Computational Fact Checking from Knowledge Networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Ciampaglia GL
PROVIDER: S-EPMC4471100 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Today's media landscape affords people access to richer information than ever before, with many individuals opting to consume content through social channels rather than traditional news sources. Although people frequent social platforms for a variety of reasons, we understand little about the consequences of encountering new information in these contexts, particularly with respect to how content is scrutinized. This research tests how perceiving the presence of others (as on social media platforms) affects the way that individuals evaluate information-in particular, the extent to which they verify ambiguous claims. Eight experiments using incentivized real effort tasks found that people are less likely to fact-check statements when they feel that they are evaluating them in the presence of others compared with when they are evaluating them alone. Inducing vigilance immediately before evaluation increased fact-checking under social settings.
| S-EPMC5468680 | biostudies-literature
Project description:[Figure: see text].
| S-EPMC8442902 | biostudies-literature
Project description:BackgroundEffective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy.ObjectiveThis work investigates (1) the changes in the public's attitude toward COVID-19 vaccines over time, (2) the effectiveness of COVID-19 vaccine fact-checking information on social media engagement and attitude change, and (3) the emotional and linguistic features of the COVID-19 vaccine fact-checking information ecosystem.MethodsWe collected a data set of 12,553 COVID-19 vaccine fact-checking Facebook posts and their associated comments (N=122,362) from January 2020 to March 2022 and conducted a series of natural language processing and statistical analyses to investigate trends in public attitude toward the vaccine in COVID-19 vaccine fact-checking posts and comments, and emotional and linguistic features of the COVID-19 fact-checking information ecosystem.ResultsThe percentage of fact-checking posts relative to all COVID-19 vaccine posts peaked in May 2020 and then steadily decreased as the pandemic progressed (r=-0.92, df=21, t=-10.94, 95% CI -0.97 to -0.82, P<.001). The salience of COVID-19 vaccine entities was significantly lower in comments (mean 0.03, SD 0.03, t=39.28, P<.001) than in posts (mean 0.09, SD 0.11). Third-party fact checkers have been playing a more important role in more fact-checking over time (r=0.63, df=25, t=4.06, 95% CI 0.33-0.82, P<.001). COVID-19 vaccine fact-checking posts continued to be more analytical (r=0.81, df=25, t=6.88, 95% CI 0.62-0.91, P<.001) and more confident (r=0.59, df=25, t=3.68, 95% CI 0.27-0.79, P=.001) over time. Although comments did not exhibit a significant increase in confidence over time, tentativeness in comments significantly decreased (r=-0.62, df=25, t=-3.94, 95% CI -0.81 to -0.31, P=.001). In addition, although hospitals receive less engagement than other information sources, the comments expressed more positive attitudinal valence in comments compared to other information sources (b=0.06, 95% CI 0.00-0.12, t=2.03, P=.04).ConclusionsThe percentage of fact-checking posts relative to all posts about the vaccine steadily decreased after May 2020. As the pandemic progressed, third-party fact checkers played a larger role in posting fact-checking COVID-19 vaccine posts. COVID-19 vaccine fact-checking posts continued to be more analytical and more confident over time, reflecting increased confidence in posts. Similarly, tentativeness in comments decreased; this likewise suggests that public uncertainty diminished over time. COVID-19 fact-checking vaccine posts from hospitals yielded more positive attitudes toward vaccination than other information sources. At the same time, hospitals received less engagement than other information sources. This suggests that hospitals should invest more in generating engaging public health campaigns on social media.
| S-EPMC9217154 | biostudies-literature
Project description:Since the World Health Organization (WHO, February 2, 2020) reported that the spread of coronavirus disease has been accompanied by a “massive infodemic,” the COVID-19 outbreak has become a national and international battleground of a struggle against misinformation. Fact-checking outlets around the world have been actively counteracting false and misleading information surrounding the pandemic. In this article, we conceptualize fact checkers in terms of the “interpretative power” that journalism holds in processes of political performances (Alexander in Soc Theory 22(4): 527–573, 2004, in: The performance of politics. Obama’s victory and the struggle for democratic power. Oxford University Press, Oxford/New York, 2010). Drawing on virus-related fact checks from Poynter’s International Fact-Checking Network (IFCN) database, we make two arguments. First, we argue that the new phenomenon of specialized “fact checking” might be considered as a further explicitly differentiated element of Alexander’s model of cultural performance, which fulfills a double duty: trying to contribute to further “de-fusion” (separating audiences from actors when the latter lack authenticity and credibility) on the one hand, and working to overcome it on the other. Second, we explain how new fact-checking practices have become a reflexive supplement to the news media of the civil sphere that might be able to help the civil sphere’s communicative institutions to defend truthfulness in a manner that contributes to democracy.
| S-EPMC7520375 | biostudies-literature
Project description:Nucleotide sequence reagents are verifiable experimental reagents in biomedical publications, because their sequence identities can be independently verified and compared with associated text descriptors. We have previously reported that incorrectly identified nucleotide sequence reagents are characteristic of highly similar human gene knockdown studies, some of which have been retracted from the literature on account of possible research fraud. Because of the throughput limitations of manual verification of nucleotide sequences, we developed a semi-automated fact checking tool, Seek & Blastn, to verify the targeting or non-targeting status of published nucleotide sequence reagents. From previously described and unknown corpora of 48 and 155 publications, respectively, Seek & Blastn correctly extracted 304/342 (88.9%) and 1066/1522 (70.0%) nucleotide sequences and a predicted targeting/ non-targeting status. Seek & Blastn correctly predicted the targeting/ non-targeting status of 293/304 (96.4%) and 988/1066 (92.7%) of the correctly extracted nucleotide sequences. A total of 38/39 (97.4%) or 31/79 (39.2%) Seek & Blastn predictions of incorrect nucleotide sequence reagent use were correct in the two literature corpora. Combined Seek & Blastn and manual analyses identified a list of 91 misidentified nucleotide sequence reagents, which could be built upon through future studies. In summary, incorrect nucleotide sequence reagents represent an under-recognized source of error within the biomedical literature, and fact checking tools such as Seek & Blastn may help to identify papers and manuscripts affected by these errors.
| S-EPMC6396917 | biostudies-literature
Project description:Professional fact-checkers and fact-checking organizations provide a critical public service. Skeptics of modern media, however, often question the accuracy and objectivity of fact-checkers. The current study assessed agreement among two independent fact-checkers, The Washington Post and PolitiFact, regarding the false and misleading statements of then President Donald J. Trump. Differences in statement selection and deceptiveness scaling were investigated. The Washington Post checked PolitiFact fact-checks 77.4% of the time (22.6% selection disagreement). Moderate agreement was observed for deceptiveness scaling. Nearly complete agreement was observed for bottom-line attributed veracity. Additional cross-checking with other sources (Snopes, FactCheck.org), original sources, and with fact-checking for the first 100 days of President Joe Biden's administration were inconsistent with potential ideology effects. Our evidence suggests fact-checking is a difficult enterprise, there is considerable variability between fact-checkers in the raw number of statements that are checked, and finally, selection and scaling account for apparent discrepancies among fact-checkers.
| S-EPMC10368232 | biostudies-literature
Project description:This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: "faithfulness" which requires that the oscillating variables of all attractors in a trap space correspond to their dimensions, "univocality" which requires that there is a unique attractor in each trap space, and "completeness" which requires that there are no attractors outside of a given set of trap spaces. Each is a reachability property for which we give equivalent model checking queries. Whereas faithfulness and univocality can be decided by model checking the corresponding subnetworks, the naive query for completeness must be evaluated on the full state space. Our main result is an alternative approach which is based on the iterative refinement of an initially poor approximation. The algorithm detects so-called autonomous sets in the interaction graph, variables that contain all their regulators, and considers their intersection and extension in order to perform model checking on the smallest possible state spaces. A benchmark, in which we apply the algorithm to 18 published Boolean networks, is given. In each case, the minimal trap spaces are faithful, univocal, and complete, which suggests that they are in general good approximations for the asymptotics of Boolean networks.
| S-EPMC4562258 | biostudies-literature
Project description:The spread of misinformation is a global phenomenon, with implications for elections, state-sanctioned violence, and health outcomes. Yet, even though scholars have investigated the capacity of fact-checking to reduce belief in misinformation, little evidence exists on the global effectiveness of this approach. We describe fact-checking experiments conducted simultaneously in Argentina, Nigeria, South Africa, and the United Kingdom, in which we studied whether fact-checking can durably reduce belief in misinformation. In total, we evaluated 22 fact-checks, including two that were tested in all four countries. Fact-checking reduced belief in misinformation, with most effects still apparent more than 2 wk later. A meta-analytic procedure indicates that fact-checks reduced belief in misinformation by at least 0.59 points on a 5-point scale. Exposure to misinformation, however, only increased false beliefs by less than 0.07 points on the same scale. Across continents, fact-checks reduce belief in misinformation, often durably so.
| S-EPMC8449384 | biostudies-literature
Project description:This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were trained to make corrections to the free-stream velocity field and the boundary layer. They were trained on a limited data-set consisting of simple two-dimensional flows. The KBaNNs were then tested on a flow over a more complex geometry, a NACA 2412 airfoil. It was demonstrated that the KBaNNs were still able to provide a local correction to the velocity field which improved its accuracy. The ability of the KBaNNs to generalise to flows around new geometries that share similar physics is encouraging. Through knowledge based neural networks it may be possible to develop a system for bi-fidelity, computer based design which uses data from past simulations to inform its predictions.
| S-EPMC8280202 | biostudies-literature
Project description:MotivationInvestigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior.ResultsThe above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust.AvailabilityGNA and the IRMA model are available at http://ibis.inrialpes.fr/.
| S-EPMC2935427 | biostudies-literature