Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Profiling, assessing and matching personalities active in social media

Hennessy, Ciarán and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2016) Profiling, assessing and matching personalities active in social media. In: Irish Conference on Artificial Intelligence and Cognitive Science, 20-21 Sept 2016, UCD, Dublin.

Abstract
The world of social media influencers, bloggers and online “clothes horses” is a relatively new one. New-media personalities, a.k.a. “clothes horses”, are now endorsing brands, products and companies in a more subtle way than through traditional advertising. They carefully cultivate and position their personal brands with a view to persuading businesses to use them for relatively inexpensive, often local, online marketing campaigns. In the world of traditional media, companies wishing to advertise use agencies to match their brand and core values to appropriate personalities. In this new media world, businesses must go it alone. In this paper, we present a pipeline for assessing and understanding the online reach of new-media personalities. Using Twitter, our method determines whether the social media followers of a new media personality, as a group, match their perceived brand values. We do this using automatically-determined sentiment and classification of tweets from the new-media personality and his/her Twitter followers. We also look at how businesses might determine which social media personalities would be a good fit for them for a marketing campaign. Finally we look at the evolving nature of the reach and brand of a new-media personality.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Social Media
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland. SFI/12/RC/2289
ID Code:21485
Deposited On:01 Dec 2016 14:07 by Alan Smeaton . Last Modified 31 Oct 2018 11:36
Documents

Full text available as:

[thumbnail of AICS_2016_paper_59(1).pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record