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Optimising and Automating the Choice of Search Strings when Investigating Possible Plagiarism

Optimising and Automating the Choice of Search Strings when Investigating Possible Plagiarism Fintan Culwin London South Bank University fintan @ lsbu.ac.uk 4 th International Plagiarism Conference 23 June 2010. “to google”. 1. Take a phrase from a student’s submission.

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Optimising and Automating the Choice of Search Strings when Investigating Possible Plagiarism

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  1. Optimising and Automating the Choice of Search Strings when Investigating Possible Plagiarism Fintan Culwin London South Bank University fintan @ lsbu.ac.uk 4th International Plagiarism Conference 23 June 2010

  2. “to google” 1. Take a phrase from a student’s submission. 4. Examine each hit against the submission & attempt to find similarities. 2. Type the phrase into google. 3. Download the top ten hits.

  3. “. . . [skimming] each paper, looking for one or more memorable phrases to conduct a manual, full text search . . . “. Kaner, C. & Fiedler, R.L., ‘A Cautionary Note on Checking Software Engineering Papers for Plagiarism’ IEEE Transaction on Education, 51(2) pp 184-8 2008

  4. 3 * 3 * 7 * 2 * 6 * 5 * 5 = 18,900 possible sentences each with a probability of .00005

  5. It is possible by chance alone. “it” is reported by Google to appear in about 850,000,000 documents. “it is” in about 265,000,000 documents. “it is possible” in about 72,000,000 documents. “it is possible by” in about 235,000 documents. “it is possible by chance” in about 142,000 documents. “it is possible by chance alone” in 3 documents. Searches for ‘a’ and ‘the’ give about 25,000,000,000 documents. Therefore the chance of any document containing the sentence is 1 in 8,000,000,000. Or a probability of .0000000001. Hence the probability of the phrase occurring twice in a corpus of student submissions?

  6. 13 documents from the IEEE digital library 3 random 6 word phrases from each This replicates the Kane & Fielder study.

  7. 13 random documents from the ACM digital library 3 random 6 word phrases from each

  8. 13 random Wikipedia articles 3 random 6 word phrases from each

  9. all 39 documents and 234 searches

  10. Searches using documents from the International Index of the Performing Arts and from the Academic OneFile database IIPA Academic OneFile (using Google) (using Google) (using Yahoo) (using Yahoo)

  11. Idioms & Statistics Sentences are produced by a mixture of statistical and idiomatic construction. Statistical: That would be a parochial matter. 0 google hits Idiomatic : That would be an ecumenical matter. 805,000 google hits

  12. Since the paper was written . . . Sergey Butakov & Vladislav Shcherbinin 2009 On the number of search queries required for Internet plagiarism detection Proc. 9th IEEE International Conference on Advanced learning technologies Sergey Butakov & Vladislav Shcherbinin 2009 The toolbox for local and global plagiarism detection Computers and Education V52 pp781-788 Report on remarkably similar work to this: 7 word strings 5% of a document Using the Bing search engine

  13. Is six words sufficient? Google is the largest corpus of documents on the planet. The proportion of stop words in Google mirrors those in established corpora. Hence Google searches are (at least) as indicative as BNC or ANC searches. Idioms can be automatically excluded as they would yield a large number of hits. So can we conclude that the presence of a pre-existing 6 word string is indicative of academic misconduct?

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