Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/3034
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Millham, Richard | - |
dc.date.accessioned | 2018-06-06T07:49:54Z | - |
dc.date.available | 2018-06-06T07:49:54Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | MIllham, R. 2016. Theme evolution and structure in Twitter : a case study of South African student protests of 2015. 2016 IEEE International Conference on Big Data Analysis (ICBDA). :1-4. | en_US |
dc.identifier.isbn | 978-1-4673-9591-5 (online) | - |
dc.identifier.isbn | 978-1-4673-9590-8 (print) | - |
dc.identifier.isbn | 978-1-4673-9589-2 (CD | - |
dc.identifier.isbn | 978-1-4673-9592-2 (PoD) | - |
dc.identifier.uri | http://hdl.handle.net/10321/3034 | - |
dc.description.abstract | Social media, based on human interactions, often has constantly changing foci, or themes, within their interactions. These themes, frequently used to categorize information within this social media, often evolve dependent on time, domain, and event contexts. Using a case study of South African student protests during a short but eventful time period in 2015, this paper analyses numerous tweets made to selected hashtags (one national and two local) in order to determine relevant themes within these tweets and to determine how these themes evolved, both at the national and local level, given their context. It was discovered that, as certain events unfolded, certain themes varied in prominence and locally-based hashtags converged into nationally-based hashtags reflecting a change in the nature of the protests. | en_US |
dc.format.extent | 4 p | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Theme evolution | en_US |
dc.subject | Tweets | en_US |
dc.subject | Student protests | en_US |
dc.subject | Social Media | en_US |
dc.title | Theme evolution and structure in Twitter : a case study of South African student protests of 2015 | en_US |
dc.type | Presentation | en_US |
dc.publisher.uri | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7509828 | en_US |
dc.dut-rims.pubnum | DUT-005339 | en_US |
dc.description.availability | Copyright: 2016. IEEE. Due to copyright restrictions, only the abstract is available. For access to the full text item, please consult the publisher's website. The definitive version of the work is published in 2016 IEEE International Conference on Big Data Analysis (ICBDA). 4 Pages. https://ieeexplore.ieee.org/document/7509828 | en_US |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Presentation | - |
item.languageiso639-1 | en | - |
Appears in Collections: | Research Publications (Accounting and Informatics) |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.