Understanding language diversity in local twitter communities

Amr Magdy, Thanaa M. Ghanem, Mashaal Musleh, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Twitter is one of the top-growing online communities in the last years. In this poster, we study the language us- Age and diversity in Twitter local communities. We identify local communities in Twitter on a country-level. For each community, we examine: (1) the language diversity, (2) the language dominance and how it differs from local to global views, (3) demographic representativeness of tweets, and (4) the spatial distribution of different cultural groups within the community. We show fruitful insights about language usage on Twitter which can be exploited in language- based applications on top of tweets, e.g., lingual analysis and disaster management. In addition, we provide an interactive tool to explore the spatial distribution of cultural groups, which provides a low-effort and high-precision localization of different cultural groups.

Original languageEnglish
Title of host publicationHT 2016 - Proceedings of the 27th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages331-332
Number of pages2
ISBN (Electronic)9781450342476
DOIs
Publication statusPublished - 10 Jul 2016
Externally publishedYes
Event27th ACM Conference on Hypertext and Social Media, HT 2016 - Halifax, Canada
Duration: 10 Jul 201613 Jul 2016

Other

Other27th ACM Conference on Hypertext and Social Media, HT 2016
CountryCanada
CityHalifax
Period10/7/1613/7/16

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software
  • Artificial Intelligence

Cite this

Magdy, A., Ghanem, T. M., Musleh, M., & Mokbel, M. (2016). Understanding language diversity in local twitter communities. In HT 2016 - Proceedings of the 27th ACM Conference on Hypertext and Social Media (pp. 331-332). Association for Computing Machinery, Inc. https://doi.org/10.1145/2914586.2914612