Automatic summarization of real world events using Twitter

Microblogging sites, such as Twitter, have become increasingly popular in recent years for reporting details of real world events via the Web. Smartphone apps enable people to communicate with a global audience to express their opinion and commentate on ongoing situations - often while geographically proximal to the event. Due to the heterogeneity and scale of the data and the fact that some messages are more salient than others for the purposes of understanding any risk to human safety and managing any disruption caused by events, automatic summarization of event-related microblogs is a non-trivial and important problem. In this paper we tackle the task of automatic summarization of Twitter posts, and present three methods that produce summaries by selecting the most representative posts from real-world tweet-event clusters. To evaluate our approaches, we compare them to the state-of-the-art summarization systems and human generated summaries. Our results show that our proposed methods outperform all the other summarization systems for English and non-English corpora

Sensing real-world events using Arabic Twitter posts

In recent years, there has been increased interest in event detection using data posted to social media sites. Automatically transforming user-generated content into information relating to events is a challenging task due to the short informal language used within the content and the variety oftopics discussed on social media. Recent advances in detecting real-world events in English and other languages havebeen published. However, the detection of events in the Arabic language has been limited to date. To address this task, wepresent an end-to-end event detection framework which comprises six main components: data collection, pre-processing, classification, feature selection, topic clustering and summarization. Large-scale experiments over millions of Arabic Twitter messages show the effectiveness of our approach for detecting real-world event content from Twitter posts

Can we predict a riot? Disruptive event detection using Twitter

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to “real world” events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale “disruptive events,” smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases

Identifying disruptive events from social media to enhance situational awareness

Decision makers use information from a range of terrestrial and online sources to help underpin the processes through which they develop policies and react to events as they unfold. One such source of online information is social media. Twitter, as a form of social media, is a popular micro-blogging Web application serving hundreds of millions of users. User-generated content can be exploited as a rich source of information for identifying 'real-world' disruptive events. In this paper, we present an in-depth comparison of three types of features that could be useful for identifying disruptive events: temporal, spatial and textual. We make several interesting observations: first, disruptive events are identifiable regardless of the “influence of the user” discussing them, and over a variety of topics. Second, temporal features are the best event identifiers and hence should not be disregarded or ignored. Third, a combination of optimum textual features with temporal and spatial features achieves best performance in the event detection task. We believe that these findings provide new insights for gathering information around real-world events as well as a useful resource for improving situational awareness and decision support

Enhancing situational awareness and communication during flood crisis events using social media framework: the case of Bosnia and Herzegovina

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe current thesis approaches the issue of using social media for the case of Bosnia and Herzegovina for the recurring flood crisis events. The current status of using and interacting with social media , through studying the literature of the previous facts and results towards using social media by governmental and public representatives have been investigated. Different experiences were found related to countries that are experiencing flood events and their uses of social media. On the other hand it was found that little or no information were presented for the uses of social media for crises events in Bosnia and Herzegovina case. It was found that the reasons for not having current implementation of a solution is related to the complex governmental structure that are present in the Bosnian state government, entities of Bosnia and Herzegovina, Brčko District, cantons and regions. Further investigations were initiated to identify the current uses, needs and obstacles towards the use of social media tools and services as a medium for increasing situational awareness and communication in Bosnia and Herzegovina. The considerations of the previous investigation were with respect to governmental complex structure and public needs. The results of the investigation managed to outline the current challenges with respect for each investigated sector. The outputs of the previous investigations have been used as inputs to direct and guide the system design of the proposed new system framework that is aiming for enhancing situational awareness and communication during flood crisis events using social media framework. The system design and functionalities have focused on providing sharing environment for the complex government structure and public needs with a direct focus on not distracting the current used structure and public ethnical segregations. The system framework has been tested and the reflection of governmental attitude and public results has been encouraging towards adopting this framework for future flood events in Bosnia and Herzegovina