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Text mining and social media: when quantitative meets qualitative, and software meets humans
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ampofo, Lawrence Collister, Simon O'Loughlin, Ben Chadwick, Andrew |
| Copyright Year | 2015 |
| Abstract | The ongoing production of staggeringly huge volumes of digital data is a ubiquitous part of life in the early twenty-first century. A large proportion of this data is text. This development has serious implications for almost all scholarly endeavour. It is now possible for researchers from a wide range of disciplines to use text mining techniques and software tools in their daily practice. In our own field of political communication, the prospect of cheap access to what, how, and to whom very large numbers of citizens communicate in social media environments provides opportunities that are too good to miss as we seek to understand how and why citizens think and feel the way they do about policies, political organizations, and political events. But what are the methods and tools on offer, how should they best be used, and what sorts of ethical issues are raised by their use? |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://apo.org.au/sites/default/files/resource-files/2013/10/apo-nid36023-1345856.pdf |
| Alternate Webpage(s) | http://static1.1.sqspcdn.com/static/f/127762/23648774/1408114537160/RHUL_NPCU_Working_Paper_Ampofo_Collister_OLoughlin_Chadwick_Text_Mining_Social_Media.pdf?token=WGvwEGcYmPwyzOjE9JzgoEQOJgg%3D |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
