%0 Journal Article %1 COIN:COIN12017 %A Atefeh, Farzindar %A Khreich, Wael %D 2015 %J Computational Intelligence %K imported %N 1 %P 132--164 %R 10.1111/coin.12017 %T A Survey of Techniques for Event Detection in Twitter %U http://dx.doi.org/10.1111/coin.12017 %V 31 %X Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real-world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.

@article{COIN:COIN12017, abstract = {Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real-world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.}, added-at = {2016-11-28T10:15:50.000+0100}, author = {Atefeh, Farzindar and Khreich, Wael}, biburl = {https://www.bibsonomy.org/bibtex/2016ac49ad5debf332f5f509166db8be7/kde-alumni}, doi = {10.1111/coin.12017}, interhash = {ae6a81fde137959c198d482ce642e803}, intrahash = {016ac49ad5debf332f5f509166db8be7}, issn = {1467-8640}, journal = {Computational Intelligence}, keywords = {imported}, number = 1, pages = {132--164}, timestamp = {2016-11-28T10:15:50.000+0100}, title = {A Survey of Techniques for Event Detection in Twitter}, url = {http://dx.doi.org/10.1111/coin.12017}, volume = 31, year = 2015 }