Less than you think: Prevalence and predictors of fake news dissemination on Facebook — TEI-tagged view

Andrew M. Guess, Jonathan Nagler, Joshua A. Tucker · 2019 · political-science · doi:10.1126/sciadv.aau4586 · TEI xml · tagged edition · index · concepts · authors
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fake news3 hard news links age11 ideology News Feeds quasi-Poisson digital media literacy observational-study83
Method, as described: a three-wave YouGov panel linked by consent to Facebook timeline data, with posted domains cross-referenced against journalist- and academic-built lists of fake news publishers, modelled as counts by quasi-Poisson regression with sample-matching weights

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonthe dissemination of deliberately false political content by ordinary users - rare, concentrated, and predicted by age more robustly than by anything else measured— “More puzzling is the independent role of age: Holding constant ideology, party identification, or both, respondents in each age category were more likely to share fake news than respondents in the next-youngest group, and the gap in the rate of fake news sharing between those in our oldest category (over 65) and youngest category is large and…
claim-typeexistence— “So-called “fake news” has renewed concerns about the prevalence and effects of misinformation in political campaigns. Given the potential for widespread dissemination of this material, we examine the individual-level characteristics associated with sharing false articles during the 2016 U.S. presidential campaign. To do so, we uniquely link…
method — evidence— “We aggregated all shares to the individual respondent level so that our dependent variables are counts (i.e., number of fake news stories shared). To account for this feature of the data, as well as the highly skewed distribution of the counts, we primarily used Poisson or quasi-Poisson regressions to model the determinants of Facebook…
object of study — evidence— “Our approach allows us to provide a comprehensive observational portrait of the individual-level characteristics related to posting articles from fake news–spreading domains to friends on social media.”
research question — evidence— “We link a representative online survey (N = 3500) to behavioral data on respondents’ Facebook sharing history during the campaign, avoiding known biases in self-reports of online activity.”
contribution — evidence— “So-called “fake news” has renewed concerns about the prevalence and effects of misinformation in political campaigns. Given the potential for widespread dissemination of this material, we examine the individual-level characteristics associated with sharing false articles during the 2016 U.S. presidential campaign. To do so, we uniquely link…
transferfrom computational social science: digital trace data linked to survey response, carried from computational social science into a question political science had been answering with self-report— “We link a representative online survey (N = 3500) to behavioral data on respondents’ Facebook sharing history during the campaign, avoiding known biases in self-reports of online activity.”transfers[0]
transferfrom machine learning: a supervised text classifier from information retrieval, used not to make the finding but to draw the boundary of the outcome variable— “Our BuzzFeed-based list began with 30 domains identified by that site’s reporting as purveyors of intentionally false election-related stories generating the most Facebook engagement. To do this, the journalists, led by Craig Silverman, used keywords and existing lists combined with the analytics service BuzzSumo. To ensure that our analysis…transfers[1]
mechanisma category built by three procedures in sequence BuzzSumo ranks domains by Facebook engagement; a journalist checks each for the hallmark features of a fake news site - no contact page, syndicated content, recently registered; a supervised classifier trained on 7 million pages removes anything it calls hard news. Thirty domains become twenty-one, and every share in the dependent variable is a link to one of those twenty-one— “Our BuzzFeed-based list began with 30 domains identified by that site’s reporting as purveyors of intentionally false election-related stories generating the most Facebook engagement. To do this, the journalists, led by Craig Silverman, used keywords and existing lists combined with the analytics service BuzzSumo. To ensure that our analysis…mechanisms[0]
mechanismbehaviour without belief linking profile data to a survey removes the self-report bias in what people say they shared, and it cannot recover what they thought they were sharing. The design buys accuracy about the act and gives up access to the state of mind - which is the quantity the survey experiments measure and cannot observe the act of— “Figure 2D shows that, if anything, those who share the most content in general were less likely to share articles from fake news–spreading domains to their friends. Thus, it is not the case that what explains fake news sharing is simply that some respondents “will share anything.” These data are consistent with the hypothesis that people who…mechanisms[1]
mechanisma consent gradient inside the sample 3500 respondents, 1331 consenting to share profile data, 1191 successfully linked - and the linked respondents were more knowledgeable and more politically engaged than those who declined. The behavioural half of the design is available only from people willing to hand it over, and willingness correlates with the subject matter— “Of 3500 initial respondents in wave 1, 1331 (38%) agreed to share Facebook profile data with us. The proportion rises (49.1%) when we consider that only 2711 of our respondents said…mechanisms[2]
mechanismthe unobserved half of every social media finding sharing is a choice made over what arrived. Without the News Feed, a group that shares more fake news may be a group that saw more of it, and no amount of control for the sharer's characteristics separates the two. The authors state this rather than resolving it
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mechanisms[3]
critiques post-election narratives about the role of social media disinformation, by measuring how rare the behaviour actually was - over 90% of linked respondents shared none at all
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claim_relations[0]
critiques self-reported measures of online activity, which the linked design is built to avoid
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claim_relations[1]
critiques the discipline's habit of treating demographic traits as controls to be conditioned away rather than as explanatory factors in their own right
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claim_relations[2]
qualifies its own ideological finding - sensitive to coding, based on few respondents, and confounded with exposure the design cannot observe
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claim_relations[3]
sensefake news sharing - AS MEASURED HERE: a post to one's own timeline whose link resolves to one of 21 domains, from profile data, with no measure of whether the sharer believed it
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senses[0]
sensefake news - AS DEFINED: knowingly false or misleading content created largely to generate ad revenue, with the authors stating there is no commonly accepted ground-truth standard for the category
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senses[1]
sensethe domain list - AS BUILT BY BUZZFEED: 30 domains ranked by Facebook engagement and checked by a journalist for the hallmark features of a fake news site
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senses[2]
sensethe domain list - AFTER THE HARD-NEWS FILTER: 21 domains, with hyperpartisan sites such as Breitbart removed by a supervised classifier so that the contested category is excluded
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senses[3]
sensethe conservative effect - AS AN OBSERVATION: conservatives shared the most, and the content was overwhelmingly pro-Trump
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senses[4]
sensethe conservative effect - AS AN INFERENCE: possibly not a greater tendency to share fake news at all, but the ordinary tendency to share what one agrees with, conditional on exposure the study cannot see
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senses[5]
controlthe total number of links shared of any kind reported beside the fake news count, and included as a covariate in every model — held
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— rules out a low fake-news count meaning people do not share links - most respondents shared hundreds, so the rarity is about the content
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controls[0]
controlthe relationship between overall sharing volume and fake news sharing examined directly, rather than only controlled for — held
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— rules out the explanation that some respondents will share anything - the heaviest sharers shared LESS fake news, which is the opposite of that prediction
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controls[1]
controlthe same model applied to hard news sharing as a placebo outcome — held
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— rules out age predicting online sharing in general - if it did, the predictors of hard news would look the same, and they do not include age at all
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controls[2]
controlfive different lists of fake news domains, including one built from 948 individual fact-checks and one crowdsourced, with the analysis rerun on each — held
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— rules out one journalist's list carrying the finding - the category has no ground truth, so the robustness has to be to the choice of vocabulary itself
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controls[3]
controlan exact-URL variant of the analysis using only the 948 fact-checked stories, rather than counting every article from a listed domain — held
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— rules out the assumption that everything published by a fake news domain is fake - the stricter definition does not need it
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controls[4]
controlhard news domains removed from the list by a classifier trained on 7 million shared pages, so that hyperpartisan sites are excluded from the outcome — held
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— rules out the contested hyperpartisan category being counted as fake news, which would make an ideological finding partly definitional
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controls[5]
controlnegative binomial and OLS models run alongside the quasi-Poisson, with the quasi-Poisson chosen by a dispersion test — held
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— rules out the count model's distributional assumptions producing the result
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controls[6]
controlideology and party identification, correlated at r = 0.31, entered separately and together, with all three specifications reported — held
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— rules out a collinearity decision being made silently in the authors' favour - they name the correlation and publish every version
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controls[7]
controlthe models re-fitted as negative binomial and OLS alongside the Poisson and quasi-Poisson specifications, with dispersion tests deciding between the latter two — held — rules out that the sharing result is an artefact of one count-model specification
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controls[8]
controlrespondents asked to check which categories of profile information they were willing to share, with no access to News Feeds or to friends' data — held — rules out collecting more than was consented to - the field-by-field opt-in bounds the data at the point of collection
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controls[9]
weightpost-election narratives about the role of social media disinformation, by measuring how rare the behaviour actually was - over 90% of linked respondents shared none at all — 0.95
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claim_relations[0]
weightself-reported measures of online activity, which the linked design is built to avoid — 0.95
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claim_relations[1]
weightthe discipline's habit of treating demographic traits as controls to be conditioned away rather than as explanatory factors in their own right — 0.9
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claim_relations[2]
weightits own ideological finding - sensitive to coding, based on few respondents, and confounded with exposure the design cannot observe — 0.95
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claim_relations[3]
differs in (stated by the tagger, no quotation)fake news —distinct-from→ hard news — whether the site is intentionally or systematically factually inaccurate, or merely partisanconcept_edges[0]
relationfake news —distinct-from→ hard news — domains a supervised classifier calls hard news are removed, so that the contested hyperpartisan category is not counted as fake news
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concept_edges[0]
weightfake news —distinct-from→ hard news — 0.95
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concept_edges[0]
auditage —distinct-from→ ideology — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but age effects hold with ideology held constant; distinct-from is loose.
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concept_edges[2]
differs in (stated by the tagger, no quotation)age —distinct-from→ ideology — whether the predictor survives conditioning on the otherconcept_edges[2]
relationage —distinct-from→ ideology — holding ideology, party or both constant, each age group still shared more than the next-youngest
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concept_edges[2]
weightage —distinct-from→ ideology — 0.95
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concept_edges[2]
outcomecontrol — held
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controls[0]
outcomecontrol — held
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controls[1]
outcomecontrol — held
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controls[2]
outcomecontrol — held
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controls[3]
outcomecontrol — held
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controls[4]
outcomecontrol — held
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controls[5]
outcomecontrol — held
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controls[6]
outcomecontrol — held
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controls[7]
outcome (stated by the tagger, no quotation)control — held
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controls[8]
outcome (stated by the tagger, no quotation)control — held
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controls[9]
limitationthere is no measure of whether respondents knew that what they shared was false
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limitations[0]
limitationthe data are observational and cannot provide causal evidence, and an omitted variable cannot be definitively ruled out
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limitations[1]
limitationhalf the respondents with Facebook accounts declined to share their profile data, and the linked ones were more knowledgeable and politically engaged
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limitations[2]
limitationthe composition of respondents' News Feeds is unobserved, so exposure cannot be separated from willingness to share
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limitations[3]
limitationposts deleted before consent are invisible, and the authors argue rather than measure that this should reduce noise rather than bias
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limitations[4]
limitationthe conservative finding is sensitive to coding and rests on a small number of respondents
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limitations[5]
limitationthe ideological pattern may reflect the pro-Trump slant of the available content and the ordinary tendency to share what one agrees with, rather than a greater propensity to share falsehood
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limitations[6]
limitationthere is no commonly accepted ground-truth standard for what counts as fake news, so the dependent variable rests on lists built by journalists and academics
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limitations[7]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
finding
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content_tags.findings[0]
finding
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content_tags.findings[1]
finding
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content_tags.findings[2]
finding
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content_tags.findings[3]
finding
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content_tags.findings[4]
finding
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content_tags.findings[5]
finding
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content_tags.findings[6]
finding
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content_tags.findings[7]
quantityof linked respondents shared no articles from fake news domains at all - the number the title refers to
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content_tags.quantities[0]
quantityweb domains constituting the entire dependent variable, after a journalist's list of 30 was filtered by a hard-news classifier
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content_tags.quantities[1]
value source notequantity — THE DESCRIPTION DRAWS ON THE PAPER BEYOND THIS QUOTE: the starting list of 30 is described in the appendix ('Our BuzzFeed-based list began with 30 domains').content_tags.quantities[1]
quantityof the 3500 survey respondents agreed to share Facebook profile data; 1191 were successfully linked
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content_tags.quantities[2]
quantitythe correlation between ideological self-placement and party identification, disclosed and handled by reporting all three specifications
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content_tags.quantities[3]
variableage
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content_tags.variables[0]
variablefake news
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content_tags.variables[1]
definitionfake news
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content_tags.definitions[0]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptfake news — not defined in this paper's own words; content deliberately dressed as news for ad revenue - a term the authors say covers a variety of phenomena and has no commonly accepted ground-truth standard, operationalised here as 21 domains
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concepts[0]
concepthard news — not defined in this paper's own words; the category a supervised classifier removes from the list, so that hyperpartisan sites like Breitbart are not counted as fake news - the boundary is drawn by a model, and the paper says which
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concepts[1]
conceptlinks — not defined in this paper's own words; the denominator that makes the rarity meaningful - most respondents shared hundreds of links and almost none of them from fake news domains
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concepts[2]
conceptage — not defined in this paper's own words; the paper's most robust predictor, surviving controls for ideology, party, education and posting volume, and absent from the same model applied to hard news sharing
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concepts[3]
conceptideology — not defined in this paper's own words; the predictor political science expects to carry the effect; present here, correlated with party at r = 0.31, and unable to absorb the age term
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concepts[4]
conceptNews Feeds — not defined in this paper's own words; what reached each respondent - unobserved in this design, and therefore the standing alternative explanation for every group difference in sharing
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concepts[5]
conceptquasi-Poisson — not defined in this paper's own words; the count model used because the dependent variable is a heavily skewed count of shares, chosen by a dispersion test rather than by default
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concepts[6]
conceptdigital media literacy — not defined in this paper's own words; one of two proposed explanations for the age effect, and one the authors say needs behaviourally validated measures before it can be tested
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concepts[7]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
quasi-Poisson
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content_tags.statistics[0]
CI
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
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When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Tag layer © the Meta-Tagging Project, CC BY 4.0. The paper text is © its authors/publisher and is linked, not reproduced. Excerpts shown are short quotations for identification and verification. · back to index · concepts · authors · עברית
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