Dr Shauna Concannon

I am a postdoctoral researcher based in CRASSH.

Contact information

CRASSH, Alison Richards Building
Cambridge
United Kingdom

Research interests

Current projects:

- Evaluating bias in textual data; gender bias in machine translation
- Perspectives on agency and control in interactions with menstrual tracking data

Past projects:

- Data-driven films for supporting public engagement with open datasets (human-data interaction, experimental design, open data; open government)

Shauna Concannon, Natasha Rajan, Parthiv Shah, Davy Smith, Marian Ursu and Jon Hook. Brooke Leave Home : Designing a Personalized Film to Support Public Engagement with Open Data. Proceedings of the ACM CHI 2020 Conference on Human Factors in Computing Systems, Honolulu, April 2020.

- Combining user generated content with Index of Multiple Deprivation data to assess how attitudes to on breastfeeding in public spaces vary according to location (critical GIS; hybrid methods; text analysis)

Shauna Concannon, Madeline Balaam, Emma Simpson and Rob Comber. Applying Computational Analysis to Textual Data from the Wild: a Feminist Perspective. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, April 2018.

- Analysing large textual datasets -expressions of authority, certainty and culpability in NHS incident reporting

Chrystie Myketiak, Shauna Concannon and Paul Curzon. Narrative Perspective, Person References, and Evidentiality in Clinical Incident Reports. Journal of Pragmatics 117 (2017): 139-154.

- Extracting insights from social media data (patient opinion, cultural preferences of arts audiences)

Shauna Concannon and Matthew Purver. Inferring Cultural Preference of Arts Audiences Through Twitter Data. In Digital Intelligence, Nantes, September 2014

- Argument mining, extracting insights from interactional data/dialogue

Shauna Concannon, Pat Healey and Matthew Purver. How Natural is Argument in Natural dialogue? In The 16th Workshop on Computational Models of Natural Argument, at IJCAI 2016, New York, July 2016.

Themes

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science.

  • Supports and connects the growing data science research community 
  • Builds research capacity in data science to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Acts as a gateway for external organisations 

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