limited ability to represent experience,
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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.
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This seems to verge toward the concerns triangulated in the spirituality/boredom connection…something about how to represent experience itself to ourselves…seems like a question that belongs to the realm of the existential.
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writing down the reasons why she would have taken a photograph instead of taking it
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Reminds of mid-century conceptualists’ instruction pieces, plus Kenneth Goldsmith’s huge "uncreative" works that you’re \""not supposed to actually read.” In this case, it's almost like a set of instructions you shouldn’t bother following (unlike, say, Fluxus "instruction pieces"), but ones that in and of themselves have some kind of aesthetic value or function.
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classifying the given situation in order to understand its structure
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Information about information.
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wholly decoded.
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Words as objects. If words are not meant to be "wholly decoded," then the words are about the "manual gesture" that expresses something affective with the expressiveness of the lines left as the "inscriptive trace" (Ingold 2007, 3).
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notations
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Annotation, but also the sense of thoroughness, exhaustive explanation, a compulsion to comment. Reminds of themes of addiction and information sublime David Foster Wallace's work, including his footnotes to footnotes.
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This drawing is fairly small, but many of Voigt's pieces are quite large - certainly as tall as she is. Scale does seem to have a place in the field of objects For Boredom is concerned with. Not all written works are long, but a lot of them are decidedly overwhelming, and certainly some of the novels are, too. Voigt’s are a bit different because you can visually take it all in at once, but still, they are large-scale and highly detailed.
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Pale King and Infinite Jest,
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David Foster Wallace’s Infinite Jest, and especially The Pale King, fictionalize all of their characters and data sets, and attempt to evoke the volume of information behind the inevitably partial fragment it can show us. Wallace attempts an obsessively thorough and resolutely methodical treatment of information management. (Eg. footnotes to footnotes).
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We might contrast this with Thomas Claburn's i feel better after i type to you takes an ephemeral and intensely personal engagement with these processes, desires, and behaviours and allows us to literally lay our hands on them in book form. Claburn’s poem renders personal information doubly public in that it not only highlights something that was leaked, but picks out the unusually continuous and strangely narrative chunk of search queries that appear to sketch a sad autobiography of AOL user
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23187425’s intimate life.
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Insofar as AOL user 23187425’s search queries stand out from among a vertiginous list of other customer entries, they evoke what Martin Herbert said of Jorinde Voigt's "Airport Study (Supersymmetrie) 7": \""a partial, but still overwhelming, informational sublime."
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To retain care and scrupulosity about each detail from within the teeming wormball of data and rule and exception and contigency which constitutes real-world accounting – this is heroism
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In a sentiment echoed by many of Wallace’s IRS-employed characters, day-in-day-out dedication to sorting through data is elevated to a sort of spiritual vocation in The Pale King.
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The real
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In Return of the Real the critic Hal Foster considers "the real" to be art and theory grounded in the materiality of actual bodies and social sites. (As opposed to the "art-as-text" model of the 70s and the "art-as-simulacrum" model of the 80s).
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In Rewiring the Real, Mark Taylor describes the thought of "god" and "the real" as synonymous. One vision of theism he is concerned with is that of Schleirmacher, Schiller, Schlegel, Hölderlin, and Novalis, through to Coleridge, Wordsworth, Emerson, Thoreau, and Stevens, where god becomes identified as the creative impulse immanent in the world. He is also concerned with the ontological thought of Schopenhauer, Kierkegaard, Freud, Poe, Melville, Blanchot, Jabès, and Derrida for whom the real is
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"wholly other, or, in Kierkegaard's words that continue to echo, 'infinitely and qualitatively different'" (4). In the latter tradition, Lacan, following most closely on Freud, is especially associated with the concept of the real. For him, the real is the state of nature from which we have been severed by our entrance into language. It erupts, however, whenever we are forced to confront the materiality of our existence, as with needs and drives, such as for hunger, sex, and sleep.
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ritualistic
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Habit and ritual are so conceptually close in the grid of boredom and spirituality that in The Spiritual Significance of Boredom in the Overload Age I propose the blend-word \""habritual” to capture the co-occurrence of the biomechanical and the psychosocially meaningful aspects.
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Boredom is a head-clearing ascesis
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One of the histories of contemporary boredom is in askēsis, which English dictionaries now define as the practice of severe self-discipline, typically for religious reasons or meditative purposes. Askēsis is the Greek root of ascetic. The original usage of askesis in antiquity, however, did not refer to the self-denial conjured by the image of an ascetic, but to the physical training required for athletic events. Over the years, various valences of athletic stamina and religious devotion from
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different discourses have intertwined. In The Pale King, as the character Lane Dean works silently processing tax returns among a room full of silent people processing tax returns, he contemplates his boredom, combats it, and loses, sliding into some liminal state where he hallucinates a phantom who performs this etymology.
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The phantom refers to the personification of boredom in antiquity as \""the demon of noontide,” who was known to attack monks \""in the stillness of the midday hour and empt[y] the world of any meaning” (Dalle Pezze & Salzani 2009, 8).
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almost anything you pay close, direct attention to becomes interesting
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Michael Raposa (1999) argues that wherever boredom appears in spiritual life, it brings ambivalence; it can both threaten to \""undermine prayer and meditation” and promise to deepen contemplation and renew religious insight.
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Outside its \""simple” form as a passing emotional state, boredom itself represents an ontological position in which the very condition of existence is found to be boring and the world is found wanting for anything intrinsically valuable.
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Yet this aspect of boredom is also just one side of a paradox from religion: as Miller says elsewhere in the Gospel of David Foster Wallace, it is as though \""there is a twist in the loop of transcendence that renders it, Möbius-like, continuous with immanence” (2016, xii).
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Why does boredom seem painful? Shouldn’t it just be boring?
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While self-shattering is nonviolent, there are many other ways that thanatos, the destructive instinct, is twinned with boredom
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Renata Salecl acknowledges the twinning of boredom and aggression when she writes that the society \""which allegedly gives priority to the individual’s freedoms over submission to group causes” (2006) and filters choice through the prism of \""opportunity cost” is one that \""causes aggression towards [the self] and apathy in relation to contemporary social problems which are completely ignored by the emporium of individualist choices” (2013a).
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Sometimes the aggression turns outward, as well. The Internet troll as bored, isolated malcontent is well established as a cultural trope and borne out by empirical data (Sanghani 2013). Liam Mitchell (2013) even ups the ante on this notion by proposing that the troll tackles the \""desire for desires” problem by erecting \""a conscious barrier to unconscious desire” by eliding investment in its principal object, which is amusement at another’s expense, or \""lulz.” In Mattathias Schwartz’s (2013)
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formulation, lulz is \""a quasi-thermodynamic exchange between the sensitive and the cruel”; humour derived from \""disrupting another’s emotional equilibrium.” In pursuing lulz, the troll establishes \""a distance from other trolls (with whom he may or may not feel a bond) and from the people who are governed by normal formations of desire” (Mitchell 2013). Insofar as the troll’s pursuits \""bypass or forestall normal formations of desire, they may be characterized as non-subjective.” This is
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significant because, as Mitchell says, our choices only \""have lasting meaning, for others and for ourselves … when we can be held accountable to our promises,” and this is impossible in a condition of both online anonymity and refusal of subjectivity.
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The study \""Just think: The challenges of the disengaged mind” (Wilson et al. 2014) asked participants to spend some time alone thinking in an empty room. There were three study conditions, in all of which participants generally gave high ratings of boredom. In one condition the experimenters gave people the option of giving themselves a mild electric shock. 67% of men and 25% of women shocked themselves. So goes the saying \""the devil makes work for idle hands.”
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Or more broadly: there are many ways, as Baudelaire said in Les Fleurs du Mal (1857, xxv), that \""ennui makes your soul cruel.”
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entropic
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This is what Edgar Orrin Klapp meant when he wrote in his 1986 Overload and Boredom: Essays on the Quality of Life in the Information Society that \""meaning and interest are found mostly in the mid-range between extremes of redundancy and variety-these extremes being called, respectively, banality and noise” (). Redundancy is repetition of the same, which creates a condition of insufficient difference, while noise is the chaos of non-referentiality, or entropy. In a way, these extremes collapse
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into each other, in that both can be viewed \""as a loss of potential … for a certain line of action at least” ().
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There is perhaps something of "the real" here, as well. Volker Woltersdorff (2012, 134) writes that: The law of increasing entropy is a concept of energy in the natural sciences that assumes the tendency of all systems to eventually reach their lowest level of energy. Organic systems therefore tend toward inertia … Freud identifies the death drive with entropy ... within his theory, the economy of the death drive is to release tension."
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Adam Phillips clarifies the death drive: \""People are not, Freud seems to be saying, the saboteurs of their own lives, acting against their own best interests; they are simply dying in their own fashion (to describe someone as self-destructive is to assume a knowledge of what is good for them, an omniscient knowledge of the ‘real’ logic of their lives)” (2000, 81, cf. 77).
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Like texting, as in Le Fraga's W8ING, Day and other "antiexpressive" or "uncreative" writing can be seen as writing with and through a particular technology of the present, something pursued with the rigor of ritual, something to which is given over astounding time and effort by the maker, and something which might be aimed at self-shattering.
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Antiexpressive writing is \""intentionally self and ego effacing” with tactics of \""uncreativity, unoriginality, illegibility, appropriation, plagiarism, fraud, theft, and falsification as its precepts; information management, word processing, databasing, and extreme process as its methodologies; and boredom … as its ethos” (Goldsmith 2008). Necessarily, antiexpressive works have to contend with the subject in terms of authorial decision-making.
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They tend to refuse \""familiar strategies of authorial control in favor of automatism, reticence, obliquity, and modes of noninterference” (Dworkin 2011, xliv) as a way of responding to the ambivalent dis/enchantments of modernity.
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Each carries a slightly different measure of critique and complicity with the encyclopedic impulse whose origins Jutta Haider and Olaf Sundin (2010) locate in Enlightenment \""assumptions about the public character of information and the desirability of free intellectual and political exchange” (Richard Yeo [2001] qtd. in Haider & Sundin 2010). Likewise, each grapples with Enlightenment ideals of universal knowledge, Romantic visions of heroism, and any concepts of teleology as ways to explain
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the present meaningfully.
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Nonetheless, it seeks to become somehow enchanted with it anyway. Identifying the self-imposed constraints that enable them to explore questions of choice, boredom, patience, endurance, obsession, and iterability, work of this kind can be seen as an \""other” of information overload, and as a cipher for overload boredom described above.
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wal-mart
15349709840000000
If we imagine Claburn retyping a litany of anonymous curiosities and statements, we might wonder whether he was tempted to answer or suggest better-formed questions and propositions, as we might be tempted to do, and as browsers and SMS software are increasingly tempted to do with \""autocomplete” suggestions.
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As they accumulate, en masse certain patternistic features of our search queries materialize. The evolution of computer programming languages used in database management systems and in search engines is toward recognizing these patterns. In 2006, as now, AOL search was powered by Google, which uses natural language processing based on machine learning. Specifically, it uses statistical language models that encode information about how language is used. This information is learned from the web
15349709840000000
by looking at billions of web pages.
15349709840000000
The search spell checker, for instance, would evaluate AOL user 23187425’s line (not included in this excerpt) \""could have gone to detriot” by suggesting corrections for \""detriot” and evaluate these suggestions in the context of the phrase in order to determine the likeliest candidate for what the user actually meant to type.
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But instead of spellchecking and search results, in Claburn’s piece the response to this litany is almost uniform silence. Considering the user’s treatment of the search box for \""confession” (or perhaps prayer), Claburn wonders \""what circumstances prompted the author to converse thus with AOL’s search engine” (qtd. in Dworkin & Goldsmith 2011, 138).
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Whereas usually search engine users employ an information management technique of a call and response - the searcher sends out a phrase and a chorus of search results would reply in their different voices - the second channel of the antiphony is muted in i feel better after i type to you. Stereo becomes mono, and as the syncopation drops out, we’re left with a \""soliloquy” or monologue \""of more than 8,200 queries” (Claburn qtd. in Dworkin & Goldsmith 2011, 138).
15349709840000000
The result definitely does read as a conversation, as the editors (Dworkin and Goldsmith) suggest in the anthology where an excerpt of the poem appears. Perhaps it is like a conversation where the other party had become reticent; maybe the search engine was only listening, just as it was expected to.
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Sophia LeFraga’s (2014) texting reinterpretation of Samuel Beckett’s boredom play Waiting for Godot dramatizes how cycles of waiting, anticipation, disappointment unfold.
15349710560000000
In W8ING LeFraga and another performer sit onstage facing an audience and texting each other rapid, terse little messages about the futility of life, passing time-stamped time, eg.:
15349710560000000
\""Nothin 2 b done.” \""word – I feel like yr right.” \""all my life I’ve been like wtvr b reasonable like you haven’t tried everything yet” \""but the struggle is real”
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In W8ING the performers’ messages scroll by on a screen behind them. Unlike these artists, most texters aren’t within earshot of each other and might spend those blank line breaks puzzling over the accuracy of their readings of the texts, as well as the intelligibility of their own \""Textspeak” (Crystal 2008, 80) - that \""idiosyncratic” dialect where abbreviations economise on keystrokes but threaten ambiguity and misunderstanding.
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But actually the content of these messages is, as Christian Licoppe (2004, 141) said of phone calling, \""secondary to the fact of” messaging. The importance of messaging is precisely in establishing what he calls \""connected presence” - that is, \""the feeling of a permanent connection, an impression that the link can be activated at any time and that one can thus experience the other’s engagement in the relationship at any time” (141).
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The back-and-forth pattern of texting emerges as both ritual and habit; fostering connectivity and yet, in needing to be constantly renewed, blunting experience with inexorable monotony.
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Communication theorist James Carey (1975) differentiates between "transmission communication," which is about the extension of messages in space, and "ritual communication." In the \""ritual model of communication," communication functions as a technology of \""the construction and maintenance of an ordered, meaningful cultural world that can serve as a control and container for human action” (1975, 19).
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But if ritual conjures only the warmth of communion or orderly processing, it shouldn’t; amid the antiphony of message alert tones the \""common mood” is about as likely to be one of anxious or bored waiting as it is likely to be the amity connoted by words like \""connectivity” and \""sharing” so often used to describe the digital social world.
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\""For me as a user,” says the man who developed the "typing awareness indicator" - the technical name for the three dots pictured at the bottom of the W8ING screenshot - if there’s any unease associated with the typing indicator, it’s not from the added immediacy … but a lack of enough immediacy. It tells you that something is going on, but leaves you to wonder what it is. It builds up the anticipation for a profound response only to disappoint you with the inevitable banality of what your
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friend actually says" (Auerbach 2014).
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With some hyperbole, the writer Maryam Abolfazli (2014) agrees: "The three dots shown while someone is drafting a message … is quite possibly the most important source of eternal hope and ultimate letdown in our daily lives. It’s the modern-day version of watching paint dry, except you might be broken up with by the time the dots deliver."
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The expectation that there will be a return message clearly has a power having to do with the anticipation of a message even apart from its content.
15349710560000000
Nonetheless, anticipation is the powerful negative of the same pattern in which reply is the positive. Even as we reach out to others, we put ourselves in the position of having to wait - for something, or maybe nothing, as if for Godot.
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flashes of meaning persist
15349711160000000
Flashes of meaning persist despite an attempt to eliminate meaning. Perhaps this suggests that meaning is impossible to eradicate?
15349711160000000
making a new book with the same words
15349711250000000
Connection to Underworld, "hope of finding patterns where there seems to be nothing but noise".
15349711250000000
sort through the historical and cultural debris of the latter half of the twentieth century in the hope of finding patterns where there seems to be nothing but noise.
15349711390000000
Similar and opposite to Adam S. Miller's statement about Foster Wallace's work in The Gospel According to David Foster Wallace: "The real is full of noise, and more, it’s full of patterns that look like noise."
15349711390000000
many of the authors cited are real and the articles and books noted have actually been published
15349711600000000
Interweaving real and made up authors could be another form of haunting. Mark Taylor describes "the real" and "god" as synonymous in Rewiring the Real. Sparsely including the real may be a way to draw attention to it.
15349711600000000
explaining his editorial procedures and adding his own observations and reflections, which are so extensive that they eventually overwhelm Zampano’s text.
15349711730000000
Similar to the annotations in Foster Wallace's writing (footnotes on footnotes) and Jorinde Voigt's writing. The compulsion to annotate and explain is so strong, that it overwhelms the original text.
15349711730000000
Collecting objects, here in an act of boredom, removes them from their function.
15349711930000000
Craig Dworkin wrotes Arcades in Zero Kerning: "What is decisive in collecting is that the object is detached from all its original functions in order to enter into the closest conceivable relation to things of the same kind. The relation is the diametric opposite of any utility, and falls into the peculiar category of completeness."
15349711930000000
The act of collecting has the potential to transform objects into a function outside of everyday utility and monotony, and fulfill a spiritual need of completeness.
15349711930000000
organizational form of the list
15349712250000000
Listing and collecting items to put them back into their original function rather than remove them from their original function.
15349712250000000
imaginative speculation rather than research
15349712360000000
Interweaving of real with imagined. Similar to Underworld's use of real and imaged sources.
15349712360000000
waiting-w8ing
15349723640000000
piece-object
15349728690000000
evaluate-suggest
15351364940000000
annotate-explain
15351370310000000
typing-indicator
15351371360000000
people-impulse
15351372580000000
destructive-drive-death
15351372850000000
interest-meaning
15351373080000000
indicator-typing-suggest-appear
15351377780000000
bored - boredom
15351377980000000
addiction-compulsion
15351378650000000
compulsion-annotate
15351378760000000
emotional-feel
15351379790000000
partial-information
15351380000000000
The real is the interruption of the basic parameters of human life into people's experience, such as reminding us of death, instincts and drives.
15351386320000000
Information is what allows us to make predictions about a system we don't know, based on what states the system can take on. Information is what allows us to find out what we don't know about the real.
15351387480000000
life-death
15351387690000000
Boredom and the real are both affective confrontations with the conditions of ontology.
15351389810000000
ontological - ontology
15351390230000000
emotional-feel-affect
15351390710000000
spiritual - prayer - spirituality
15351392400000000
prayer-search
15351393110000000
algorithm - instruction
15357359430000000
troll-aggression
15357361760000000
database - data
15357362280000000
process-instruction
15357362610000000
process-algorithm
15357362760000000
process-information
15357363060000000
pattern-algorithm
15357363730000000
god-religion
15357365480000000
affective-drive
15357366430000000
affective-feel
15357366520000000
affective-boredom
15357366600000000
affective-experience
15357366690000000
affective-experience
15357366820000000
emotional-affective
15357367010000000
affective-personal
15357367120000000
affective-state
15357367260000000
affective-society
15357368260000000
social-people-society
15357368420000000
cultural-social-society
15357368890000000
cultural-people
15357369070000000
suggest-attempt-tempt
15357371510000000
suggest-search
15357371730000000
suggest-search-engine
15357371840000000
ascetic-boredom-extreme
15357377020000000
Boredom is a problem of meaning, and it can be the result of information overload or too little information. Meaning is a matter of how things refer to each other. If information is that which allows us to make predictions about what is unknown in a system, then it is a precursor to meaning.
15357384150000000
Insofar as information allows us to make predictions about the nodes that will become the points of reference.
15357384650000000
religious-athletic-ascetic-extreme
15357385770000000
ascetic-prayer
15357385990000000
ascetic-god
15357386150000000
ascetic-body
15357386250000000
mean-refer
15357390130000000
religious - athletic - ascetic - extreme
15357392250000000
#real
15360990740000000
#athletic
15364377140000000
#athletic
15364377320000000
#religious
15364377380000000
religious
15364377470000000
athetic
15364377500000000
author-authorial
15374656290000000
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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.
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.
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.
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.
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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.
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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.
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Main Topical Clusters:
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+ ⤓ ? ⤓ 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.
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:
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+ ⤓ ↻ ?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.
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
? 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 Connectors
(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.
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).
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:
⤓ ? ⤓
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
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...
+ ?
Top Relations in 4-grams
(bidirectional, for directional bigrams see the CSV table below):
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
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:
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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Influence Distribution | 0 % |
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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.
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.