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

Event identification in social media using classification-clustering framework

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 smallscale incidents, using streamed data is a non-trivial task, due to the heterogeneity, the scalability and the varied quality of the data as well as the presence of noise and irrelevant information. However, it would be of high value to public safety organisations such as local police, who need to respond accordingly. To address these challenges we present an end-to-end integrated event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering enables events to be detected, especially “disruptive events" - incidents that threaten social safety and security, or that 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 large-scale, realworld datasets from Twitter and Flickr. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We show that our system can perform as well as terrestrial sources, such as police reports, traditional surveillance, and emergency calls, even better than local police intelligence in most cases. The framework developed in this thesis provides a scalable, online solution, to handle the high volume of social media documents in different languages including English, Arabic, Eastern languages such as Chinese, and many Latin languages. Moreover, event detection is a concept that is crucial to the assurance of public safety surrounding real-world events. Decision makers use information from a range of terrestrial and online sources to help inform decisions that enable them to develop policies and react appropriately to events as they unfold. 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 thesis 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

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

A combined classification-clustering framework for identifying disruptive events

Twitter is a popular micro-blogging web application serving hundreds of millions of users. Users publish short messages to communicate with friends and families, express their opinions and broadcast news and information about a variety of topics all in real-time. User-generated content can be utilized as a rich source of real-world event identification as well as extract useful knowledge about disruptive events for a given region. In this paper, we propose a novel detection framework for identifying real-time events, including a main event and associated disruptive events, from Twitter data. Theapproach is based on five steps:data collection, pre-processing,classification, online clustering and summarization. We use a Naïve Bayes classification model and an Online Clustering method to validate our model on a major real-world event (Formula 1 Abu Dhabi Grand Prix 2013)

A classifier to detect informational vs. non-informational heart attack tweets

Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively

Review of Progress and Prospects in Research on Enzymatic and Non- Enzymatic Biofuel Cells; Specific Emphasis on 2D Nanomaterials

Energy generation from renewable sources and effective management are two critical challenges for sustainable development. Biofuel Cells (BFCs) provide an elegant solution by combining these two tasks. BFCs are defined by the catalyst used in the fuel cell and can directly generate electricity from biological substances. Various nontoxic chemical fuels, such as glucose, lactate, urate, alcohol, amines, starch, and fructose, can be used in BFCs and have specific components to oxide fuels. Widely available fuel sources and moderate operational conditions make them promise in renewable energy generation, remote device power sources, etc. Enzymatic biofuel cells (EBFCs) use enzymes as a catalyst to oxidize the fuel rather than precious metals. The shortcoming of the EBFCs system leads to integrated miniaturization issues, lower power density, poor operational stability, lower voltage output, lower energy density, inadequate durability, instability in the long-term application, and incomplete fuel oxidation. This necessitates the development of non-enzymatic biofuel cells (NEBFCs). The review paper extensively studies NEBFCs and its various synthetic strategies and catalytic characteristics. This paper reviews the use of nanocomposites as biocatalysts in biofuel cells and the principle of biofuel cells as well as their construction elements. This review briefly presents recent technologies developed to improve the biocatalytic properties, biocompatibility, biodegradability, implantability, and mechanical flexibility of BFCs.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) under UREP grant #UREP28-052-2-020. The statements made herein are solely the responsibility of the authors

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

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