Dmytro Karamshukis a Senior Data Scientist at Skyscanner. His research focuses on data mining and modeling behaviour of online users. He has previously worked on analysis of BBC iPlayer and various social media websites (Foursquare, Twitter, Pinterest, etc.). He is an active contributor to the computer networks (Infocom, ComMag, JSAC etc.) and data mining communities (KDD, ICWSM, etc.). Dmytros work has been featured in New Scientist and BBC News.
Nishanth Sastryis a Senior Lecturer at Kings College London. He holds a PhD from the University of Cambridge, UK, a Masters degree from The University of Texas at Austin, and a Bachelors degree from Bangalore University, India, all in Computer Science. He has spent several years in Industry, at Cisco Systems and at IBM (both in the Software Group and at the TJ Watson Research Center). His work in the last few years has focused on analysing large real-world datasets, funded by several grants from two different UK Research Councils (EPSRC and ESRC), as well as by the European Commission. He has given several keynotes about his work, and has frequently been featured in various TV shows and other media outlets including Nature News, New Scientist and BBC.
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Frances Shawis a Postdoctoral Researcher in Applied Ethics with the Black Dog Institute. She is a social theorist and qualitative researcher in the area of media and technology, with a background in media studies and politics. She is currently examining the ethics and politics of social media and mobile device interventions for the diagnosis and prevention of mental illness, with a particular focus on questions of data privacy and security, confidentiality, surveillance and consent, algorithmic accountability, and the allocation of moral responsibility in mHealth and eHealth solutions. Previously she was a Research Fellow at the University of Edinburgh on a research project partnered with the suicide reduction charity Samaritans UK, researching the expression of emotional distress on social media, and how trust and empathy is established in online spaces. Her primary research interests include digital ethics, social media cultures, digital methods, health cultures, digital embodiment and the self.
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Open Access funded by Economic and Social Research Council
Bridging big data and qualitative methods in the social sciences: A case study of Twitter responses to high profile deaths by suicide
With the rise of social media, a vast amount of new primary research material has become available to social scientists, but the sheer volume and variety of this make it difficult to access through the traditional approaches: close reading and nuanced interpretations of manual qualitative coding and analysis. This paper sets out to bridge the gap by developing semi-automated replacements for manual coding through a mixture of crowdsourcing and machine learning, seeded by the development of a careful manual coding scheme from a small sample of data. To show the promise of this approach, we attempt to create a nuanced categorisation of responses on Twitter to several recent high profile deaths by suicide. Through these, we show that it is possible to code automatically across a large dataset to a high degree of accuracy (71%), and discuss the broader possibilities and pitfalls of using Big Data methods for Social Science.
Julie Brownlieis senior lecturer in sociology at the University of Edinburgh. Her research and teaching interests include the sociology of emotions, relationships, digital narratives and the everyday. She is currently researching trust and empathy online as part of the ESRCs EMoTICON programme.