%0 Conference Paper %1 Tran:2014:ADT:2615569.2615655 %A Tran, Tuan %A Georgescu, Mihai %A Zhu, Xiaofei %A Kanhabua, Nattiya %B Proceedings of the 2014 ACM Conference on Web Science %C New York, NY, USA %D 2014 %I ACM %K alexandria %P 251--252 %R 10.1145/2615569.2615655 %T Analysing the Duration of Trending Topics in Twitter Using Wikipedia %U http://doi.acm.org/10.1145/2615569.2615655 %X The analysis of trending topics in Twitter is a goldmine for a variety of studies and applications. However, the contents of topics vary greatly from daily routines to major public events, enduring from a few hours to weeks or months. It is thus helpful to distinguish trending topics related to real-world events with those originated within virtual communities. In this paper, we analyse trending topics in Twitter using Wikipedia as reference for studying the provenance of trending topics. We show that among different factors, the duration of a trending topic characterizes exogenous Twitter trending topics better than endogenous ones. %@ 978-1-4503-2622-3

@inproceedings{Tran:2014:ADT:2615569.2615655, abstract = {The analysis of trending topics in Twitter is a goldmine for a variety of studies and applications. However, the contents of topics vary greatly from daily routines to major public events, enduring from a few hours to weeks or months. It is thus helpful to distinguish trending topics related to real-world events with those originated within virtual communities. In this paper, we analyse trending topics in Twitter using Wikipedia as reference for studying the provenance of trending topics. We show that among different factors, the duration of a trending topic characterizes exogenous Twitter trending topics better than endogenous ones.}, acmid = {2615655}, added-at = {2016-10-18T14:59:49.000+0200}, address = {New York, NY, USA}, author = {Tran, Tuan and Georgescu, Mihai and Zhu, Xiaofei and Kanhabua, Nattiya}, biburl = {https://www.bibsonomy.org/bibtex/211cd79621f5fadead7e810c11cc876b3/alexandriaproj}, booktitle = {Proceedings of the 2014 ACM Conference on Web Science}, doi = {10.1145/2615569.2615655}, interhash = {4636f99939366635e7ddabf6ec34ee3e}, intrahash = {11cd79621f5fadead7e810c11cc876b3}, isbn = {978-1-4503-2622-3}, keywords = {alexandria}, location = {Bloomington, Indiana, USA}, numpages = {2}, pages = {251--252}, publisher = {ACM}, series = {WebSci '14}, timestamp = {2016-10-18T14:59:49.000+0200}, title = {Analysing the Duration of Trending Topics in Twitter Using Wikipedia}, url = {http://doi.acm.org/10.1145/2615569.2615655}, year = 2014 }