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Facilitating social media research in social sciences


Project description

Our project - Chorus Twitter - aims to facilitate social media research in social science (and other non-technical disciplines) by merging new and existing methods from the computer sciences with the requirements and methodologies of the social sciences. The Chorus initiative began in 2011, having its origins in two projects that were being undertaken at Brunel University – MATCH, a research programme investigating wide ranging issues around medical device manufacture, and FoodRisC (www.foodrisc.org), a European initiative directed towards improving risk communication around food issues. More recently, we have adapted our software to investigate the process of online radicalisation.

Chorus comprises two main tools: Tweetcatcher, for retrieving data from the Twitter API, and Tweetvis, for analysing and visualising the semantic and temporal properties of this data. Chorus tools have been used extensively for research and teaching both at Brunel and many other organisations across the UK and the rest of the world. For instance, here at Brunel, undergraduate students have successfully used Tweetcatcher as part of an introduction to data analysis. More broadly, Chorus has been used by thousands of students and professionals worldwide who require free, easy, non-programmatic access to social media data. For instance, we know of over 47 publications that  describe or use Chorus in some way.

We continue to develop the code-base underpinning Chorus and are planning to release new tools and publications over the coming years. Current areas of development focus on improving the scope, flexibility and accessibility of retrieval and processing functionality, including access to new media resources and algorithms for detecting, profiling and tracking online communities.

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  • Beretta, Valentina, Maccagnola, D., Cribbin, T., & Messina, E. (2015). An interactive method for inferring demographic attributes in Twitter. Proceedings of the 26th ACM Conference on Hypertext and Social Media, 113–122. http://dx.doi.org/10.1145/2700171.2791031

  • Brooker, P., Barnett, J., Cribbin, T., Lang, A., & Martin, J. (2014). User-driven data capture: Locating and analysing Twitter conversation about cystic fibrosis without keywords. In SAGE Research Methods Cases. Retrieved from http://dx.doi.org/10.4135/978144627305014526813

  • Brooker, P., Barnett, J., Cribbin, T., & Sharma, S. (2016). Have we even solved the first ‘big data challenge?’ Practical issues concerning data collection and visual representation for social media analytics. In H. Snee, C. Hine, Y. Morey, S. Roberts, & H. Watson (Eds.), Digital Methods for Social Science: An Interdisciplinary Guide to Research Innovation. Retrieved from http://www.palgrave.com/page/detail/Digital-Methods-for-Social-Science/?sf1=barcode&st1=9781137453655

  • Brooker, Phillip, Barnett, J., & Cribbin, T. (2016). Doing social media analytics. Big Data & Society, 3(2), 1–12. https://doi.org/10.1177/2053951716658060