An Overview of Topic Discovery in Twitter Communication through Social Media Analytics
Chinnov Andrey, Kerschke Pascal, Meske Christian, Stieglitz Stefan, Trautmann Heike
The need for automatic methods of topic discovery in the Internet grows exponentially with the amount of available textual information. Nowadays it becomes impossible to manually read even a small part of the information in order to reveal the underlying topics. Social media provide us with a great pool of user generated content, where topic discovery may be extremely useful for businesses, politicians, researchers, and other stakeholders. However, conventional topic discovery methods, which are widely used in large text corpora, face several challenges when they are applied in social media and particularly in Twitter – the most popular microblogging platform. To the best of our knowledge no comprehensive overview of these challenges and of the methods dedicated to address these challenges does exist in IS literature until now. Therefore, this paper provides an overview of these challenges, matching methods and their expected usefulness for social media analytics.