1. Introduction

Social media platforms such as Facebook, Twitter, and Instagram allow their users to easily connect and share information. The unprecedented data generated by millions of users from all around the world make social media ideal places of finding what is happening in the wider world beyond direct personal experience. As a microblog site, Twitter enables its users to post

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

instantly what is happening in their location in 140-character messages, or tweets. Twitter is an information system that provides a real-time reflection of its users. As a consequence, Twitter serves as a rich source for exploring what is attracting users' attention and what is happening around the world. For example, for news and communications in time of a disaster, social media users use Twitter to tweet and post text, images, and video through their smartphones and tablets. As a result, Twitter becomes a good source for detection of events such as disasters [1].

that no one user could be in multiple events at the given time, demanding that the image was

Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets

http://dx.doi.org/10.5772/intechopen.77178

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This chapter introduces a novel algorithm to detect a major event such as a wildfire through mining social media Twitter. In developing an efficient event detection algorithm, our considerations are: For Twitter users, it is much easier than ever before to post about natural disaster like wildfire, by posting different kinds of multimedia like pictures, rather than just typing a message. Using both image and text can improve disaster management than using image only or text only. Furthermore, Twitter has been used as a source for obtaining information about wildfires, specifically when landlines and mobile phone lines are damaged. Therefore, we propose to use visual information as well as textual information to improve the performance of automatic even detection. The algorithm starts with monitoring a Twitter stream to pick up tweets having texts and images. Secondly, it preprocesses the Twitter data to eliminate unwanted data and transform unstructured data into structured data. Thirdly, it extracts features from the text and image. Fourthly, a multiple kernel learning is applied to the features

The chapter is organized as follows. After this section, Section 2 describes the proposed event detection method, which consists of Twitter data collection, data preprocessing, feature extraction, multiple kernel learning fusion, and event classification. Section 3 gives experiment

The proposed automatic event detection method includes five steps, including Twitter data collection, data preprocessing, features extraction, multimedia data fusion, and final event detection. The block diagram of the proposed method is shown in Figure 1. The following

Data about specific events have been obtained through the use of a Twitter application program or through Twitter partner sites. This study characterizes public responses on Twitter for different kinds of events such as storm, earthquake, wildfire, terror attacks, and other events. Two recent extreme events which happened in the last 4 years were used as case studies,

to fuse the multimedia data. Finally, a decision on event detection is made.

subsections explain the details of these five steps of the proposed algorithm.

design, results, and discussion. Section 4 presents conclusion.

including Brisbane hailstorm and California wildfire.

Figure 1. The block diagram of the proposed method of event detection from Twitter.

separated by user at the beginning.

2. The proposed algorithm

2.1. Twitter data collection

An event is the basis on which people form and recall memories. Events are a natural way to refer to any observable occurrence that groups persons, places, times, and activities together. They are useful because they help us make sense of the world around us, helping to recollect real-world experiences, explaining phenomena that we observe, or assisting us in predicting future events. Social events are the events that are attended by people and are represented by multimedia content shared online. Instances of such events are concerts, disasters, sports events, public celebrations, or protests. Twitter platform forms a rich site for news, events, and information mining. It allows the posting of images and videos to accompany tweets produced by users of the site. As a result, the site contains multimedia content which can be mined using complicated algorithms. However, due to the huge burst in information, event detection in Twitter is a complicated task that requires a lot of skill and expertise in data mining. Here, an event detection is a data mining task aiming to identify the event in a media collection. To enhance the process of event detection, an automatic algorithm needs be developed to mine multimedia information.

Many approaches have been proposed for event detection [2–4]. For event detection using Twitter data, there are different ways to detect event, including using part of speech technique [5], hidden Markov model (HMM) [6], and term frequency and inverse document frequency (TF-IDF), and part-of-speech (POS) tagging and parsing. Alqhtani et al. [7] introduced a data fusion approach in multimedia data for earthquake detection in Twitter by using kernel fusion. It had achieved a high detection accuracy of 0.94, comparing to accuracy of 0.89 with texts only, and accuracy of 0.83 with images only. Sakaki et al. [8] showed that mining of relevant tweets can be used to detect earthquake events and predict the earthquake center in real time by using TF-IDF. In the process of event detection, the method utilized TF-IDF to eliminate redundant information or keywords. It provided a way of real-time interaction for earthquakes in Twitter. It developed a classifier based on several features including keywords, the number of words and the context, location and time of the words. It used a probabilistic spatiotemporal model to detect the location of the earthquake happened in Japan. Yardi and Boyd [9] used keyword search to present the role of stream news in spreading local information from Twitter for two accidents including a shooting and a building collapse. Ozdikis et al. [10] discussed an event detection method for various topics in Twitter using semantic similarities between hashtags based on clustering. Zhang et al. [11] proposed an event detection from online microblogging stream. It combined the normalized term frequency and user's social relation to weight words. Although many approaches have been proposed for event detection using Twitter data, most of them used no images but only textual analysis of tweet texts. With the cases of using images, restrictions had been applied. For example, Nguyen et al. [12] used textual features and image features for event detection. However, they focused on the principle that no one user could be in multiple events at the given time, demanding that the image was separated by user at the beginning.

This chapter introduces a novel algorithm to detect a major event such as a wildfire through mining social media Twitter. In developing an efficient event detection algorithm, our considerations are: For Twitter users, it is much easier than ever before to post about natural disaster like wildfire, by posting different kinds of multimedia like pictures, rather than just typing a message. Using both image and text can improve disaster management than using image only or text only. Furthermore, Twitter has been used as a source for obtaining information about wildfires, specifically when landlines and mobile phone lines are damaged. Therefore, we propose to use visual information as well as textual information to improve the performance of automatic even detection. The algorithm starts with monitoring a Twitter stream to pick up tweets having texts and images. Secondly, it preprocesses the Twitter data to eliminate unwanted data and transform unstructured data into structured data. Thirdly, it extracts features from the text and image. Fourthly, a multiple kernel learning is applied to the features to fuse the multimedia data. Finally, a decision on event detection is made.

The chapter is organized as follows. After this section, Section 2 describes the proposed event detection method, which consists of Twitter data collection, data preprocessing, feature extraction, multiple kernel learning fusion, and event classification. Section 3 gives experiment design, results, and discussion. Section 4 presents conclusion.
