Facilitating Twitter data analytics: Platform, language and functionality

Ke Tao*, Claudia Hauff, Geert Jan Houben, Fabian Abel, Guido Wachsmuth

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)


Conducting analytics over data generated by Social Web portals such as Twitter is challenging, due to the volume, variety and velocity of the data. Commonly, adhoc pipelines are used that solve a particular use case. In this paper, we generalize across a range of typical Twitter-data use cases and determine a set of common characteristics. Based on this investigation, we present our Twitter Analytical Platform (TAP), a generic platform for conducting analytical tasks with Twitter data. The platform provides a domain-specific Twitter Analysis Language (TAL) as the interface to its functionality stack. TAL includes a set of analysis tools ranging from data collection and semantic enrichment, to machine learning. With these tools, it becomes possible to create and customize analytical workflows in TAL and build applications that make use of the analytics results. We showcase the applicability of our platform by building Twinder-a search engine for Twitter streams.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781479956654
Publication statusPublished - 7 Jan 2015
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: 27 Oct 201430 Oct 2014


Conference2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/TerritoryUnited States

Cite this