Dataset Information

Scientific Content Analysis (SCAN) Cannot Distinguish Between Truthful and Fabricated Accounts of a Negative Event.

ABSTRACT: The Scientific Content Analysis (SCAN) is a verbal veracity assessment method that is currently used worldwide by investigative authorities. Yet, research investigating the accuracy of SCAN is scarce. The present study tested whether SCAN was able to accurately discriminate between true and fabricated statements. To this end, 117 participants were asked to write down one true and one fabricated statement about a recent negative event that happened in their lives. All statements were analyzed using 11 criteria derived from SCAN. Results indicated that SCAN was not able to correctly classify true and fabricated statements. Lacking empirical support, the application of SCAN in its current form should be discouraged.

SUBMITTER: Bogaard G

PROVIDER: S-EPMC4766305 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Scientific Content Analysis (SCAN) Cannot Distinguish Between Truthful and Fabricated Accounts of a Negative Event.

Bogaard Glynis G Meijer Ewout H EH Vrij Aldert A Merckelbach Harald H

Frontiers in psychology 20160225

The Scientific Content Analysis (SCAN) is a verbal veracity assessment method that is currently used worldwide by investigative authorities. Yet, research investigating the accuracy of SCAN is scarce. The present study tested whether SCAN was able to accurately discriminate between true and fabricated statements. To this end, 117 participants were asked to write down one true and one fabricated statement about a recent negative event that happened in their lives. All statements were analyzed usi ...[more]

PMID: 26941694

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Project description:BACKGROUND:Cellular events play a central role in the understanding of biological processes and functions, providing insight on both physiological and pathogenesis mechanisms. Automatic extraction of mentions of such events from the literature represents an important contribution to the progress of the biomedical domain, allowing faster updating of existing knowledge. The identification of trigger words indicating an event is a very important step in the event extraction pipeline, since the following task(s) rely on its output. This step presents various complex and unsolved challenges, namely the selection of informative features, the representation of the textual context, and the selection of a specific event type for a trigger word given this context. RESULTS:We propose TrigNER, a machine learning-based solution for biomedical event trigger recognition, which takes advantage of Conditional Random Fields (CRFs) with a high-end feature set, including linguistic-based, orthographic, morphological, local context and dependency parsing features. Additionally, a completely configurable algorithm is used to automatically optimize the feature set and training parameters for each event type. Thus, it automatically selects the features that have a positive contribution and automatically optimizes the CRF model order, n-grams sizes, vertex information and maximum hops for dependency parsing features. The final output consists of various CRF models, each one optimized to the linguistic characteristics of each event type. CONCLUSIONS:TrigNER was tested in the BioNLP 2009 shared task corpus, achieving a total F-measure of 62.7 and outperforming existing solutions on various event trigger types, namely gene expression, transcription, protein catabolism, phosphorylation and binding. The proposed solution allows researchers to easily apply complex and optimized techniques in the recognition of biomedical event triggers, making its application a simple routine task. We believe this work is an important contribution to the biomedical text mining community, contributing to improved and faster event recognition on scientific articles, and consequent hypothesis generation and knowledge discovery. This solution is freely available as open source at http://bioinformatics.ua.pt/trigner.

Project description:ObjectiveTo investigate whether language used in science abstracts can skew towards the use of strikingly positive and negative words over time.DesignRetrospective analysis of all scientific abstracts in PubMed between 1974 and 2014.MethodsThe yearly frequencies of positive, negative, and neutral words (25 preselected words in each category), plus 100 randomly selected words were normalised for the total number of abstracts. Subanalyses included pattern quantification of individual words, specificity for selected high impact journals, and comparison between author affiliations within or outside countries with English as the official majority language. Frequency patterns were compared with 4% of all books ever printed and digitised by use of Google Books Ngram Viewer.Main outcome measuresFrequencies of positive and negative words in abstracts compared with frequencies of words with a neutral and random connotation, expressed as relative change since 1980.ResultsThe absolute frequency of positive words increased from 2.0% (1974-80) to 17.5% (2014), a relative increase of 880% over four decades. All 25 individual positive words contributed to the increase, particularly the words "robust," "novel," "innovative," and "unprecedented," which increased in relative frequency up to 15,000%. Comparable but less pronounced results were obtained when restricting the analysis to selected journals with high impact factors. Authors affiliated to an institute in a non-English speaking country used significantly more positive words. Negative word frequencies increased from 1.3% (1974-80) to 3.2% (2014), a relative increase of 257%. Over the same time period, no apparent increase was found in neutral or random word use, or in the frequency of positive word use in published books.ConclusionsOur lexicographic analysis indicates that scientific abstracts are currently written with more positive and negative words, and provides an insight into the evolution of scientific writing. Apparently scientists look on the bright side of research results. But whether this perception fits reality should be questioned.

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Scientific Content Analysis (SCAN) Cannot Distinguish Between Truthful and Fabricated Accounts of a Negative Event.

Publications

Scientific Content Analysis (SCAN) Cannot Distinguish Between Truthful and Fabricated Accounts of a Negative Event.

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