Areas of Research

We are interested in modeling and analyzing all kinds of humanities data, including digitized texts (electronic corpora) as well as born digital texts (social media), images, music, film and digital culture (e.g. video games). This page provides an overview of some recent areas of research from the Computational Humanities Group:

  1. Computer-based Analysis of Movies and Series
  2. Music Information Retrieval for Handwritten Folksongs
  3. Drametrics - Quantitative Analysis of Drama
  4. Social Media Analytics
  5. Crowdsourcing

1. Computer-based Analysis of Movies and Series

In the past, movies and series have been analyzed quantitatively mainly with regard to the frequency and length of their shots. However, there are many other features that can be studied in a computational, quantitative way, e.g. language, color and visual features (objects, people). Also check out the DHd working group on "Film and Video".

Workshops & Panels

  1. Organisation eines 2-tägigen, internationalen Workshops zum Thema “Analyse von Filmstil und Filmgenre mit computerbasierten Verfahren“ (Potsdam). Am Brandenburgischen Zentrum für Medienwissenschaften (ZeM). (2018)
  2. Organisation eines Panels im Rahmen der DHd 2018 (Köln) zum Thema “Computergestützte Film- und Videoanalyse”. (2018)
  3. Organisation des Symposiums „Film rechnen – Computerbasierte Methoden in der Filmanalyse“, in Regensburg. (2017)

Talks

  1. Burghardt, M. (2018). Computational Film Studies – Computergestützte Ansätze für die Analyse von Filmen und Serien. Vortrag im Rahmen des Digital Humanities Day, Leipzig.
  2. Burghardt, M. (2018). “Digitale Tools und Methoden für die Filmanalyse”. Gastvortrag an der Brandenburgisch Technischen Universität (BTU) Cottbus-Senftenberg, organisiert vom Fachgebiet Angewandte Medienwissenschaften.
  3. Burghardt, M. (2018). Using Subtitles and Movie Scripts for the Automatic Analysis of Movies and Series. International Workshop on “Computer-based Approaches for the Analysis of Film Style”
  4. Burghardt, M. (2015). Digital Humanities in Bewegung: Tools für die Filmanalyse. Im Rahmen der Ringvorlesung “Dispositiv Film”, Regensburg.

Publications

  1. Burghardt, M., Pause, J. & Walkowski, N.-O. (2019). Scalable Viewing in den Filmwissenschaften. In Book of Abstracts, DHd 2019.
  2. Burghardt, M., Meyer, S., Schmidtbauer, S. & Molz, J. (2019). “The Bard meets the Doctor” – Computergestützte Identifikation intertextueller Shakespearebezüge in der Science Fiction-Serie Dr. Who. In Book of Abstracts, DHd 2019.
  3. Burghardt, M., Kao, M. & Walkowski, NO (2018). Scalable MovieBarcodes – An Exploratory Interface for the Analysis of Movies. 3rd IEEE VIS Workshop on Visualization for the Digital Humanities, Berlin.
  4. Burghardt, M., Hafner, K. Edel, L., Kenaan, S. & Wolff, C. (2017). An Information System for the Analysis of Color Distributions in MovieBarcodes. In Proceedings of the 15th International Symposium of Information Science (ISI 2017).
  5. Burghardt, M., Kao, M. & Wolff, C. (2016). Beyond Shot Lengths – Using Language Data and Color Information as Additional Parameters for Quantitative Movie Analysis. In Book of Abstracts of the International Digital Humanities Conference (DH).
  6. Burghardt, M. & Wolff, C. (2016). Digital Humanities in Bewegung: Ansätze für die computergestützte Filmanalyse. In Book of Abstracts, DHd 2016.

2. Music Information Retrieval for Handwritten Folksongs

Symbolic music, i.e. music that has been written down, can be analyzed in a number of ways, for instance from the perspective of melodic similarity between specific songs or even just melodic fragments.

Talks

  1. Burghardt, M. (2018). Computational Musicology – Chancen und Herausforderungen beim Einsatz digitaler Methoden in der Musikwissenschaft am Beispiel monophoner Volkslieder. Im Rahmen des Corpus Monodicum-Projekts, 7. Juni 2018, Würzburg.
  2. Burghardt, M. (2018). “Music Information Retrieval meets Digital Humanities” – Computergestützte Erschließung und Analyse von handschriftlichen Volksliedblättern. Im Rahmen des MWW / DARIAH-DE Workshop “Suchtechnologien”, 25. Mai 2018, Klassik Stiftung Weimar.
  3. Burghardt, M. (2017). More Than Words – Computergestützte Erschließungsstrategien und Analyseansätze für handschriftliche Liedblätter. Im Rahmen des DH-Kolloquiums der Berlin-Brandenburgischen Akademie der Wissenschaften (BBAW).
  4. Burghardt, M. (2015). Encoding and Analyzing a Large Corpus of German Folk Music – A Case Study. Im Rahmen des Pre-conference Workshop bei der ISI 2015, Zadar.

Publications

  1. Burghardt, M. & Fuchs, F. (2019). A Computational Approach to Analyzing Musical Complexity of the Beatles. In Book of Abstracts, DH 2019.
  2. Burghardt, M. (2018). Digital Humanities in der Musikwissenschaft – Computer-gestützte Erschließungsstrategien und Analyseansätze für handschriftliche Liedblätter. In B. Wiermann & A. Bonte (Hrsg.): Bibliothek. Forschung und Praxis, Sonderheft “Digitale Forschungsinfrastruktur für die Musikwissenschaft” (Preprint).
  3. Burghardt, M. & Lamm, L. (2017). Entwicklung eines Music Information Retrieval-Tools zur Melodic Similarity-Analyse deutschsprachiger Volkslieder. GI Workshop „Musik trifft Informatik“, INFORMATIK 2017, Chemnitz.
  4. Burghardt, M. & Spanner, S. (2017). Allegro: User-centered Design of a Tool for the Crowdsourced Transcription of Handwritten Music Scores. Proceedings of the DATeCH (Digital Access to Textual Cultural Heritage) conference. ACM.
  5. Burghardt, M., Spanner, S., Schmidt, T., Fuchs, F., Buchhop, K., Nickl, M. & Wolff, C. (2017). Digitale Erschließung einer Sammlung von Volksliedern aus dem deutschsprachigen Raum. In Book of Abstracts, DHd 2017.
  6. Burghardt, M., Lamm, L., Lechler, D., Schneider, M. & Semmelmann, T. (2016). Tool based Identification of Melodic Patterns in MusicXML Documents. In Book of Abstracts of the International Digital Humanities Conference (DH).
  7. Burghardt, M., Lamm, L., Lechler, D., Schneider, M. & Semmelmann, T. (2015). MusicXML Analyzer. Ein Analysewerkzeug für die computergestützte Identifikation von Melodie-Patterns. In Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshops (HiER) (S. 29–42).
  8. Meier, F., Bazo, A., Burghardt, M. & Wolff, C. (2015). A Crowdsourced Encoding Approach for Handwritten Sheet Music. In J. Roland, Perry; Kepper (Hg.), Music Encoding Conference Proceedings 2013 and 2014 (S. 127–130).

3. Drametrics - Quantitative Analysis of Drama

Drama is an ideal literary form to apply quantitative methods and digital tools, as they can be used to analyze its unique structural features (acts, scenes, characters, speeches).

Talks

  1. Burghardt, M. (2019). „Shall I Compare Thee to a Million Books?“ Challenges for the Computational Detection of Shakespearean Intertextuality. Vortrag im Rahmen der 9. European Summer University for Culture and Technology, Leipzig.
  2. Schmidt, T. & Burghardt (2018). Toward a Tool for Sentiment Analysis for German Historic Plays. COMHUM 2018, Lausanne.
  3. Burghardt, M. (2015). Description of Dramatic Texts for Quantitative Analysis. Im Rahmen des Pre-conference Workshop bei der ISI 2015, Zadar.
  4. Burghardt, M. & Wilhelm, T. (2015). Interaktive Analyse und Visualisierung von Dramen. Im Rahmen des Workshops “Computer-based analysis of drama and its uses for literary criticism and historiography”, Bayerische Akademie der Wissenschaften, München

Publications

  1. Schmidt, T., Burghardt, M., Dennerlein, K. & Wolff, C. (2019). Katharsis – A Tool for Computational Drametrics. In Book of Abstracts, DH 2019.
  2. Schmidt, T., Burghardt, M. & Wolff, C. (2019). Towards Multimodal Sentiment Analysis of Historic Plays: A Case Study with Text and Audio for Lessing’s Emilia Galotti. Proceedings of the DHN (DH in the Nordic Countries) Conference, Copenhagen.
  3. Schmidt, T. & Burghardt, M. (2018). An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing. Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 139-149). Santa Fe, New Mexico: Association for Computational Linguistics.
  4. Schmidt, T., Burghardt, M. & Dennerlein, K. (2018). Sentiment Annotation of Historic German Plays: An Empirical Study on Annotation Behavior. Sandra Kübler, Heike Zinsmeister (eds.), Proceedings of the Workshop on Annotation in Digital Humanities (annDH 2018) (pp. 47-52). Sofia, Bulgaria.
  5. Schmidt, T. & Burghardt, M. (2018). Toward a Tool for Sentiment Analysis for German Historic Plays. In: Piotrowski, M. (ed.), COMHUM 2018: Book of Abstracts for the Workshop on Computational Methods in the Humanities 2018 (pp. 46-48). Lausanne, Switzerland: Laboratoire laussannois d’informatique et statistique textuelle.
  6. Schmidt, T., Burghardt, M. & Wolff, C. (2018). Herausforderungen für Sentiment Analysis-Verfahren bei literarischen Texten. In: Burghardt, M. & Müller-Birn, C. (Hrsg.), INF-DH-2018. Bonn: Gesellschaft für Informatik e.V.
  7. Schmidt, T., Burghardt, M. & Dennerlein, K. (2018). “Kann man denn auch nicht lachend sehr ernsthaft sein?” – Zum Einsatz von Sentiment Analyse-Verfahren für die quantitative Untersuchung von Lessings Dramen. In Book of Abstracts, DHd 2018.
  8. Wilhelm, T., Burghardt, M. & Wolff, C. (2013). “To See or Not to See” - An Interactive Tool for the Visualization and Analysis of Shakespeare Plays. In Tagungsband der Konferenz „Kultur und Informatik“: Visual Worlds & Interactive Spaces (S. 175–185).

4. Social Media Analytics

Facebook, Twitter and Twitch.tv are just some of the many social media platforms that can be found on the Internet. The user-generated content that can be obtained from these platforms can be used to investigate digital culture from a text-centric perspective.

Talks

  1. Burghardt, M. (2017). Dialektsprache mit Hilfe von Facebook erforschen. Im Rahmen der TEDx-Reihe, OTH Regensburg. Video online verfügbar unter: https://www.youtube.com/watch?v=ciWFd-1O4KU

Publications

  1. Creating a Lexicon of Bavarian Dialect by Means of Facebook Language Data and Crowdsourcing. In Proceedings of the 10th edition of the Language Resources and Evaluation Conference (LREC).
  2. Burghardt, M., Karsten, H., Pflamminger, M. & Wolff, C. (2013). Twitter als interaktive Erweiterung des Mediums Fernsehen: Inhaltliche Analyse von Tatort-Tweets. In Workshop Proceedings of the GSCL 2013.
  3. Glücker, H., Burghardt, M. & Wolff, C. (2014). Sentilyzer – A Mashup Application for the Sentiment Analysis of Facebook Pages. In Workshop proceedings of the 12th edition of the KONVENS conference (S. 58–61).
  4. Burghardt, M. (2015). Tools for the Analysis and Visualization of Twitter Language Data. 10Plus1 Journal, Special Issue on “Media Linguistics,” 1(1).

5. Crowdsourcing

Transcribing and annotating data from the humanities can be a laborious task – we use crowdsourcing in a number of different scenarios to facilitate this task.

Publications

  1. Burghardt, M. & Spanner, S. (2017). Allegro: User-centered Design of a Tool for the Crowdsourced Transcription of Handwritten Music Scores. Proceedings of the DATeCH (Digital Access to Textual Cultural Heritage) conference. ACM.
  2. Burghardt, M., Hertlein, F., Hinterleitner, B., Lehenmeier, C. & Spröd, T. (2015). A Crowdsourced Approach for the Documentation and Transcription of Graffiti in Public Restrooms. In Proceedings of the 14th International Symposium of Information Science (ISI 2015).
  3. Burghardt, M., Schneider, P., Bogatzki, C. & Wolff, C. (2015). StreetartFinder – Eine Datenbank zur Dokumentation von Kunst im urbanen Raum. In Book of Abstracts, DHd 2015.
  4. Meier, F., Bazo, A., Burghardt, M. & Wolff, C. (2015). A Crowdsourced Encoding Approach for Handwritten Sheet Music. In J. Roland, Perry; Kepper (Hg.), Music Encoding Conference Proceedings 2013 and 2014 (S. 127–130).

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