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Islands and Bridges of Language: Bio-Inspired Structural Analysis of Language Embedding Data
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Found speech and humans in the loop : Ways to gain insight into large quantities of speech
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Support in-video content searching and result visualization of the flipped classroom ; Stöd sökning av innehåll i video och resultatvisualisering av det flippade klassrummet
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Su, Siyuan. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2022
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eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University
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In: Lee, Hyowon orcid:0000-0003-4395-7702 , Scriney, Michael orcid:0000-0001-6813-2630 , Dey-Plissonneau, Aparajita and Smeaton, Alan orcid:0000-0003-1028-8389 (2021) Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University. In: Virtual Exchange in Higher Education: Charting the Irish Experience, 17 Sept 2021, Online vs MS Teams. (2021)
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Data Visualization, Dashboards, and Evidence Use in Schools: Data Collaborative Workshop Perspectives of Educators, Researchers, and Data Scientists
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(an:a)-lyzer: An interactive visualization of Google Books Ngrams with R and Shiny: Exploring a(n) historical increase in onset strength in a(n) huge database ...
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Making Visible the Invisible Work of Scientists during the COVID-19 Pandemic ...
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Making Visible the Invisible Work of Scientists during the COVID-19 Pandemic ...
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Analyzing Non-Textual Content Elements to Detect Academic Plagiarism
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Abstract:
Identifying academic plagiarism is a pressing problem, among others, for research institutions, publishers, and funding organizations. Detection approaches proposed so far analyze lexical, syntactical, and semantic text similarity. These approaches find copied, moderately reworded, and literally translated text. However, reliably detecting disguised plagiarism, such as strong paraphrases, sense-for-sense translations, and the reuse of non-textual content and ideas, is an open research problem. The thesis addresses this problem by proposing plagiarism detection approaches that implement a different concept—analyzing non-textual content in academic documents, such as citations, images, and mathematical content. The thesis makes the following research contributions. It provides the most extensive literature review on plagiarism detection technology to date. The study presents the weaknesses of current detection approaches for identifying strongly disguised plagiarism. Moreover, the survey identifies a significant research gap regarding methods that analyze features other than text. Subsequently, the thesis summarizes work that initiated the research on analyzing non-textual content elements to detect academic plagiarism by studying citation patterns in academic documents. To enable plagiarism checks of figures in academic documents, the thesis introduces an image-based detection process that adapts itself to the forms of image similarity typically found in academic work. The process includes established image similarity assessments and newly proposed use-case-specific methods. To improve the identification of plagiarism in disciplines like mathematics, physics, and engineering, the thesis presents the first plagiarism detection approach that analyzes the similarity of mathematical expressions. To demonstrate the benefit of combining non-textual and text-based detection methods, the thesis describes the first plagiarism detection system that integrates the analysis of citation-based, image-based, math-based, and text-based document similarity. The system’s user interface employs visualizations that significantly reduce the effort and time users must invest in examining content similarity. To validate the effectiveness of the proposed detection approaches, the thesis presents five evaluations that use real cases of academic plagiarism and exploratory searches for unknown cases. Real plagiarism is committed by expert researchers with strong incentives to disguise their actions. Therefore, I consider the ability to identify such cases essential for assessing the benefit of any new plagiarism detection approach. The findings of these evaluations are as follows. Citation-based plagiarism detection methods considerably outperformed text-based detection methods in identifying translated, paraphrased, and idea plagiarism instances. Moreover, citation-based detection methods found nine previously undiscovered cases of academic plagiarism. The image-based plagiarism detection process proved effective for identifying frequently observed forms of image plagiarism for image types that authors typically use in academic documents. Math-based plagiarism detection methods reliably retrieved confirmed cases of academic plagiarism involving mathematical content and identified a previously undiscovered case. Math-based detection methods offered advantages for identifying plagiarism cases that text-based methods could not detect, particularly in combination with citation-based detection methods. These results show that non-textual content elements contain a high degree of semantic information, are language-independent, and largely immutable to the alterations that authors typically perform to conceal plagiarism. Analyzing non-textual content complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of academic plagiarism. ; published
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Keyword:
Citation Analysis; Content-based Image Retrieval; Data mining; ddc:004; Digital libraries and archives; Document representation; Evaluation of retrieval results; Image search; Information extraction; Information integration; Information Visualization; Link and co-citation analysis; Math Retrieval; Mathematics retrieval; Multilingual and cross-lingual retrieval; Natural Language Processing; Near-duplicate and plagiarism detection; Open Source Software; Plagiarism Detection; Retrieval models and ranking; Surveys and overviews; User Interaction; Users and interactive retrieval; Web searching and information discovery; Web-based interaction
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URL: https://doi.org/10.5281/zenodo.4913345
http://nbn-resolving.de/urn:nbn:de:bsz:352-2-ll951b8bh8s30
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An interactive visualization of Google Books Ngrams with R and Shiny : exploring a(n) historical increase in onset strength in a(n) huge database
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An interactive visualization of Google Books Ngrams with R and Shiny : exploring a(n) historical increase in onset strength in a(n) huge database
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A linguagem da nutrição: criação e análise de corpus como base para a elaboração de recursos linguísticos e visualizações de dados
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Visualisation of semantic shifts: the case of modal markers ...
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