Universität Wien FIND

Auf Grund der COVID-19 Pandemie kann es bei Lehrveranstaltungen und Prüfungen auch kurzfristig zu Änderungen kommen. Informieren Sie sich laufend in u:find und checken Sie regelmäßig Ihre E-Mails.

Lesen Sie bitte die Informationen auf https://studieren.univie.ac.at/info.

Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

128141 FS FS Research Seminar I / II (2021S)

Stylometry and authorship attribution

10.00 ECTS (2.00 SWS), SPL 12 - Anglistik
Prüfungsimmanente Lehrveranstaltung
DIGITAL

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 20 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine

Vorläufig online
Donnerstag 16:15-17:45
Beginn: 11.03.2021

IMPORTANT: All projects will be presented at a student-mini-conference, which will take place online on Saturday, June 19th, 10:00 to 14:00! Attendance is required.


Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Authorship attribution is concerned with identifying the author of an – often anonymously written – text. The term subsumes a variety of methods, both qualitative and quantitative, that are used for diverse applications in many different fields such as historical linguistics (e.g. identifying the author of a historical text), forensic linguistics (e.g. identifying the author of fraud mail), or plagiarism detection. In this course, we will focus on quantitative methods from the field of stylometry. Students will learn how to employ statistical programming, methods from text mining and basic machine-learning techniques in authorship attribution tasks. Students will work in groups on small projects, in which they first compile their own training corpus and then apply relevant methods to tackle anonymous texts.

This course has a quantitative focus. Statistical programming will be done in R(Studio Cloud). In this regard, no preliminary knowledge is required (introductory tutorials will be provided), however, previous exposure to R (e.g. in the MA English Research Methods course) will prove useful. I will take knowledge of high-school mathematics (basic calculus, percentages/fractions, probabilities, functions like log or exp) as well as of linguistic concepts (word classes, types/tokens, etc.) for granted.

All classes will take place online. There will be individual feedback sessions for each group (dates to be announced).

IMPORTANT: All projects will be presented at a student-mini-conference, which will take place online on Saturday, June 19th, 10:00 to 14:00! Attendance is required.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Student assessment is based on their assignments (online- &/or offline) & class participation; their project presentation (in groups), the research project log consisting of individual reflections and group research documentation and the project report (in groups), consisting of (a) a project description, (b) a literature review, (c) data collection, (d) data analysis and interpretation. The presentation and report are based on the same research project.

Mindestanforderungen und Beurteilungsmaßstab

Course evaluation is based on:
*) class participation and tasks (oral, written &/or eLearning)
*) oral group presentations
*) a research process log consisting of individual reflections and group research documentation
*) a final group project report consisting of various assignments

The minimum requirements for passing the course are:
(a) regular class attendance (max. 2 absences)
(b) engaging actively in group work (both online- and/or offline) and project milestones (on set dates)
(c) handing in the research process log and the project report and its parts (on time)
(d) attaining 60 of the maximum of 100 points.

Final grades & points achieved:
1: 90-100; 2: 80-89; 3: 70-79; 4: 60-69; 5: 0-59

• Programming exercises (10%, individual)
• Abstract (10%, in groups)
• Project log (15%, individual)
• Presentation (15%, in groups)
• Project report (35%, in groups)
• Participation in class (15%, individual)

Prüfungsstoff

Continuous assessment based on what was covered in class, details will be given in class and on Moodle.

Literatur

Information on the required readings will be provided on Moodle.

Chaski, C. (2013). Best Practices and Admissibility of Forensic Author Identification. Journal of Law and Policy, 21(2), 333–376.
Juola, P. (2006). Authorship attribution. Foundations and Trends in Information Retrieval, 1(3): 233-334.
Koppel, M., Schler, J., & Argamon, S. (2009). Computational methods in authorship attribution. Journal of the American Society for Information Science and Technology. https://doi.org/10.1002/asi.20961
Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology. https://doi.org/10.1002/asi.21001

Zuordnung im Vorlesungsverzeichnis

Studium: MA 812 (2)
Code/Modul: M04 FS. M05
Lehrinhalt: 12-8142

Letzte Änderung: Mi 21.04.2021 11:26