existing guidelines, troponin tests can be requested for patients with chest pain of potentially cardiac origin upon the patients’ admission, six to 12 hours after its first measurement and also 12 to 24 hours after the onset of the symptoms (9, 10). Although performing this test has a key role in the diagnosis, prognosis and classification of the risk of acute coronary syndrome, its overuse and inappropriate requests increase laboratory personnel’s workload, the costs imposed on the health system, unnecessary hospital stays and the costs incurred by the patients (11-13). The present study was therefore conducted to examine the veracity of troponin test requests for patients presenting to an emergency department with chest pain and the effectiveness of training emergency medical assistants in reducing unnecessary and inappropriate requests in emergency departments.
So, is fish good for you or not good for you? Do you need to have clients do eye exercises in the clinic or can they do them as a home program? The answers are in the data. Differing results may indicate that the veracity of the data is lacking, that there may be an error in the data analyses, or that the researchers may be misinterpreting the data. Whether you are conducting a study with big data or small data, using steps to foster the veracity of the data is important to ensure that clinical practice is accurately informed. Let the data lead you where they may, even if they counter your assumptions and expectations. With improved veracity in the data, we can ensure better evidence on which to base our practice.
Finally, the analysis of the research model, introduced in Chapter 1, did not find significant support for the mediating role of the visual attention. In this model, we did not investigate the direct effect of message veracity and media on detection accuracy. Visual attention is a complex topic, affected by both psychological and physiological factors (Duchowski, 2007). Human visual attention is driven by both, the low-level visual features of the scene observed, as well as the higher-level intentional factors, such as cognitive processes navigating the visual focus. Our interpretation of the elements we see, however, does not have to be unanimous. Viewers may attribute completely opposite meanings to the same stimuli they observe. For example, prominence interpretation theory (Fogg, 2003) suggests that when assessing the credibility of a website, users must first see the elements of the website and then interpret that element to come to a conclusion. While gaze aversion could be interpreted as a sign of deception, some judges may interpret it as an attempt to remember the details of the situation being told. Thus, when presented with the task to judge the veracity of the speaker, the judges may pick up visual behaviors, and yet their final assessment will eventually be driven by the interpretations they apply to the elements they have noticed.
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We propose a shared task where participants analyse rumours in the form of claims made in user-generated content, and where users respond to one another within conversations attempting to resolve the veracity of the rumour. We define a ru- mour as a “circulating story of questionable verac- ity, which is apparently credible but hard to verify, and produces sufficient scepticism and/or anxiety so as to motivate finding out the actual truth” (Zu- biaga et al., 2015b). While breaking news unfold, gathering opinions and evidence from as many sources as possible as communities react becomes crucial to determine the veracity of rumours and consequently reduce the impact of the spread of misinformation.
There was a great emphasis on correct evaluation of data from the qualitative research. The team that participated in evaluation consisted of social media and marketing experts and a psychologist. Evaluation of acquired data was carried out in two phases. Outputs from the dual group interview were specified separately for each group in the first phase. Five partial tables with the resulting attributes were produced. Subsequently, a data synthesis into the final 20-attribute form took place: Speed of response to my question, Individual approach of the company, Online company communication all day long, Regular updates of information, The veracity of the information provided, Clarity of information provided, Humorous form of information, Qualification of provided information, Lotteries, contests, coupons from companies to SM, Complaint processing, Obtaining information for SM through advertising, Index of corporate information to social media, The way of providing information to social media, Presentation in Czech, Corporate social responsibility (environ- ment, ethics), Supporting not-for-profit events (cultural, sports), Link to the company's website, Acquiring solicited information only, Obtaining integral and complete information, Communication through forums (chat).
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Automatically verifying rumorous informa- tion has become an important and challeng- ing task in natural language processing and so- cial media analytics. Previous studies reveal that people’s stances towards rumorous mes- sages can provide indicative clues for identi- fying the veracity of rumors, and thus deter- mining the stances of public reactions is a cru- cial preceding step for rumor veracity predic- tion. In this paper, we propose a hierarchical multi-task learning framework for jointly pre- dicting rumor stance and veracity on Twitter, which consists of two components. The bot- tom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network. The top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution. Experimental results on two bench- mark datasets show that our method outper- forms previous methods in both rumor stance classification and veracity prediction.
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In this paper we problematize relational transparency as an element of authentic leadership when viewed through the lens of emotional labour. Using the method of analytic co-constructed auto-ethnography we examine a senior hospital manager’s experience of seeking to be authentic during a period of intense challenge as he pursues the closure of a hospital ward. A first-person account is developed that speaks to the necessity of hiding felt emotions and displaying his perceptions of desired emotions warranted in the context in which he seeks to lead. That this is not experienced as inauthentic is seen as deriving from two dimensions of experienced authenticity: strength of identification with leadership role and fidelity to leadership purpose. The veracity of this reframing of authenticity in leadership practice is explored through a second study, of practising leaders required to balance the demands of performing emotional labour and appearing and feeling authentic. We suggest that reframing relational transparency as ‘fidelity to purpose’ may be a valuable counter-weight to the goal of relational transparency promulgated by the leadership industry and a practical advance for those seeking to practise authentic leadership.
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Other research has focused on stylistic tells of un- trustworthiness in the source itself (Conroy et al., 2015; Singhania et al., 2017). Rumour verifica- tion is a particular case of fact checking. Rumours are “circulating stories of questionable veracity, which are apparently credible but hard to verify, and produce sufficient skepticism and/or anxiety so as to motivate finding out the actual truth” (Zu- biaga et al., 2016). One can distinguish several component to a rumour resolution pipeline such as rumour detection, rumour tracking and stance classification, leading to the final outcome of de- termining the veracity of a rumour (Zubiaga et al., 2018). Thus what characterises rumour verifica- tion compared to other types of fact checking is time sensitivity and the importance of dynamic in- teractions between users, their stance and infor- mation propagation. Initial work on rumour de- tection and stance classification (Qazvinian et al., 2011) was succeeded by more elaborate systems and annotation schemas (Kumar and Geethaku- mari, 2014; Zhang et al., 2015; Shao et al., 2016; Zubiaga et al., 2016). Vosoughi (2015) demon- strated the value of making use of propagation in- formation, i.e. the ensuing discussion, in rumour verification. Stance detection is the task of clas- sifying a text according to the position it takes with respect to a statement. Research supports the importance of this subtask as a first step to
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This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year’s SemEval event. Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the dangers of “fake news” have become a mainstream concern. Yet automated support for rumour checking re- mains in its infancy. For this reason, it is important that a shared task in this area continues to provide a focus for effort, which is likely to increase. We therefore pro- pose a continuation in which the veracity of further ru- mours is determined, and as previously, supportive of this goal, tweets discussing them are classified accord- ing to the stance they take regarding the rumour. Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also included.
Context-based fake news detection. Here, fake news items are identified via meta information and spread patterns. For example, Long et al. (2017) show that author information can be a useful fea- ture for fake news detection, and Derczynski et al. (2017) attempt to determine the veracity of a claim based on the conversation it sparks on Twitter as one of the RumourEval tasks. The Facebook analy- sis of Mocanu et al. (2015) shows that unsubstan- tiated claims spread as widely as well-established ones, and that user groups predisposed to conspir- acy theories are more open to sharing the former. Similarly, Acemoglu et al. (2010), Kwon et al. (2013), Ma et al. (2017), and Volkova et al. (2017) model the spread of (mis-)information, while Bu- dak et al. (2011) and Nguyen et al. (2012) propose algorithms to limit its spread. The efficacy of coun- termeasures like debunking sites is studied by Tam- buscio et al. (2015). While achieving good results, context-based approaches suffer from working only a posteriori, requiring large amounts of data, and disregarding the actual news content.
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and the system scores an accuracy of 0.53. While the system described above engages in the task of resolving veracity given a single ru- mour text, another interesting approach is based on the use of crowd/collective stance, which is the set of stances over a conversation (Dungs et al., 2018). This system predicts the veracity of a ru- mour, based solely on crowd stance as well as tweet times. A Hidden Markov Model (HMM) is implemented, which is utilised such that individ- ual stances over a rumour’s lifetime is regarded as an ordered sequence of observations. This is then used to compare sequence occurrence probabili- ties for true and false rumours respectively. The best scoring model, which include both stance la- bels and tweet times, scores an F 1 of 0.804, while
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Anthrologists and literary critics tend to read even sacred ancient literature in the manner of Homer’s and Virgil’s epics, that is, as fiction with historical elements. They don’t, however, always follow up with the implications of that. Mesopotamian myths and epics are similar to Greek and Roman ones in that regard. The pertinent questions are who believed what and what effect literal belief in myths had on given social orders. One answer in the Hebraic tradition is typical of other traditions, namely that calls for reform at home and for campaigns against enemies abroad rely heavily on the presumed historicity of the texts. For the Israelites, that means the unquestioned validity of covenants struck between legendary patriarchs and Yahweh, at least within the Yahweh cult itself. The hybrid forms of Dante, Milton, and others in the Christian European tradition draw on both well-traveled epic conventions and the veracity of biblical tra- ditions, as Milton does in turning a Homeric invocation of the muse into an appeal to the Holy Spirit. Much as Milton, too, is now read as a poet rather than an inspired seer, so probably were earlier authors who claimed direct personal revelations. If that was in fact the case, it would have weakened moral teachings less than cult recruitment and the call for military campaigns against foreign powers. Whereas legal and ethical matters have much to recommend them independently of their origin, waging war on re- ligious grounds requires strong convictions.
We present a data-driven method for determining the veracity of a set of rumorous claims on social media data. Tweets from different sources pertaining to a rumor are processed on three levels: first, factuality values are assigned to each tweet based on four textual cue categories relevant for our journalism use case; these amalgamate speaker support in terms of polarity and commitment in terms of certainty and speculation. Next, the proportions of these lexical cues are utilized as predictors for tweet certainty in a generalized linear regression model. Subse- quently, lexical cue proportions, predicted certainty, as well as their time course characteristics are used to compute veracity for each rumor in terms of the identity of the rumor-resolving tweet and its binary resolution value judgment. The system operates without access to extralinguis- tic resources. Evaluated on the data portion for which hand-labeled examples were available, it achieves .74 F1-score on identifying rumor resolving tweets and .76 F1-score on predicting if a rumor is resolved as true or false.
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The application of NIPT has revolutionized prenatal screening of common aneuploidies and other conditions of clinical relevance. This study provides further proof of the high accuracy of NIPT compared to conventional screening methods. The role of first trimester nuchal translucency measurement and conventional biochem- ical testing needs to be reassessed in the context of the use of cfDNA testing which is a powerful tool in pre- natal care. Furthermore, we hereby show that Veracity, a new NIPT test based on a novel technology which was developed to overcome many of the limitations of other NIPT tests, exhibits high accuracy both in validation studies and routine testing conditions. The test’s high read depth and ability to efficiently capture cell- free DNA fragments delivers state-of the-art performance in fetal aneuploidy detection, fetal fraction estimation and cost effectiveness.
The main question, which leads this analysis, interrogates the existence of a causality relation between the human development index and Index of Patent Rights. It was decided to use a data- base whereas all units of the cross section have the same periods of time for human development index and Index of Patent Rights. This database covers 84 countries with the two indicators be- tween 1975 and 2005. By the use of the Granger test, it was found that Index of Patent Rights does not temporally precede human development index, which indicates the veracity and, consequently, the corroboration of the idea of Trade Related Intellectual Property Rights should represent the interests of the richer countries’ great corporations, and not the underdeveloped nations, as said in the statement of World Trade Organization.
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The goals of this overview is to discuss the ethics of telling the patient the truth, with some historical backgrounds and it is importance in medical prac- tice, Justifying Less than Full Disclosure in some situations in which the truth may have a terrible impact on the occasional patient, and the influence of the culture on the health care professionals attitudes towards telling the patient the truth. Conclusion: The health care professionals need more awareness, and training in ethics of veracity and also in the communication skills espe- cially in the context of breaking bad news in telling the patient the truth about diagnosis, treatment outcomes, and prognosis of any serious illness.
Abstract: Open data initiative has been adopted by many countries worldwide due to the need for establishing agile open government and knowledge-based economy. As a result, we can witness an increasing amount of government open data shared on public government's portals that become sources of rich big data. While this scenario provides data transparency and eases accessibility for public data consumers, the quality aspect, or the veracity property (as commonly coined to big data) of open data is the topic of concerned. Not only poor quality data causes misleading results, the reputation of the government as an open data provider can also be negatively affected. Thus, to understand how the government's portals deal with the veracity aspect of their data, in this paper, we present the results of examining quality criteria imposed by selected government's data portals for their data contributors. In particular, we extract quality criteria from the open data policy of the government's data portals under study. The result shows that out of 108 portals, only 27% of the portals explicitly state their quality criteria in the policy, with varying coverage of quality criteria. The frequency of the identified 15 quality criteria shows the types of quality criteria that receive more (and less) attention by the open data portals based on their relative importance. We conclude with suggestions on the areas of further research and development in the government's open data.
news article, and extracted the text. The resulting datafile includes roughly 4,000 rows, each con- taining a claim discussed by Snopes annotators, the veracity label assigned to it, and the text of a news article related to the claim. The main chal- lenge in using this data for training/testing a fake news detector is that some of the links on a Snopes page that we collect automatically do not actually point to the discussed news article, i.e., the source of the claim. Many links are to pages that pro- vide contextual information for the fact-checking of the claim. Therefore, not all the texts in our automatically extracted dataset are reliable or sim- ply the “supporting” source of the claim. To come up with a reliable set of veracity-labeled news arti- cles, we randomly selected 312 items and assessed them manually. Two annotators performed inde- pendent assessments on the 312 items. A third an- notator went through the entire list of items for a final check and resolving disagreements. Snopes has a fine-grained veracity labeling system. We selected [fully] true, mostly true, mixture of true and false, mostly false, and [fully] false stories. Table 1 shows the distribution of these labels in the manually assessed 312 items, and how many from each category of news articles were veri- fied to be the “supporting” source (distributing the discussed claim), “context” (providing back- ground or related information about the topic of the claim), “debunking” (against the claim), “irrel- evant” (completely unrelated to the claim or dis- torted text) and ambiguous (not sure how it related