Real-time Content Identification for Events and Sub-Events from Microblogs.

PhDIn an age when people are predisposed to report real-world events through their social media accounts, many researchers value the advantages of mining such unstructured and informal data from social media. Compared with the traditional news media, online social media services, such as Twitter, can provide more comprehensive and timely information about real-world events. Existing Twitter event monitoring systems analyse partial event data and are unable to report the underlying stories or sub-events in realtime. To ll this gap, this research focuses on the automatic identi cation of content for events and sub-events through the analysis of Twitter streams in real-time. To full the need of real-time content identification for events and sub-events, this research First proposes a novel adaptive crawling model that retrieves extra event content from the Twitter Streaming API. The proposed model analyses the characteristics of hashtags and tweets collected from live Twitter streams to automate the expansion of subsequent queries. By investigating the characteristics of Twitter hashtags, this research then proposes three Keyword Adaptation Algorithms (KwAAs) which are based on the term frequency (TF-KwAA), the tra c pattern (TP-KwAA), and the text content of associated tweets (CS-KwAA) of the emerging hashtags. Based on the comparison between traditional keyword crawling and adaptive crawling with di erent KwAAs, this thesis demonstrates that the KwAAs retrieve extra event content about sub-events in real-time for both planned and unplanned events. To examine the usefulness of extra event content for the event monitoring system, a Twitter event monitoring solution is proposed. This \Detection of Sub-events by Twit- ter Real-time Monitoring (DSTReaM)" framework concurrently runs multiple instances of a statistical-based event detection algorithm over different stream components. By evaluating the detection performance using detection accuracy and event entropy, this research demonstrates that better event detection can be achieved with a broader coverage of event content.School of Electronic Engineering Computer Science (EECS), Queen Mary University of London (QMUL) China Scholarship Council (CSC)

Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions

The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to detect localised spikes in Twitter activity corresponding to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which fully describe the event and by linking to highly relevant news articles. We apply our methods to outbreaks of illness and events strongly affecting sentiment. In both case studies we are able to detect events verifiable by third party sources and produce high quality summaries

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events

Towards reproducible research of event detection techniques for Twitter

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