The COVID-19 social media infodemic

Matteo Cinelli, Walter Quattrociocchi, Alessandro Galeazzi, Carlo Michele Valensise, Emanuele Brugnoli, Ana Lucia Schmidt, Paola Zola, Fabiana Zollo, Antonio Scala · Scientific Reports · 2020 · social-psychology · 10.1038/s41598-020-73510-5 · record cinelli2020-the-covid-19-social-media-infodemi
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Other fields define these differently the same word, defined another way in another discipline
misinformation 4 fields · engagement 4 fields · dataset 2 fields · interest 2 fields · lack of transparency 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 154 concept tags on this paper, all 154 are grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
questionable reliable news outlets single articles platform Gab volume reactions amplification factor fake news keywords post diffusion of information massive data analysis Twitter Instagram YouTube Reddit engagement interest epidemic models basic reproduction number R0 misinformation spreading patterns rumors’ amplification SARS-CoV-2 virus outbreak global threat disintermediated news cycle infodemic misinformation phenomena social response information is consumed and reported behavioral response of the population public health interventions communication dynamics users’ preferences and attitudes algorithms content promotion social perceptions framing of narratives policy-making polarized groups shared narratives comparative analysis five social media platforms 8 million comments and posts 45 days secondary cases “infectious” individual R0 = 1 mainstream platforms less regulated social media platforms crowdfunded social media social news aggregation collective filtering of information Google Trends’ COVID-19 related queries 1,342,103 posts 7,465,721 comments 3,734,815 users interactions users’ activity long tail dynamics of reactions and content consumption Natural Language Processing techniques word embedding word vector representation clustering procedures cumulative number of new posts each day first situation report specific timings for content consumption interaction mechanisms standard epidemic models monotonic increasing trend growth mechanisms phenomenological models infective process EXP model SIR (Susceptible, Infected, Recovered) compartmental model supercritical engagement rate social contagion phenomena abrupt jump continuous models Media Bias/Fact Check extreme bias propaganda/conspiracies poor or no sourcing lack of transparency overall reactions strong linear correlation growth dynamics re-scaled version re-scaling factor ρ mainstream social media coefficient of relative amplification permeability of a platform Twitter is the most neutral YouTube amplifies questionable sources less Reddit reduces the impact of questionable sources Gab strongly amplifies them interaction paradigm interaction patterns of groups of users tailored communication strategies Reddit dataset YouTube Data API Twitter API stream API search API Instagram Dataset URLs MediaBias/FactCheck (MBFC) Factual/Sourcing Conspiracy-Pseudoscience Pro-Science binary label matching ability Partitioning Around Medoids (PAM) algorithm Skip-gram model distributed representations of words Latent Semantic Analysis (LSA) Latent Dirichlet Allocation (LDA) linear regularities among words English HTML code, URLs and email addresses stop-words cosine distance matrix average silhouette width average pairwise Jaccard similarity word clouds mechanistic models adjusted exponential model data-scarce settings classical SIR model susceptible population infected individuals rate β γ linear regression coefficients R2 values average number of secondary cases Word embeddings EXP E U /E R interaction paradigm imposed by the specific social media information spreading perils of misinformation phenomena dataset R0 > 1 R0 audience and interaction mechanisms cluster words linear relationship between reliable and questionable sources the same curve, a different multiplier volume and reaction come apart the label is on the outlet, and the authors say so the keyword set is the aperture an epidemiological model borrowed for language questionable sources observational-study
Method, as described: keyword-driven collection from five platform APIs over 45 days, 8 million posts and comments, links tagged reliable or questionable from Media Bias/Fact Check, and an epidemic model fitted to obtain a basic reproduction number per platform

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controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
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transfertransfersa method or concept carried in from another field
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The paper

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Every tag on this page

claim_relations 4 · claim_type 1 · concept_edges 12 · concepts 154 · content_tags 12 · contribution 1 · controls 5 · limitations 5 · mechanisms 5 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 16 · transfers 2 · unhoused_relations 10

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facettagfieldconf.whose words
object_of_studyhow COVID-19 information spread on Twitter, Instagram, YouTube, Reddit and Gab over the same 45 days, and whether content from questionable outlets travels differently from content from reliable ones
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object_of_studythe paper's words
research_questionwhether the diffusion of misinformation is a property of the content or of the platform it is on
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research_questionthe paper's words
contributiona five-platform comparison over one keyword set, with an epidemic model fitted per platform, showing that reliable and questionable content share a growth pattern whose ratio depends entirely on the platform
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contributionthe paper's words
claim_typeexistence
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claim_typethe paper's words
phenomenonan infodemic - and a quantity whose value is a property of the environment measured rather than of the thing measured
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phenomenonthe paper's words
methodobservational-study
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methodthe paper's words
mechanismsposts and reactions from questionable outlets grow at the same pace as those from reliable ones inside a platform, so the questionable series is a re-scaling of the reliable one. THE ONLY THING THAT VARIES IS THE SCALING FACTOR, AND IT VARIES BY PLATFORM - which means a single-platform study measures its platform, not the phenomenon
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mechanisms[0]0.95the tagger's words
mechanismsGab posts about 70% as much questionable as reliable content and reacts to it about three times more. Counting posts and counting reactions therefore rank the platforms differently, and the amplification factor exists precisely to separate them
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mechanisms[1]0.95the tagger's words
mechanismsreliability comes from Media Bias/Fact Check at the level of the news outlet. Every article from a listed outlet inherits its label, and every article from an unlisted one is invisible. The paper states the limit and prints the six criteria rather than citing the list, which is what makes the boundary checkable
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mechanisms[2]0.95the tagger's words
mechanismsevery platform's data was gathered by the same Google-Trends-derived keyword list, and on Gab the hashtags were then manually browsed and selected by meaning. What is in the dataset is what those keywords caught, and the comparability across five platforms rests on the list being applied identically
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mechanisms[3]0.95the tagger's words
mechanismsR0 is the number of secondary cases an infectious individual creates. Here the infection is posting about COVID-19, so R0 > 1 means the topic keeps spreading. The metaphor imports a threshold at 1 that the social process did not have
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mechanisms[4]0.95the tagger's words
limitationsreliability is labelled at the level of the news outlet rather than the single article, and the authors name this as the limit of the approach
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limitations[0]0.95the tagger's words
limitationsthe definition of `fake news` may be `inadequate, since political debate often resorts to labelling opposite news as unreliable or fake`
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limitations[1]0.95the tagger's words
limitationsTwitter data before 27 January could not be gathered because of API limitations, so that platform's series starts later than the others
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limitations[2]0.95the tagger's words
limitationsGab has no official API guide, so hashtags were manually browsed and selected by meaning rather than by a documented procedure
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limitations[3]0.95the tagger's words
limitationsthe whole dataset is bounded by a keyword list derived from Google Trends
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limitations[4]0.95the tagger's words
transfersthe basic reproduction number from epidemiology, applied to users who begin posting about a topic rather than to infected individuals
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transfers[0]0.95the tagger's words
transfersa third-party fact-checking organisation's outlet ratings, used as the ground truth for content reliability
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transfers[1]0.95the tagger's words
controlsfive platforms analysed over the same time window with the same keyword set, rather than one
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eliminates: a platform-specific effect being reported as a property of misinformation - which the paper names as the flaw in single-platform studies · outcome: held
controls[0]0.95the tagger's words
controlsthe growth of reliable-source content plotted against questionable-source content within each platform, rather than questionable content alone
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eliminates: a rise in misinformation that is really a rise in posting - the two grow at the same pace, so the ratio is the only thing that carries information · outcome: held
controls[1]0.95the tagger's words
controlsthe amplification factor defined and measured separately from post volume
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eliminates: posting and reacting being conflated - on Gab they point in opposite directions · outcome: held
controls[2]0.95the tagger's words
controlsthe six criteria for a questionable source printed in the paper rather than cited to the fact-checking organisation
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eliminates: a category readers cannot inspect - the boundary is stated even though the labels come from a third party · outcome: held
controls[3]0.95the tagger's words
controlskeyword-driven collection applied identically across platforms, with the Gab hashtags manually browsed because no documented API guide exists
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eliminates: a cross-platform difference that is really a difference in how each platform was searched · outcome (stated by the tagger, no quotation): held
controls[4]0.95the tagger's words
conceptsquestionable
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conceptsreliable
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conceptsnews outlets
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conceptssingle articles
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conceptsplatform
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conceptsGab
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conceptsvolume
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conceptsreactions
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conceptsamplification factor
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conceptsfake news
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conceptskeywords
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conceptspost
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conceptsdiffusion of information
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conceptsmassive data analysis
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conceptsTwitter
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conceptsInstagram
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conceptsYouTube
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conceptsReddit
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also in: Reddit
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conceptsengagement
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conceptsinterest
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conceptsepidemic models
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conceptsbasic reproduction number R0
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concepts[21]0.95the paper's words
conceptsmisinformation
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conceptsspreading patterns
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conceptsrumors’ amplification
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conceptsSARS-CoV-2 virus outbreak
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conceptsglobal threat
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conceptsdisintermediated news cycle
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conceptsinfodemic
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conceptsmisinformation phenomena
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conceptssocial response
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conceptsinformation is consumed and reported
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conceptsbehavioral response of the population
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conceptspublic health interventions
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conceptscommunication dynamics
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conceptsusers’ preferences and attitudes
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conceptsalgorithms
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conceptscontent promotion
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conceptssocial perceptions
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conceptsframing of narratives
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conceptspolicy-making
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conceptspolarized groups
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conceptsshared narratives
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conceptscomparative analysis
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conceptsfive social media platforms
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concepts8 million comments and posts
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concepts45 days
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conceptssecondary cases
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concepts“infectious” individual
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conceptsR0 = 1
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conceptsmainstream platforms
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conceptsless regulated social media platforms
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conceptscrowdfunded social media
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conceptssocial news aggregation
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conceptscollective filtering of information
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conceptsGoogle Trends’ COVID-19 related queries
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concepts1,342,103 posts
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concepts7,465,721 comments
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concepts3,734,815 users
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conceptsinteractions
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conceptsusers’ activity
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conceptslong tail
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conceptsdynamics of reactions and content consumption
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conceptsNatural Language Processing techniques
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conceptsword embedding
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conceptsword vector representation
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conceptsclustering procedures
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conceptscumulative number of new posts each day
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conceptsfirst situation report
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conceptsspecific timings for content consumption
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conceptsinteraction mechanisms
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conceptsstandard epidemic models
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conceptsmonotonic increasing trend
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conceptsgrowth mechanisms
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conceptsphenomenological models
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conceptsbasic reproduction number R0
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conceptsinfective process
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conceptsEXP model
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conceptsSIR (Susceptible, Infected, Recovered) compartmental model
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conceptssupercritical
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conceptsengagement rate
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conceptssocial contagion phenomena
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conceptsabrupt jump
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conceptscontinuous models
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conceptsMedia Bias/Fact Check
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conceptsextreme bias
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conceptspropaganda/conspiracies
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conceptspoor or no sourcing
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concepts[87]0.95the paper's words
conceptslack of transparency
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concepts[88]0.95the paper's words
conceptsoverall reactions
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concepts[89]0.95the paper's words
conceptsstrong linear correlation
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concepts[90]0.95the paper's words
conceptsgrowth dynamics
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concepts[91]0.95the paper's words
conceptsre-scaled version
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concepts[92]0.95the paper's words
conceptsre-scaling factor ρ
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concepts[93]0.95the paper's words
conceptsmainstream social media
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concepts[94]0.95the paper's words
conceptscoefficient of relative amplification
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concepts[95]0.95the paper's words
conceptspermeability of a platform
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concepts[96]0.95the paper's words
conceptsTwitter is the most neutral
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concepts[97]0.95the paper's words
conceptsYouTube amplifies questionable sources less
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concepts[98]0.95the paper's words
conceptsReddit reduces the impact of questionable sources
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concepts[99]0.95the paper's words
conceptsGab strongly amplifies them
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concepts[100]0.95the paper's words
conceptsinteraction paradigm
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concepts[101]0.95the paper's words
conceptsinteraction patterns of groups of users
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concepts[102]0.95the paper's words
conceptstailored communication strategies
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concepts[103]0.95the paper's words
conceptsReddit dataset
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concepts[104]0.95the paper's words
conceptsYouTube Data API
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concepts[105]0.95the paper's words
conceptsTwitter API
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also in: Twitter API
concepts[106]0.95the paper's words
conceptsstream API
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also in: stream API
concepts[107]0.95the paper's words
conceptssearch API
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also in: search API
concepts[108]0.95the paper's words
conceptsInstagram Dataset
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concepts[109]0.95the paper's words
conceptsURLs
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also in: URLs
concepts[110]0.95the paper's words
conceptsMediaBias/FactCheck (MBFC)
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concepts[111]0.95the paper's words
conceptsFactual/Sourcing
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concepts[112]0.95the paper's words
conceptsConspiracy-Pseudoscience
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concepts[113]0.95the paper's words
conceptsPro-Science
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also in: Pro-Science
concepts[114]0.95the paper's words
conceptsbinary label
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also in: binary label
concepts[115]0.95the paper's words
conceptsmatching ability
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concepts[116]0.95the paper's words
conceptsPartitioning Around Medoids (PAM) algorithm
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concepts[117]0.95the paper's words
conceptsSkip-gram model
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concepts[118]0.95the paper's words
conceptsdistributed representations of words
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concepts[119]0.95the paper's words
conceptsLatent Semantic Analysis (LSA)
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concepts[120]0.95the paper's words
conceptsLatent Dirichlet Allocation (LDA)
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concepts[121]0.95the paper's words
conceptslinear regularities among words
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concepts[122]0.95the paper's words
conceptsEnglish
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also in: English
concepts[123]0.95the paper's words
conceptsHTML code, URLs and email addresses
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concepts[124]0.95the paper's words
conceptsstop-words
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also in: stop-words
concepts[125]0.95the paper's words
conceptscosine distance matrix
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concepts[126]0.95the paper's words
conceptsaverage silhouette width
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concepts[127]0.95the paper's words
conceptsaverage pairwise Jaccard similarity
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concepts[128]0.95the paper's words
conceptsword clouds
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also in: word clouds
concepts[129]0.95the paper's words
conceptsmechanistic models
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concepts[130]0.95the paper's words
conceptsadjusted exponential model
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concepts[131]0.95the paper's words
conceptsdata-scarce settings
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concepts[132]0.95the paper's words
conceptsclassical SIR model
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concepts[133]0.95the paper's words
conceptssusceptible population
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concepts[134]0.95the paper's words
conceptsinfected individuals
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concepts[135]0.95the paper's words
conceptsrate β
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also in: rate β
concepts[136]0.95the paper's words
conceptsγ
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also in: γ
concepts[137]0.95the paper's words
conceptslinear regression coefficients
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concepts[138]0.95the paper's words
conceptsR2 values
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also in: R2 values
concepts[139]0.95the paper's words
conceptsaverage number of secondary cases
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concepts[140]0.95the paper's words
conceptsWord embeddings
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concepts[141]0.95the paper's words
conceptsEXP
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also in: EXP
concepts[142]0.95the paper's words
conceptsE U /E R
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also in: E U /E R
concepts[143]0.95the paper's words
conceptsinteraction paradigm imposed by the specific social media
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concepts[144]0.95the paper's words
conceptsinformation spreading
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concepts[145]0.95the paper's words
conceptsperils of misinformation phenomena
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concepts[146]0.95the paper's words
conceptsdataset
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also in: dataset
concepts[147]0.95the paper's words
conceptsR0 > 1
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also in: R0 > 1
concepts[148]0.95the paper's words
conceptsR0
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also in: R0
concepts[149]0.95the paper's words
conceptsaudience and interaction mechanisms
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concepts[150]0.95the paper's words
conceptsQuestionable
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also in: Questionable
concepts[151]0.95the paper's words
conceptscluster words
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also in: cluster words
concepts[152]0.95the paper's words
conceptslinear relationship between reliable and questionable sources
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concepts[153]0.95the paper's words
unhoused_relationsadjusted exponential model → EXP
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The paper indicates the adjusted exponential model as EXP; the gate's same-as cues do not include this wording, so the alias remains unhoused.
unhoused_relations[0]0.95the tagger's words
unhoused_relationsalgorithms → content promotion
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Algorithms mediate content promotion; mediates is absent from the eight governed types.
unhoused_relations[1]0.95the tagger's words
unhoused_relationsGoogle Trends’ COVID-19 related queries → dataset
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The query set filters which platform content enters the dataset; selects-or-filters-into is absent from the governed types.
unhoused_relations[2]0.95the tagger's words
unhoused_relationsR0 > 1 → infodemic
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R0 above one indicates the possibility of an infodemic; indicator-of is absent from the eight governed types.
unhoused_relations[3]0.95the tagger's words
unhoused_relationsR0 → engagement rate
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
R0 is used as a proxy for engagement rate; proxy-for is absent from the eight governed types.
unhoused_relations[4]0.95the tagger's words
unhoused_relationsspecific timings for content consumption → audience and interaction mechanisms
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
Platform timing patterns may depend on audience and social/algorithmic interaction mechanisms; depends-on is absent and the statement is hedged.
unhoused_relations[5]0.95the tagger's words
unhoused_relationsgrowth dynamics → re-scaling factor ρ
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Questionable-source growth is a rescaled version of reliable-source growth, with rho providing the platform-dependent scale; scaled-by is absent.
unhoused_relations[6]0.95the tagger's words
unhoused_relationsConspiracy-Pseudoscience → Questionable
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MBFC outlets in Conspiracy-Pseudoscience are assigned the binary Questionable label; labelled-as by a classifier is absent from the governed types.
unhoused_relations[7]0.95the tagger's words
unhoused_relationsPartitioning Around Medoids (PAM) algorithm → cluster words
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
PAM is used to cluster words from their embedding vectors; used-for is absent from the governed types.
unhoused_relations[8]0.95the tagger's words
unhoused_relationsR2 values → linear relationship between reliable and questionable sources
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High R-squared values confirm the linear source-class diffusion relationship; supports-or-confirms is absent from the eight types.
unhoused_relations[9]0.95the tagger's words
sensesa questionable source - AS DEFINED HERE: a news OUTLET systematically showing extreme bias, promotion of propaganda or conspiracies, poor sourcing, unverifiable information, a lack of transparency, or fake news
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senses[0]0.95the tagger's words
sensesa questionable source - WHAT THE LABEL CANNOT SEE: the individual article. The authors name labelling outlets rather than single articles as the limit of the approach, in the sentence that adopts it
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senses[1]0.95the tagger's words
senses`fake news` - AS A TERM: possibly `inadequate, since political debate often resorts to labelling opposite news as unreliable or fake`
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senses[2]0.95the tagger's words
sensesthe spread of misinformation - AS A PATTERN: the same growth dynamics as reliable content, re-scaled
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senses[3]0.95the tagger's words
sensesthe spread of misinformation - AS A VOLUME: a ratio strongly dependent on the platform, with Gab's reactions running three times the reliable volume
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senses[4]0.95the tagger's words
sensesR0 - AS AN EPIDEMIC PARAMETER: the average number of secondary cases one infectious individual creates, with 1 as the threshold
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senses[5]0.95the tagger's words
sensesInfodemic - misinformation during outbreak management can fragment social response and interfere with disease countermeasures
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senses[6]0.95the tagger's words
sensesCross-platform unit of comparison - the same COVID-19 argument is observed in environments with different audiences and interaction settings
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senses[7]0.95the tagger's words
sensesEngagement distribution - all five platforms show long-tailed user activity even though their interaction volumes differ
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senses[8]0.95the tagger's words
sensesEpidemic analogy - exposure precedes first posting, an already-posting user is infectious, and R0 above one denotes possible infodemic growth
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senses[9]0.95the tagger's words
sensesModel limitation - Instagram's abrupt growth jump drives an implausibly large SIR R0 and cannot be represented by a continuous epidemic model
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senses[10]0.95the tagger's words
sensesSource-class growth - questionable and reliable content share a linear growth pace within a platform but differ by a platform-specific rescaling factor
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senses[11]0.95the tagger's words
sensesVolume versus amplification - Gab has lower questionable posting volume than reliable volume while questionable reactions are several times larger
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senses[12]0.95the tagger's words
sensesRelative amplification - alpha above one favors questionable sources, below one favors reliable sources, and near one is neutral
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senses[13]0.95the tagger's words
sensesOutlet-label aperture - only posts with external URLs matching the MBFC list can receive the study's Questionable or Reliable classification
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senses[14]0.95the tagger's words
sensesPhenomenological versus mechanistic modeling - one reproduces empirical curves without a growth mechanism while the other tries to encode the mechanism
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senses[15]0.95the tagger's words
claim_relationscritiques → single-platform studies of misinformation diffusion, on the grounds that the dynamics may be particular to the environment
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claim_relations[0]0.95the tagger's words
claim_relationsqualifies → the finding that fake news spreads faster and wider than fact-based news, by noting it might be a platform-specific effect
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claim_relations[1]0.95the tagger's words
claim_relationsqualifies → its own reliability labels, by naming outlet-level rather than article-level tagging as the limit and printing the criteria
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claim_relations[2]0.95the tagger's words
claim_relationsextends → epidemiological modelling, by fitting a basic reproduction number to the spread of a topic rather than of a disease
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claim_relations[3]0.95the tagger's words
concept_edgesnews outlets —distinct-from→ single articles
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differs in: what unit the reliability judgement is attached to · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): what unit the reliability judgement is attached to · the relation stated: reliability is tagged at the outlet level from a third-party fact-checker; other studies label individual articles, and the authors name the difference as the limit of their approach
concept_edges[1]0.95the tagger's words
concept_edgesvolume —distinct-from→ reactions
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differs in: whether the platform's difference is in what is posted or in what is reacted to · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether the platform's difference is in what is posted or in what is reacted to · the relation stated: on Gab the volume of questionable posts is about 70% of reliable ones while their reactions are about three times bigger - the volume and the response come apart, and only on that platform Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span compares post volume with reactions; distinct-from is loose.
also in: volume · reactions
concept_edges[2]0.95the tagger's words
concept_edgesamplification factor —operationalised-as→ post
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the relation itself is separately evidenced · strength of the relation: 0.95 · the relation stated: amplification is defined as the average number of reactions a post receives, which separates how much is published from how much is responded to Audit 2026-09-28: DOUBTFUL - read against its span and kept, but E is the average number of reactions to a post; the endpoint 'post' drops 'reactions'.
concept_edges[3]0.95the tagger's words
concept_edgesGab —is-a→ crowdfunded social media
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the relation itself is separately evidenced · strength of the relation: 1 · note: The paper directly classifies Gab as a crowdfunded social medium.
concept_edges[4]0.95the tagger's words
concept_edgesReddit —is-a→ social news aggregation
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: The paper directly classifies Reddit as social news aggregation.
concept_edges[5]0.95the tagger's words
concept_edgesR0 —operationalised-as→ average number of secondary cases
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: The i.e. clause supplies the study's platform-level operational definition.
concept_edges[6]0.95the tagger's words
concept_edgesinteractions —operationalised-as→ engagement
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the relation itself is separately evidenced · strength of the relation: 1 · note: The study equates its interaction measure with engagement using i.e.
concept_edges[7]0.95the tagger's words
concept_edgesword embedding —operationalised-as→ word vector representation
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: The paper defines the embedding representation with an i.e. clause.
concept_edges[8]0.95the tagger's words
concept_edgesphenomenological models —contrasted-with→ mechanistic models
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differs in: reproducing empirical data without versus incorporating growth mechanisms · the relation itself is separately evidenced · strength of the relation: 1 · differs in (stated by the tagger, no quotation): reproducing empirical data without versus incorporating growth mechanisms · note: The methods section explicitly distinguishes the two model families.
concept_edges[9]0.95the tagger's words
concept_edgesWord embeddings —operationalised-as→ distributed representations of words
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: An i.e. clause defines word embeddings as distributed representations.
concept_edges[10]0.95the tagger's words
concept_edgescoefficient of relative amplification —operationalised-as→ E U /E R
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the relation itself is separately evidenced · strength of the relation: 1 · note: The coefficient is explicitly defined as the unreliable-to-reliable amplification ratio.
concept_edges[11]0.95the tagger's words
concept_edgesinteraction paradigm imposed by the specific social media —causes→ information spreading
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the relation itself is separately evidenced · strength of the relation: 0.7 · note: The analysis suggests that platform interaction paradigms drive spreading; weight reflects 'suggests'. · the relation stated: information spreading is driven by the interaction paradigm imposed by the specific social media or/and by the specific interaction patterns of groups of users engaged with the topic Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but 'Our analysis suggests' spreading is driven by the interaction paradigm; hedged.
concept_edges[12]0.95the tagger's words
content_tagsthe growth of questionable-source content is a re-scaled version of reliable-source content, and the re-scaling factor depends strongly on the platform
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.findings[0]0.95the tagger's words
content_tagsinformation from reliable and questionable sources does not present different spreading patterns
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.findings[1]0.95the tagger's words
content_tagson Gab, questionable posts are about 70% of the reliable volume while their reactions are about three times bigger
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content_tags.findings[2]0.95the tagger's words
content_tagsreliability is labelled at the level of the news outlet rather than the single article, which the authors name as the limit of the approach
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.findings[3]0.95the tagger's words
content_tagsthe definition of fake news may be inadequate because political debate resorts to labelling opposite news as unreliable
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.findings[4]0.95the tagger's words
content_tagsinformation spreading appears driven by the interaction paradigm imposed by the specific platform or by the interaction patterns of the users engaged
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.findings[5]0.95the tagger's words
content_tagsa news outlet systematically showing one or more of: extreme bias, consistent promotion of propaganda or conspiracies, poor or no sourcing, information not supported by evidence or unverifiable, a complete lack of transparency, or fake news
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.definitions[0]0.95the tagger's words
content_tagsthe average number of reactions to a post - a measure of the extent to which a post is amplified in a social media
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.definitions[1]0.95the tagger's words
content_tagsdays of data across five platforms, more than 8 million comments and posts
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[0]0.95the tagger's words
content_tagsR0
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: R0
content_tags.statistics[0]0.95the tagger's words
content_tagsplatform
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: platform
content_tags.variables[0]0.95the tagger's words
content_tagsamplification factor
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.variables[1]0.95the tagger's words

Interpretive choices

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What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.

© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824