SportsBuzzer: Detecting Events at Real Time in Twitter using Incremental Clustering

  • Jeyakumar Kannan PG and Research Department of Computer Science Jamal Mohamed College Autonomous Tiruchirappalli 620020 INDIA
  • AR. Mohamed Shanavas Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India
  • Sridhar Swaminathan Department of Computer Science Engineering, Bennett University, Greater Noida, India;
Keywords: Social media, Twitter, Sports event detection, locality sensitive hashing, incremental clustering,


In the recent past, twitter users are highly regarded as social sensors who can report events and Twitter has been widely used to detect social and physical events such as earthquakes and traffic jam. Real time event detection in Twitter is the process of detecting events at real time from live tweet stream as soon as an event has happened. Real time event detection from sports tweets, such as Cricket is an interesting, yet a complex problem. Because, an event detection system needs to collect live sports tweets and should rapidly detect key events such as boundary and catch at real-time when the game is ongoing. In this paper, a novel framework is proposed for detecting key events at real time from live tweets of the Cricket sports domain. Feature vectors of live tweets are created using TF-IDF representation and tweet clusters are discovered using Locality Sensitive Hashing (LSH) where the post rate of each cluster based on the volume of tweets is computed. If the post rate is above the predefined threshold, then a key event recognized from that cluster using our domain specific event lexicon for Cricket sports. The predefined threshold helps to filter out small spikes in the tweets volume. The proposed real-time event detection algorithm is extensively evaluated on 2017 IPL T20 Cricket live tweets using ROC evaluation measure. The experimental results on the performance of the proposed approach show that the LSH approach detects sports events with nearly 90% true positive rate and around 10% false positive rate. The results have also demonstrated the influence of different parameters on the accuracy of the event detection.


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