Celebrity profiling on twitter using sociolinguistic features notebook for PAN at CLEF 2019
Social networks have been a revolutionary scenario for celebrities because they allow them to reach a wider audience with much higher frequency than using traditional means. These platforms enable them to improve or sometimes deteriorate, their careers through the construction of closer relationship...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9190
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9190
- Palabra clave:
- Author profiling
Celebrity profiling
Computational linguistic
Natural language processing
Socio-linguistic feature
Twitter
User profiling
Classification (of information)
Computational linguistics
Decision making
Marketing
Natural language processing systems
Social networking (online)
Author profiling
Celebrity profiling
Linguistic features
Natural language processing
Twitter
User profiling
Linguistics
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | Social networks have been a revolutionary scenario for celebrities because they allow them to reach a wider audience with much higher frequency than using traditional means. These platforms enable them to improve or sometimes deteriorate, their careers through the construction of closer relationships with their fans and the acquisition of new ones. Indeed, networks have promoted the emergence of a new type of celebrities that exists only in the digital world. Being able to characterize the celebrities that are more active on social networks, such as Twitter, gives an enormous opportunity to identify what is their real level of fame, what is their relevance for an age group, or a specific gender or occupation. These facts may enrich decision making, especially in advertising and marketing. To achieve this aim, this paper presents a novel strategy for the characterization of celebrities profile on Twitter based on the generation of socio-linguistic features from their posts that serve as input to a set of classifiers. Specifically, we produced four classifiers that describe the level of fame, the gender, the birth date, and the possible occupation of a celebrity. We obtained the training and test data sets as part of our participation at PAN 2019 at CLEF. Results of each classifier are reported including the analysis of which features are more relevant, which classification techniques were more useful and which were the final precision and recall results. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). |
|---|