The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; none of the 25 sources its claims cite is checked for support yet.
Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of “Fake News”?
Open accessAbstract
There is considerable concern about the propagation of disinformation through social media, particularly for political purposes. “Organic reach” has been found to be important in the propagation of disinformation on social networks. This is the phenomenon whereby social media users extend the audience for a piece of information: interacting with it, or sharing it with their wider networks, greatly increases the number of people the information reaches. This project evaluated the extent to which characteristics of the message source (how trustworthy they were) and the recipient (risk propensity and personality) influenced the organic reach of a potentially false message. In an online study, 357 Facebook users completed personality and risk propensity scales and rated their likelihood of interacting in various ways with a message posted by either a trustworthy or untrustworthy source. Message source impacted on overall organic reach, with messages from trusted sources being more likely to be propagated. Risk propensity did not influence reach. However, low scores on trait agreeableness predicted greater likelihood of interacting with a message. The findings provide preliminary evidence that both message source and recipient characteristics can potentially influence the spread of disinformation.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In an online scenario study, Facebook users were more likely to say they would share, like, comment on, or react to a potentially fake news post when it came from a trusted close friend rather than an unknown contact. Risk propensity did not predict organic reach, while lower Agreeableness was associated with greater likelihood of propagating the post.
Key findings
- The trust manipulation was effective: participants rated themselves more likely to trust information in the high-trust condition (M = 2.47, SD = 1.09) than in the low-trust condition (M = 1.69, SD = .92), t(349.65) = −7.34, p < .0005.
- In multiple regression, trust condition significantly predicted organic reach (B = 1.55, SE = 0.36, β = .22, t = 4.31, p = .000), with higher reach for the higher-trust source.
- Agreeableness had a significant negative effect on organic reach (B = −0.12, SE = 0.05, β = −.15, t = −2.60, p = .01): less agreeable people were more likely to extend the message’s reach.
- Risk propensity was not significantly associated with organic reach (B = 0.00, SE = 0.02, β = .01, t = 0.20, p = .843), and the other four personality traits were non-significant; the model explained R² = .081, F = 4.33, p = .000.
Methods
- Online study conducted via www.personalitytest.org.uk and Qualtrics; final sample was 409 participants, of whom 357 were Facebook users, with respondents from 44 countries and a majority relatively young female US students.
- Measures included a 41-item Big Five personality questionnaire, Meertens and Lion’s 7-item Risk Propensity Scale, and an experimental trust manipulation: high-trust condition described a close friend’s Facebook post about political corruption, while low-trust condition described the same post from someone who had recently sent a friend request but was not really known.
- Organic reach was measured as a summed scale of four self-rated likelihood items—share, like, comment, and react—with acceptable reliability (α = .80); analysis used a manipulation-check t-test and multiple regression predicting organic reach from trust condition, risk propensity, and the five personality variables.
Limitations
- The paper states that only a single exemplar of a potential fake news item was used, so the Agreeableness effect may be specific to that item and requires replication with other materials; the election-campaign scenario also limits generalizability because trust is context dependent.
- The paper states that the trust manipulation confounded trustworthiness with closeness of the relationship, that the truthfulness of the story was never made clear to participants, and that the study relied entirely on self-reported likelihood of interaction rather than actual behavioral observations.
- The paper states that the measured variables accounted for only a small proportion of variance in self-rated likelihood of propagating disinformation (R² = .081), with unmeasured factors such as digital literacy, political interest, identity consistency, and general social media behavior likely playing a role; the convenience sample’s skew toward young female US students is also evident from the reported demographics.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
21 Not checked yet · 11 Own method · 10 Own result · 9 Own interpretation
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The analysis, summarized in Table 4, indicated there was a statistically significant effect of trust manipulation on organic reach, with a higher level of reach for posts in the higher trust condition.”
- Also stated in: § Discussion · plain
“As hypothesized, trustworthiness of the source of a message was associated with its potential organic reach.”
- Also stated in: § Discussion · hedged
“People rated themselves as more likely to extend the reach of a message coming from a trustworthy friend by sharing, liking, and so on.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: All four Facebook actions (share, like, comment, react) contribute to the organic reach of a posting.
- Also stated in: § Discussion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As predicted, participants reported a higher likelihood of trusting the information in the high-trust condition (M = 2.47, SD = 1.09) than in the low-trust condition (M = 1.69,SD = .92).This difference was statistically significant (t (349.65)= -7.34,p < .0005,using adjusted degrees of freedom due to a significant Levene's test for equality of variance across conditions).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Beyond this, only Agreeableness had a statistically significant effect on organic reach.Agreeableness was negatively associated with reach: less agreeable people were more likely to increase the message's reach.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: All four Facebook actions (share, like, comment, react) contribute to the organic reach of a posting.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the five dimensions included, four had no effect on the measure of potential organic reach”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The hypothesized effect of risk propensity on organic reach of fake news was not found.”
- Also stated in: § Conclusion · plain
“Risk propensity | 0.00 | 0.02 | .01 | 0.20 | .843”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Conclusion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that material coming from a trusted source is more likely to be interacted with”
- Also stated in: § Conclusion · plain
“Trust condition | 1.55 | 0.36 | .22 | 4.31** | .000”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Implications for Policy and Practice
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Conclusion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The only personality variable found to affect the potential reach of a Facebook posting was Agreeableness.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, the variables measured here accounted for only a small proportion of variance in the self-rated likelihood of propagating disinformation.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Limitations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Among those participants who used Facebook, the four items indexing organic reach (share, like, comment, and react) intercorrelated sufficiently to form a scale with acceptable reliability.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the effect may well have practical significance: disinformation is more likely to be propagated on Facebook if it reaches one from a trusted, rather than untrusted, source.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 14
- Probably not a substantial claim. 22
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Beyond this, only Agreeableness had a statistically significant effect on organic reach.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: All four Facebook actions (share, like, comment, react) contribute to the organic reach of a posting.
In the paper
“At β = .22,the effect size exceeded the .2threshold proposed for an effect that would have realworld significance (Ferguson, 2009).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
What the sources say
- Not checked yetcites Ferguson, 2009
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More agreeable people gave lower organic reach scores, and so were less likely to contribute to the propagation of a fake news item.”
- Also stated in: § Conclusion · plain
“Agreeableness | -0.12 | 0.05 | -.15 | -2.60* | .01”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Conclusion · plain
None of the sources cited for this claim have been checked yet.
In the paper
“This has been validated for use on the internet (Buchanan et al., 2005) and correlates well with the dimensions of Costa and McCrae's (1992) model.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Materials
What the sources say
- Not checked yetcites Buchanan et al., 2005
- Not checked yetcites Costa and McCrae's (1992)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This indicates that the trust manipulation was effective, and the two conditions differed in the extent to which the information source described was considered trustworthy by participants.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Results
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Fogel and Nehmad (2009) showed that individuals who used social media had higher levels of risk acceptance than those who did not.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Risk Propensity and Cyber-Victimization
What the sources say
- Not checked yetcites Fogel and Nehmad (2009)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Qualtrics' proprietary technology was used to prevent multiple completions, and the dataset was screened for unrealistic combinations of demographic data.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Data Screening and Processing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Hajli and Lin (2016) found that social network users' perceptions of privacy risk influenced their attitude toward sharing information online.People who judged information sharing as being more risky had less positive attitudes toward it.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Risk Propensity and Cyber-Victimization
What the sources say
- Not checked yetcites Hajli and Lin (2016)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“There are indications that social media users with high levels of risk propensity are more likely to become victims of cybercrime (Saridakis et al., 2016;Whittle et al., 2013).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Risk Propensity and Cyber-Victimization
What the sources say
- Not checked yetcites Saridakis et al., 2016
- Not checked yetcites Whittle et al., 2013
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This is consistent with Williams et al.'s (2017) proposal that trust in a message source increases our vulnerability to online influence, for example, in the context of internet scams.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
What the sources say
- Not checked yetcites Williams et al.'s (2017)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“There is evidence that an individual's status on all of these dimensions can be inferred from their social media footprint (e.g., Azucar et al., 2018;Hinds & Joinson, 2019).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Personality Traits
What the sources say
- Not checked yetcites Azucar et al., 2018
- Not checked yetcites Hinds & Joinson, 2019
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In the context of sharing news information online, there is evidence that individuals are more likely to trust, and engage with, a story when it is shared by someone they have a higher level of trust in (Sterret et al., 2018).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Trust
What the sources say
- Not checked yetcites Sterret et al., 2018
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“It also chimes with Sterret et al.'s (2018) finding that trust in the source of information influences the extent to which we interact with it.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
What the sources say
- Not checked yetcites Sterret et al.'s (2018)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Extant research indicates that trust and risk propensity are among the factors that impact cyber-victimization of individuals (Saridakis et al., 2016;Williams et al., 2017).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Disinformation as Cybercrime
What the sources say
- Not checked yetcites Saridakis et al., 2016
- Not checked yetcites Williams et al., 2017
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It has acceptable reliability, and has been used successfully in an online format (Branley & Covey, 2017).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Materials
What the sources say
- Not checked yetcites Branley & Covey, 2017
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Of those who did, half were then randomly assigned to see the high-trust version of the scenario, and the other half saw the low-trust version.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Procedure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Generalizability is also limited by the nature of the trust manipulation and the scenario used (an election campaign).”
- Also stated in: § Limitations · plain
“The single scenario used (an election campaign) also limits generalizability.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Trust is context dependent, and who one trusts may differ across different scenarios.
- Also stated in: § Limitations · plain
In the paper
“For example, Extraversion is related to the number of Facebook friends a user has, Agreeableness to the type of affect (positive or negative) expressed in status updates, Conscientiousness to the presence of political material in status updates, Neuroticism to frequency of posting, and Openness to Experience is related to the sharing of various forms of media (Hall et al., 2013).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Personality Traits
What the sources say
- Not checked yetcites Hall et al., 2013
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“There is evidence that this would be feasible, as Agreeableness is known to be detectible from social media footprints (Azucar et al., 2018).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
What the sources say
- Not checked yetcites Azucar et al., 2018
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“People are known to vary in the extent to which they are willing to accept risk in everyday life (Meertens & Lion, 2008).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Risk Propensity and Cyber-Victimization
What the sources say
- Not checked yetcites Meertens & Lion, 2008
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As an example, work by Matz et al. (2017) has shown that personality-targeted advertising on social media can indeed influence user behavior.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
What the sources say
- Not checked yetcites Matz et al. (2017)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In terms of informationsharing behavior, Openness to Experience is positively associated, and Neuroticism negatively associated, with users' self-reports of the breadth of their self-disclosure on Facebook.In addition, Extraversion is positively associated with depth of self-disclosure (Hollenbaugh & Ferris, 2014).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Personality Traits
What the sources say
- Not checked yetcites Hollenbaugh & Ferris, 2014
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The current dataset does not allow us to say whether this effect would apply to all fake news items, or is particular to the item described here.”
- Also stated in: § Limitations · hedged
“It remains to be seen whether Agreeableness would also influence the organic reach of other types of material.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § Limitations · hedged
In the paper
“It has been shown that trust in virtual communities and teams increases knowledge sharing and information flow (Watson-Manheim & Bélanger, 2007).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Trust
What the sources say
- Not checked yetcites Watson-Manheim & Bélanger, 2007
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It is pertinent to note that in 2018, Twitter identified fraudulent accounts set up by a group linked to disinformation, which simulated those of US local newspapers (Mak & Berry, 2018).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Implications for Policy and Practice
What the sources say
- Not checked yetcites Mak & Berry, 2018
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the effect size is low, when operating at the massive scale made possible by social media, the effect has potential real-world significance.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The beliefs and motivations of the estimated 10% of Facebook users who do spread such information (Guess et al., 2019) require examination.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Limitations
What the sources say
- Not checked yetcites Guess et al., 2019
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It is worth noting a Pew Research Center survey that found believing a story was false was not necessarily a barrier to sharing it (Barthel et al., 2016).”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Limitations
What the sources say
- Not checked yetcites Barthel et al., 2016
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The present findings point to potential strategies for spreading disinformation.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Implications for Policy and Practice
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These appear to have been sleeper accounts established specifically for the purpose of building trust, just as envisioned in this scenario.”
§ Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of "Fake News"? > Implications for Policy and Practice
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The authors state this as an assumption.
Users who encounter disinformation online and spread it further are victims, duped by the message originators into spreading lies.
“Those who encounter the material online and spread it further are essentially victims, duped by the message originators into spreading lies.”
The paper relies on this without support.
Disinformation is, by its very nature, designed to provoke controversy.
“Disinformation is, by its very nature, designed to provoke controversy.”
The paper relies on this without support.
All four Facebook actions (share, like, comment, react) contribute to the organic reach of a posting.
“While Facebook's algorithms assign different weights to each of these user behaviors in determining whether a user's audience sees the post they are responding to, it is clear that all contribute to the organic reach of a posting.”
The paper relies on this without support.
Trust is context dependent, and who one trusts may differ across different scenarios.
“Trust is very much context dependent, and who one trusts may differ across different scenarios.”
The paper relies on this without support.
In real life, individuals often have no way of knowing for sure whether information they come across is true.
“In fact, this would normally be the situation in real life: individuals often have no way of knowing for sure whether information they come across is true.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“All the studies in our corpus suggest that trust in the information on social media [10,56,65,68] and trust in the platforms themselves [30,59,66,70] positively infuence people's intention to share information, news, and opinions on social media [122].”
From What Do We Mean When We Talk about Trust in Social Media? A Systematic Review (2023) · cites this paper as [10]
Experimental — accuracy not yet measured
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentt(349.65) = -7.34reported p < .0005 · recomputed p < .001
“t (349.65)= -7.34,p < .0005”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Self-check results appear here once an independent check has confirmed them.
Citations
Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1×
“In fact, analysis by Vosoughi et al. (2018) indicated that false material spread on Twitter “farther, faster, deeper, and more broadly than the truth” and that this was primarily due to human behavior rather than the operation of bots.”
BackgroundTrust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
An effect size primer: A guide for clinicians and researchers.
Ferguson, C. J. (2009) · Professional Psychology: Research and Practice · doi:10.1037/a0015808 · Cited by 3,467
Less than you think: Prevalence and predictors of fake news dissemination on Facebook
Guess, A., Nagler, J., & Tucker, J. (2019) · Science Advances · doi:10.1126/sciadv.aau4586 · Cited by 1,652
Internet social network communities: Risk taking, trust, and privacy concerns
Fogel, J., & Nehmad, E. (2009) · Computers in Human Behavior · doi:10.1016/j.chb.2008.08.006 · Cited by 987
Psychological targeting as an effective approach to digital mass persuasion
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710966114 · Cited by 892
A survey of trust in social networks
Sherchan, W., Nepal, ., & Paris, C. L. (2013) · ACM Computing Surveys · doi:10.1145/2501654.2501661 · Cited by 687
Advanced social engineering attacks
Krombholz, K., Hobel, H., Huber, M., & Weippl, E. (2015) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2014.09.005 · Cited by 575 · cited 1×
“Krombholz et al. (2015) describe ways in which the information people post to social media can be exploited in social engineering attacks, such as targeted “social phishing.””
BackgroundPredicting the Big 5 personality traits from digital footprints on social media: A meta-analysis
Azucar, D., Marengo, D., & Settanni, M. (2017) · Personality and Individual Differences · doi:10.1016/j.paid.2017.12.018 · Cited by 572
Like, comment, and share on Facebook: How each behavior differs from the other
Kim, C., & Yang, S.-U. (2017) · Public Relations Review · doi:10.1016/j.pubrev.2017.02.006 · Cited by 550
Research works printed but not linked: 1
- Costa P. T., 1992, Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO FFI): Professional manual
Cited by
Sorted by citations
Items 101–153 of 153
Sargın, S., Uyar, K., & Özyirmidokuz, E. K. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1040099 · Cited by 2
MENTORING AS A MODERN METHOD OF BUILDING THE PEDAGOGICAL PROCESS
Akhmirova, E. R., & Telitsyna, A. Y. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-5-9 · Cited by 2
Zala, D. R., Karthick, A., V, & Sreekumar, A. (2025) · Health Marketing Quarterly · doi:10.1080/07359683.2025.2504813 · Cited by 2
Desinformationen und Messengerdienste: Herausforderung und Lösungsansätze
Krämer, Hornung, Jansen, Kluck, Rinsdorf, Setz, Steinebach, Vogel, & Yannikos (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748913344-317 · Cited by 2
Procesamiento cognitivo de fake news políticas. Revisión de estudios experimentales
García, A. P. P., Danieli, N. E., & Freire, I. E. M. (2023) · Dixit · doi:10.22235/d.v37i1.3112 · Cited by 2
Exploitation of Human Trust, Curiosity and Ignorance by MalwarePreprint
Krishnan, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11805 · Cited by 2
Han, Y., Modaresnezhad, M., Dissanayake, I., Mehta, N., & Nemati, H. (2023) · Discourse & Communication · doi:10.1177/17504813231207948 · Cited by 1
Wang, M.-H., Sheen, G. C.-H., Chen, P.-Y., & Ho, E. (2025) · Personality and Individual Differences · doi:10.1016/j.paid.2025.113241 · Cited by 1
Improving mobilisation of anti-slavery activism through Facebook communications
Newton, J., & O’Brien, E. (2022) · Crime and justice briefing papers. · doi:10.5204/book.eprints.239340 · Cited by 1
Network Analysis of Disinformation Actors During COVID-19 Pandemics and Beyond
Burda, R., & Procházka, J. (2024) · Obrana a strategie (Defence & Strategy) · doi:10.3849/1802-7199.24.2024.01.019-039 · Cited by 1
Novel machine learning technique for analyzing personalities in status updates on facebook
Khajanchi, Y., John, M., Wadhwa, D., & Gupta, R. (2024) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2024ss0502 · Cited by 1
Competing for Trust: How Initiative and Repetition of Exposure Influence Information Credibility
Ma, D. C., Cürebal, F., Ardhi, D. C., & Sari, D. P. (2024) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1170 · Cited by 2
Yang, G. H., Zhao, L., Liang, Y., & Wang, Z. (2025) · Journalism Practice · doi:10.1080/17512786.2025.2464192 · Cited by 1
Mavyan, R., Esmail, A., & Mavyan, H. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7468-9.ch002 · Cited by 1
Fighting Modern Slavery on Facebook: Clicktivism to Offline Participation
Newton, J. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.233174 · Cited by 1
How to reduce the spread of fake news – by doing nothing
Buchanan, T. (2020) · doi:10.64628/ab.cs6dwfg6m · Cited by 1
Self-citationالأخبار المزیفة على وسائل التواصل الاجتماعی وکفاءتها کمصادر للمعلومات عن جائحة کورونا
سلامة, ح. ع. (2020) · Al-Maǧallah Al-Miṣriyyaẗ Li-Buḥūṯ Al-Rāʼī Al-ʿām · doi:10.21608/joa.2020.144433
Farrand, B. M., & Carrapico, H. F. (2021) · Springer eBooks · doi:10.1007/978-3-030-74837-1_2
Analysis on the Effects of Misinformation: Taking Facebook as an Example
Liu, Y. (2023) · Lecture Notes in Education Psychology and Public Media · doi:10.54254/2753-7048/3/2022465
Blackout and fakes of russian propaganda: a criminology view of the problem of symbiosis
Batyrgareieva, V. S. (2023) · INFORMATION AND LAW · doi:10.37750/2616-6798.2023.2(45).282334
Surfing the Hoaxes Wave Fabrication During the Pandemic Period in Indonesia
Haryono, C. G., Kedaton, K. R. S., & Karsten, H. E. (2023) · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · doi:10.2991/978-2-38476-098-5_37
FEATURES OF CHILD-PARENT ATTACHMENT IN PERSONS WITH MANIFESTED ADDICTION
Kartashev, I. V., & Danilova, A. A. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475//2409-4102-2023-24-4-67-72
Andreev, A. S. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475//2409-4102-2023-24-4-16-20
PSYCHOLOGICAL ASPECTS OF THE EDUCATIONAL PROCESS OF STUDENTS AS A PREVENTION OF DESTRUCTIVE BEHAVIOR
Kuznetsova, M. N., Rychkova, L. S., & Ivanovsky, E. V. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-31-35
PSYCHOLOGICAL CHARACTERISTICS OF ADOLESCENTS WITH ILLEGAL BEHAVIOR
Artemyeva, Z., & Artemyeva, V. A. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-25-30
SOME ASPECTS OF TRAINING IN THE INTERACTION OF POLICE OFFICERS WHEN USING PHYSICAL FORCE
Kostyukov, A. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-21-24
EXISTENTIAL VIEW ON DEPRESSION
Abrosimova, E. A. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-36-45
STRUCTURAL MODEL OF INFLUENCE OF PERSONAL DETERMINANTS ON PROFESSIONAL HEALTH
Kornienko, A. V., & Starchenkova, E. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-73-80
INTERRELATION OF TEMPORAL FOCUS, SUBJECTIVE AGE AND SUBJECTIVE WELL-BEING OF PEOPLE IN RETIREMENT
Сергеевна, В. О., & Забелина, Е. В. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475//2409-4102-2023-24-4-56-66
AXIOLOGICAL APPROACH TO THE MORAL TRADITIONS DEPARTMENT OF THE INTERIOR MINISTRY
Mazur, М. A. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-10-15
Kochkorbaeva, A. E., Денисова, Е. Г., & Abakumova, I. (2023) · Вестник Челябинского государственного университета Образование и здравоохранение · doi:10.47475/2409-4102-2023-24-4-81-89
Koinig, I., Woschnagg, F., Bausch, A., & Scheiber, R. (2024) · Springer eBooks · doi:10.1007/978-3-658-45040-3_9
PersAD: adversarial protection against text-based personality inference
Qiu, H., Ma, X., Liu, B., Cai, Y., Peng, B., & Huang, D. F. (2025) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-025-01144-0
Song, S. (2025) · Elaris Computing Nexus · doi:10.65148/ecn/2025005
Disrupted Voter Loyalty as a Political Marketing Challenge
Erdem, E., & Nasır, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4089-0.ch004
Shin, J., Wang, H., Carley, L. R., & Carley, K. M. (2025) · Winter Simulation Conference · doi:10.1109/wsc68292.2025.11338920
A Critical Look at Ethics of Facebook on Litigation & Misinformation
Narasimhan, V. L., Kenna, C. A., Sharma, N. K., & Bharath, B. M. (2025) · International Conference on Communication, Computing and Industry 4.0 (C2I4) · doi:10.1109/c2i666499.2025.11366958
“#LetsInternetBetter” or “Less Internet Better”: Consumer Responses to Google’s #LetsInternetBetter
Aydoğan, H., & Hoştut, S. (2026) · Trakya Üniversitesi sosyal bilimler dergisi/Trakya Üniversitesi Sosyal Bilimler dergisi · doi:10.26468/trakyasobed.1688501
Hu, Y., Yu, G., & Guo, Y. (2026) · Current Psychology · doi:10.1007/s12144-026-09503-w
Malviya, S., Goh, Z. H., Li, B. J., & Tandoc, E. C. (2026) · Journal of Cyber Policy · doi:10.1080/23738871.2026.2673009
Disinformation in Science: A Review of Empirical Evidence on Causes, Diffusion, and Countermeasures
Lysek, J., Šaradín, P., Zapletalová, M., Lebeda, T., Lebedová, E., Ostrá, D., & MOLNÁR, O. (2026) · Czech Sociological Review · doi:10.13060/csr.2026.021
Ratnesh, M., Vardhan, J., Mishra, Y., & Choudhary, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6985153
Gong, Y. (2026) · Queensland University of Technology · doi:10.5204/thesis.eprints.266363
Strategies and Solutions to Combat Disinformation
KARANFİLOĞLU, M., & Kulaklıkaya, K. (2026) · Springer eBooks · doi:10.1007/978-981-95-8553-3_4
KARANFİLOĞLU, M., & Kulaklıkaya, K. (2026) · Springer eBooks · doi:10.1007/978-981-95-8553-3_2
Integrative Strategies and Future Directions
KARANFİLOĞLU, M., & Kulaklıkaya, K. (2026) · Springer eBooks · doi:10.1007/978-981-95-8553-3_5
Kot, K. (2026) · Roczniki Nauk Społecznych · doi:10.18290/rns2026.0042
Information Overload and Mental Health: A Systematic Examination of Digital-Era StressorsPreprint
Anber, R., Mahmud, S., Rahman, T., & Aminnah, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7636479
Giraldo, D. C., González, A. D., & Barker, K. (2026) · Risk Analysis · doi:10.1111/risa.70345
Metadata
- Authors
- Buchanan, T., & Benson, V.
- Affiliations
- University of Westminster (GB)
- Aston University (GB)
- Corresponding author
- Buchanan, Tom
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2019-10
- Volume
- 5
- Issue
- 4
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W2983472018
- Semantic Scholar
- 12c371b58a7a5083d9451ceac712078d92dd927a
- Cited by
- 151
- References
- 28
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 22 · 2023: 40 · 2022: 27 · 2021: 19 · 2020: 12 · 2019: 1
- FWCI
- 29.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Social Media + Society (publishedVersion, cc-by-nc)
- WestminsterResearch (University of Westminster) (acceptedVersion, cc-by-nc)
- Aston Publications Explorer (Aston University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Keywords
- disinformation, fake news, organic reach, Facebook, risk propensity, personality, agreeableness, information sharing, political disinformation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
