Citation-based Plagiarism Detection [electronic resource] : Detecting Disguised and Cross-language Plagiarism using Citation Pattern Analysis /
Gipp, Bela.author.
Plagiarism is a problem with far-reaching consequences for the sciences. However, even today’s best software-based systems can only reliably identify copy&paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The author proposes Citation-based Plagiarism Detection to address this shortcoming. Unlike character-based approaches, this approach does not rely on text comparisons alone, but analyzes citation patterns within documents to form a language-independent "semantic fingerprint" for similarity assessment. The practicability of Citation-based Plagiarism Detection was proven by its capability to identify so-far non-machine detectable plagiarism in scientific publications.  Contents Current state of plagiarism detection approaches and systems Citation-based Plagiarism Detection  Target Groups Readers interested in the problem of plagiarism in the sciences Faculty and students from all disciplines, but especially computer science The Author Bela Gipp is a postdoctoral researcher at the University of California, Berkeley.
Description based upon print version of record.
""Acknowledgements""; ""Contents""; ""List of Tables""; ""List of Figures""; ""Glossary""; ""Abstract""; ""Kurzfassung""; ""1 Introduction""; ""1.1 Problem Setting""; ""1.2 Motivation""; ""1.3 Research Objective""; ""1.4 Thesis Outline""; ""2 Plagiarism Detection""; ""2.1 Academic Plagiarism""; ""2.1.1 Definition""; ""2.1.2 Forms of Academic Plagiarism""; ""2.1.3 Prevalence of Plagiarism in the Academic Environment""; ""2.2 Plagiarism Detection Approaches""; ""2.2.1 Generic Detection Approach""; ""2.2.2 Overview of Plagiarism Detection Approaches""; ""2.2.3 Fingerprinting""
""2.2.4 Term Occurrence Analysis""""2.2.5 Stylometry""; ""2.2.6 Cross-Language Plagiarism Detection""; ""2.3 Plagiarism Detection Systems""; ""2.3.1 Evaluations of PDS""; ""2.3.2 Technical Weaknesses of PDS""; ""2.4 Conclusion""; ""3 Citation-based Document Similarity""; ""3.1 Terminology""; ""3.1.1 Citation vs. Reference""; ""3.1.2 Similarity vs. Relatedness""; ""3.1.3 Dimensions of Similarity: Lexical, Semantic, Structural""; ""3.2 Citation-based Similarity Measures""; ""3.2.1 Direct Citation""; ""3.2.2 Bibliographic Coupling""; ""3.2.3 Co-citation""; ""3.2.4 Amsler""
""3.2.5 Co-citation Proximity-based Methods""""3.3 Conclusion""; ""4 Citation-based Plagiarism Detection""; ""4.1 Concept""; ""4.1.1 Citing Behavior""; ""4.2 Citation Characteristics Considered""; ""4.2.1 Bibliographic Coupling Strength""; ""4.2.2 Probability of Citation Co-occurrence""; ""4.2.3 Order and Proximity of Citations""; ""4.3 Challenges to Citation Pattern Identification""; ""4.3.1 Unknown Pattern Constituents""; ""4.3.2 Transpositions""; ""4.3.3 Scaling""; ""4.3.4 Insertions or Substitutions of Citations""; ""4.4 Design of Citation-based Detection Algorithms""
""4.4.1 Bibliographic Coupling (BC)""""4.4.2 Longest Common Citation Sequence (LCCS)""; ""4.4.3 Greedy Citation Tiling (GCT)""; ""4.4.4 Citation Chunking (Cit-Chunk)""; ""4.5 Projected Suitability of CbPD Algorithms for Plagiarism Forms""; ""4.6 Assessment of Identified Citation Patterns""; ""4.6.1 Citing Frequency-Score (CF-Score)""; ""4.6.2 Continuity-Score (Cont.-Score)""; ""4.7 Conclusion""; ""5 Prototype: CitePlag""; ""5.1 Document Parser""; ""5.2 Database""; ""5.2.1 Consolidation of Reference Identifiers""; ""5.3 Detector""; ""5.4 Frontend""; ""5.5 Conclusion""
""6 Quantitative and Qualitative Evaluation""""6.1 Methodology""; ""6.1.1 Test Collection Requirements""; ""6.1.2 Test Collection Challenges""; ""6.1.3 GuttenPlag Wiki""; ""6.1.4 VroniPlag Wiki""; ""6.1.5 PubMed Central OAS""; ""6.1.6 Summary and Comparison of Test Collections""; ""6.2 Evaluation using GuttenPlag Wiki""; ""6.3 Evaluation using VroniPlag Wiki""; ""6.3.1 Evaluation: Random Sample of Sources""; ""6.3.2 Evaluation: Translated Plagiarism""; ""6.3.3 Evaluation: Plagiarism Case Heun""; ""6.3.4 Conclusion VroniPlag Wiki""; ""6.4 Evaluation using PubMed Central OAS36""
""6.4.1 Methodology""
Includes bibliographical references and index.
Plagiarism is a problem with far-reaching consequences for the sciences. However, even today’s best software-based systems can only reliably identify copy&paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The author proposes Citation-based Plagiarism Detection to address this shortcoming. Unlike character-based approaches, this approach does not rely on text comparisons alone, but analyzes citation patterns within documents to form a language-independent "semantic fingerprint" for similarity assessment. The practicability of Citation-based Plagiarism Detection was proven by its capability to identify so-far non-machine detectable plagiarism in scientific publications.  Contents Current state of plagiarism detection approaches and systems Citation-based Plagiarism Detection  Target Groups Readers interested in the problem of plagiarism in the sciences Faculty and students from all disciplines, but especially computer science The Author Bela Gipp is a postdoctoral researcher at the University of California, Berkeley.
Artificial intelligence.
Computers.
Artificial Intelligence.
Information Systems and Communication Service.