EveSense: What Can You Sense from Twitter?
Social media has become a useful source for detecting real-life events. This paper presents an event detection application EveSense. It detects real-life events and related trending topics from the Twitter stream and allows users to find interesting events that have recently occurred. It uses a novel Dynamic Heartbeat Graph (DHG) approach, which efficiently extracts distinguishing features and performs better than the existing event detection methods. We tested and evaluated the application on three case studies, including a sports event (FA cup Final) and two political events (Super Tuesday and US Election).
1 Introduction and Motivation
2 System Design and Evaluation
The goal of this paper is to describe how the EveSense processes data and produces event descriptions from the Twitter stream. Event detection is performed using an unsupervised graph-based approach devised in our previous studies1.
Interactive Unit: consists of Event Detector and User Interface (UI) modules. The event detector module uses a binary classifier to label the event candidate graphs. Topic extractor combines top trending topics from the candidate graphs, and then a ranked list is generated. All the results are presented and visualized on the user interface. UI is one of the major modules controlling the services of all other modules and provides support for customizing different parameter settings corresponding to crawler, and pre-processor. Some of the parameters (Fig. 3E) associated with the DHG approach that allows various modes of building the graph’s structure in which temporal aggregation (batch) of tweets and relationships between words are the most important among the others. Events concerning the type, user participation, and region, varies in popularity and life span hence needed to adjust some of the tuning parameters. The UI can customize the usage and fusion of feature set to observe the optimum results.
Visualizer: The visualization functionality of EveSense produces three temporal signals based (Fig. 3A) on heartbeat score, network size, and user participation. It improves the information seeking and observation process. The UI allows users to analyze different time-slots to observe the event(s) in that particular time interval by generating an interactive word cloud (Fig. 3B) of ranked topics.
Searching Micro-documents: Multiple words from the cloud can be selected to generate a query to retrieve the actual tweets from the corpus matching with the search term(s) (Fig. 3C). The system uses .Net version of a well-known Lucence Library V2.3 to generate a full-text index and facilitate search engine operation within the context of system design and ranking the retrieved tweets (Fig. 3D) with ten unique color codes. Each color covers 10% of the matched tweets facilitating the process of user’s information needs.
Performance: Our study shows that the DHG approach, which is the foundation of the event detection method in the system design, is superior in terms of both execution time and accuracy. The detail performance comparison is discussed in .
3 Contributions and Conclusion
Event related trending topics ignored in mainstream media
Top 10 detected keywords
Relevant Tweets from the Corpus
newt, gas, president, price, gingrich, king, saudi, speech, make, bow
Gingrich announces 49 step plan to stop Americans from bowing to Saudi King
I want to have an energy policy in America so no president will ever again bow to a Saudi king
drogba, ankle, torr, groin, physio, hurt, time, wast, ball, injury, stop
He’s hurt his ankle, but he’s just having a quick wank while he waits
Drogba has started the fake injury time wasting
The EveSense also visualize the topics to depict the theme of different events. Generally, the system is useful for individuals who are interested in discovering interesting events from the Twitter stream. It can be helpful for News agencies trying to shape the news story around significant real-life events. Additionally, it can effectively contribute to help state institutions for efficient decision-making and policy-making after analyzing recent local events of interest such as traffic jams, security threats, and epidemics in a specific region. It is an open-source application4 developed in .Net framework and is fairly easy to use.
- 3.Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: TwitInfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2011, pp. 227–236. ACM, New York (2011)Google Scholar
- 4.Petrovic, S., Osborne, M., McCreadie, R., Macdonald, C., Ounis, I., Shrimpton, L.: Can Twitter replace newswire for breaking news? In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, USA, pp. 713–716. AAAI Press, July 2013Google Scholar
- 9.Saeed, Z., Abbasi, R.A., Sadaf, A., Razzak, M.I., Xu, G.: Text stream to temporal network - a dynamic heartbeat graph to detect emerging events on Twitter. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10938, pp. 534–545. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93037-4_42CrossRefGoogle Scholar