Theme Detection in Social Media
Bibliographic Data
| ID | 23760397 |
|---|---|
| Authors | Daniel Angus (0000-0002-1412-5096, corresponding author) |
| Year | 2016 |
| Pages | 530-544 |
| Publication date | 2016-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | CHAPTER |
| Venue | The Sage Handbook of Social Media Research Methods (SOURCE_BOOK) |
| Publisher | SAGE Publications Ltd (PUBLISHER • GB) |
| DOI | 10.4135/9781473983847.n31 |
| OpenAlex | W2763184725 |
| ISBN | 9781473983847 |
| Language | EN |
| Citations received | 1 |
Visual text analytics is an emerging field that blends and extends upon information visualisation and computational linguistics. This chapter introduces a range of visual text analytic methods which are suitable for analysing thematic trends in text-based social media data. The chapter introduces the Discursis (Angus, Smith, & Wiles, 2012a; Angus, Smith, & Wiles, 2012b) and Leximancer (Smith, 2000; Smith & Humphreys, 2006) technologies, and explains how they can be used in conjunction with other software (Microsoft ExcelTM and Gephi) to generate informative visual representations of Twitter data. The chapter explores a series of visual text analytic workflows that blend the aforementioned technologies, using a Twitter corpus comprising approximately 50,000 tweets, with analyses of the dataset offered to showcase the utility of the methods for social science research.
Social media · Sociology · Theme (computing) · World Wide Web · Computational and Text Analysis Methods · Computer Science · Sentiment Analysis and Opinion Mining
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,17 |
| Citation span | 2020 - 2020 (1) |
| Citation velocity | historical |
| Highly cited | No |