Audience Segmentation in Social Media

Verena Henrich, Alexander Lang


Abstract
Understanding the social media audience is becoming increasingly important for social media analysis. This paper presents an approach that detects various audience attributes, including author location, demographics, behavior and interests. It works both for a variety of social media sources and for multiple languages. The approach has been implemented within IBM Watson Analytics for Social Media and creates author profiles for more than 300 different analysis domains every day.
Anthology ID:
E17-3014
Volume:
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
André Martins, Anselmo Peñas
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
53–56
Language:
URL:
https://aclanthology.org/E17-3014
DOI:
Bibkey:
Cite (ACL):
Verena Henrich and Alexander Lang. 2017. Audience Segmentation in Social Media. In Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 53–56, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Audience Segmentation in Social Media (Henrich & Lang, EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-3014.pdf