies relations between different parts of a text http://www.cis.upenn.edu/~nenkova/Courses/cis700-2/rst.pdf

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Semantic Network Analysis as a Method for Visual Text Analytics a method for visual text analytics to support knowledge building, analytical reasoning and explorative analysis. Semantic networks are analyzed with methods of network analysis to gain quantitative and qualitative insights. Quantitative metrics can support the qualitative analysis and exploration of semantic structures. https://www.sciencedirect.com/science/article/pii/S1877042813010227

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Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports http://www.jbe-platform.com/content/journals/10.1075/prag.21.4.07pol

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Complexity of Word Collocation Networks: A Preliminary Structural Analysis We analyzed graphically and statistically how the global properties of these networks varied across different genres, and among different network types within the same genre. Our results indicate that the distributions of network properties are visually similar but statistically apart across different genres, and interesting variations emerge when we consider different network types within a single genre.

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https://arxiv.org/abs/1310.5111

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Discovery of Collocation Patterns: from Visual Words to Visual Phrases - collocation helps develop better image recognition and categorization systems https://ieeexplore.ieee.org/abstract/document/4270247/

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Word co-occurrence networks are one Global topology of word co-occurrence networks: beyond the two-regime power-law of the most common linguistic networks studied in the past and they are known to exhibit several interesting topological characteristics. In this article, we investigate the global topological properties of word co-occurrence networks and, in particular, present a detailed study of their spectrum. https://dl.acm.org/citation.cfm?id=1944585

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KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor. Our algorithm, KeyGraph, is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each cluster corresponds to a concept on which an author's idea is based, and the top-ranked terms are selected as keywords using a statistic based on each term's relationship to these clusters. https://ieeexplore.ieee.org/abstract/document/670375/

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Small world of human language http://rspb.royalsocietypublishing.org/content/268/1482/2261.short

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Featuring web communities based on word co-occurrence structure of communications: 736 . We present a communication-content based generalization of an existing business-oriented classification of Web communities, using KeyGraph, a method for visualizing the co-occurrence relations between words and word clusters in text. Here, the text in a message board is analyzed with KeyGraph, and the structure obtained is shown to reflect the essence of the content-flow.

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https://dl.acm.org/citation.cfm?id=511542

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ecological ideas

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A way of trapping carbon with "green coal" biochar

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Dumping iron dust in the ocean to remove carbon.

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plant-derived biofuels

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biofuel biochar

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hydrogen-hybrid boats

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hydrogen coal

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drinking fountains

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drinking water resources coal

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community electrical grid fuel

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species relocation resource fuel

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ecological - clever use of resources, renewing resources, producing new energy, moving relocation to the new parts of the spectrum

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ocean water

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water and carbon — fuel and medium.

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moving like water and burning carbon - breathing

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moving resources, like water and carbon around you, small-scale and big-scale

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interactions among organisms and their environment - ecological

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organisms and resources in the environment

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biodiversity, distribution, biomass, and populations of organisms, as well as cooperation and competition within and between species.

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Ecosystems are dynamically interacting systems of organisms, the communities they make up, and the non-living components of their environment.

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How can we save resources, produce the new ones, make it possible to survive, to live and develop physically, when we consume, when we lay waste — on every context. And how we can also direct energy within and modulate interactions.

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A practice of ecological operation on the physical, personal, and collective levels.

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how to relocation and redistribute energy through ecological interaction

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Correcting Between-Participant Discourse Bias in Comment Classification — Text mining and natural language processing have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment classification, with much attention being given to sentiment analysis and opinion mining. We find that content extracted from between-participants' discourse is often highly correlated https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2601942

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http://www.research.lancs.ac.uk/portal/en/publications/-(f059d10e-5292-4379-8d0d-ce729bfe6373).html

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Acceptable bias?: Using corpus linguistics methods with critical discourse analysis corpus linguistics approaches can improve the objectivity of critical discourse analysis research, Taking a recent project which examined the representation of Islam and Muslims in the British press, corpus-driven procedures identified that Muslims tended to be linked to the concept of extreme belief much more http://www.research.lancs.ac.uk/portal/en/publications/-(f059d10e-5292-4379-8d0d-ce729bfe6373).html

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semantic variability:
×  ⁝⁝ 
×  ⁝⁝ 
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.
Generate AI Suggestions
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:
AI Suggestions  
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Main Topical Clusters:

please, add your data to display the stats...
+     full table   ?     Show AI Categories

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...
+     Reveal Non-obvious   ?

AI Summarize Graph   AI Article Outline

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:
N/A
?
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.


Download: TXT Report  CSV Report  More Options
Discourse Structure Advice:
N/A
Structural Gap Insight
(topics that could be better linked):
N/A
Highlight in Network   ↻ Show Another Gap   ?  
AI: Bridge the Gap   AI: Article Outline
 
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 Connectors
(less visible terms that link important topics):
N/A
?   ↻ Undo Selection
AI: Select & Generate Content
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):
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.
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

 
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...
+   ⤓ download CSV   ?

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):

⤓ Download   ⤓ Directed Bigrams CSV   ?

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:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Network Structure Insights
 
mind-viral immunity:
N/A
  ?
stucture:
N/A
  ?
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
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Influence Distribution
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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) ?   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.

Degree Distribution:
  calculate & show   ?
(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.
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):

 
We will build two graphs:
1) Google search results for your query;
2) Related searches for your query (Google's SERP);
Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
Please, enter a search query to discover what else people are searching for (from Google search or AdWords suggestions):

 
We will build a graph of the search phrases related to your query (Google's SERP suggestions).
Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

 
We will build two graphs:
1) the content that already exists when you make this search query (informational supply);
2) what else people are searching for when they make this query (informational demand);
You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
Please, enter your query to visualize Google search results as a graph, so you can learn more about this topic:

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Discover the main topics, recurrent themes, and missing connections in any text or an article:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in open survey responses:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in customer product reviews:  
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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Enter a topic or a @user to analyze its social network on Twitter:

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