31,135 research outputs found

    Scalable distributed event detection for Twitter

    Get PDF
    Social media streams, such as Twitter, have shown themselves to be useful sources of real-time information about what is happening in the world. Automatic detection and tracking of events identified in these streams have a variety of real-world applications, e.g. identifying and automatically reporting road accidents for emergency services. However, to be useful, events need to be identified within the stream with a very low latency. This is challenging due to the high volume of posts within these social streams. In this paper, we propose a novel event detection approach that can both effectively detect events within social streams like Twitter and can scale to thousands of posts every second. Through experimentation on a large Twitter dataset, we show that our approach can process the equivalent to the full Twitter Firehose stream, while maintaining event detection accuracy and outperforming an alternative distributed event detection system

    Real Time Event Detection in Twitter

    Get PDF
    Event detection has been an important task for a long time. When it comes to Twitter, new problems are presented. Twitter data is a huge temporal data flow with much noise and various kinds of topics. Traditional sophisticated methods with a high computational complexity aren't designed to handle such data flow efficiently. In this paper, we propose a mixture Gaussian model for bursty word extraction in Twitter and then employ a novel time-dependent HDP model for new topic detection. Our model can grasp new events, the location and the time an event becomes bursty promptly and accurately. Experiments show the effectiveness of our model in real time event detection in Twitter. ? 2013 Springer-Verlag Berlin Heidelberg.EI

    Real-time traffic event detection using Twitter data

    Get PDF
    Incident detection is an important component of intelligent transport systems and plays a key role in urban traffic management and provision of traveller information services. Due to its importance, a wide number of researchers have developed different algorithms for real-time incident detection. However, the main limitation of existing techniques is that they do not work well in conditions where random factors could influence traffic flows. Twitter is a valuable source of information as its users post events as they happen or shortly after. Therefore, Twitter data have been used to predict a wide variety of real-time outcomes. This paper aims to present a methodology for a real-time traffic event detection using Twitter. Tweets are obtained through the Twitter streaming application programming interface in real time with a geolocation filter. Then, the author used natural language processing techniques to process the tweets before they are fed into a text classification algorithm that identifies if it is traffic related or not. The authors implemented their methodology in the West Midlands region in the UK and obtained an overall accuracy of 92·86%

    What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

    Full text link
    © 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

    Real-time event detection using Twitter

    Get PDF
    Twitter has become the social network of news and journalism. Monitoring what is said on Twitter is a frequent task for anyone who requires timely access to information: journalists, traders, and the emergency services have all invested heavily in monitoring Twitter in recent years. Given this, there is a need to develop systems that can automatically monitor Twitter to detect real-world events as they happen, and alert users to novel events. However, this is not an easy task due to the noise and volume of data that is produced from social media streams such as Twitter. Although a range of approaches have been developed, many are unevaluated, cannot scale past low volume streams, or can only detect specific types of event. In this thesis, we develop novel approaches to event detection, and enable the evaluation and comparison of event detection approaches by creating a large-scale test collection called Events 2012, containing 120 million tweets and with relevance judgements for over 500 events. We use existing event detection approaches and Wikipedia to generate candidate events, then use crowdsourcing to gather annotations. We propose a novel entity-based, real-time, event detection approach that we evaluate using the Events 2012 collection, and show that it outperforms existing state-of-the-art approaches to event detection whilst also being scalable. We examine and compare automated and crowdsourced evaluation methodologies for the evaluation of event detection. Finally, we propose a Newsworthiness score that is learned in real-time from heuristically labelled data. The score is able to accurately classify individual tweets as newsworthy or noise in real-time. We adapt the score for use as a feature for event detection, and find that it can easily be used to filter out noisy clusters and improve existing event detection techniques. We conclude with a summary of our research findings and answers to our research questions. We discuss some of the difficulties that remain to be solved in event detection on Twitter and propose some possible future directions for research into real-time event detection on Twitter

    A 'glocal' approach for real-time emergency event detection in Twitter

    Get PDF
    Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale
    • …
    corecore