Keywords

1 Introduction

Nowadays, social media play a very important role in the world. Social media can be used for sharing information’s, ideas, and interests among the people throughout the world. Lot of people communicated through social media by sending and receiving messages. Social media can be different types such as Twitter, news archive, multimedia, BlogPosts, web pages, Facebook, etc. Social media generate huge amount of information it will be very much useful to the people. The information can be described as something happened outside is known as event. Social media contains information about related to be event and not related to event. During a crisis, colossal information can be posted in social media. In that, determining the information related to event is challenging task on social media. Different types of social media have the different characteristics. Figure 1 describes about event detection on social media.

Fig. 1
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Event detection on different types of social media

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Whenever social event occurs, traditional media take 2 h or even days for reporting the news of a particular event, at the same time corresponding information may begin to spread promptly in the social media like Twitter. Twitter is a free social networking microblogging service which allows users for registering and post short texts (messages) on microblog called tweets. Each tweet allows 140 characters which may be text images, hyperlink, videos, etc. As per the statistics given in [1], average of 41 million users have been registered in July 2009 which was drastically increased to 317 million users, in August 2016. Whenever the user follows any another user, user is able to read the tweets and share the same tweet, called retweet. A user has the ability to read the tweet which is related to other users. In twitter, the user-generated contents are updated each and every time which will be useful to the users in the twitter [2]. During disasters, large colossal information can be generated in the microblog. At that time, it is very important to know where the disasters occur and where can be disseminated? And there is a need to know what is happening? in those locations. Extraction of the event is a challenging task. By entering pertinent word to search engine (Google, Bing, Yahoo! etc) which gives very large roll of URL which are related to some concepts of interests. At the present, researchers mainly focus on ranking of the list of documents based on the weight [3].

Users easily access the news, web, videos by leverage the search engines and video sharing websites such as Google, YouTube, Baidu and YouKu. Newswires like CNN, BBC, and CCTV also publish news videos. According to the 2014 YouTube data, for every minute, 100 h of videos are uploaded to YouTube, and each month six billion hours of videos are watched on the YouTube. These factualities describe a new challenge for the users to clench the great events available from searching video databases [4]. In general, belief of the users is initially user-generated content (UGC) and got huge popularity and then gradually dim into oblivion. Whenever analyzing more than 350k YouTube videos, it needs at least 1 year time for uploading videos to get huge popularity. These videos are called sleeping beauties. Sleeping beauties will get huge popularity after one year which is challenging a problem. Identifying such videos will not only comfort the advertisers, but also the designers of recommendation systems who search for to maximum user satisfaction [5].

Due to ubiquitous of the We-media, information can be received and published into numerous forms at anywhere at any time with leverage the Internet.The rich cross-media information contains multimodal data in multiple media and has many audiences. The large information available on different events which have great impact compare to single media information. Hence, detection of the event in cross-media is a challenging problem in the following aspects (1) It contains the different media data which has different characteristics (2) Topics are presented among the noisy web data. Detection of the event in cross-media is very helpful for the government organization and advertising agencies [6].

Today, there is huge growth in web technology, social websites have become the flexible platform for users to access the world and exchanging the ideas (opinions). However, prodigious explosion in the extensiveness of web pages from the social media has made the strenuous problem for the users to access the hot topics and web administrators to detect the web activities properly. The content of social media is less restricted and less predictable compared to others (news articles) and also it is short, noisy and sparse [7].

2 Literature Survey

First story detection (FSD) or new event detection can be defined as identifying the event which is unseen or unknown previously. In [8], the author proposed a method which is called Inverted Index for FSD, which is efficient and it is helpful for large data streams. This approach was used by the Umass FSD system. The limitation of the proposed method is that it is not scalable to unbounded streams and processing of one document will take longer time. Hence, it will not provide good performance for Twitter due to continuous tweets, it is clear as some document takes long time for processing each document than arrival of tweets. In [9], the author proposed a method called probabilistic approaches for FSD. The main advantage of this method is that it presents the data in structured way but it is more expensive and also very less suitable for processing large amount of data.

In [10], the author proposed a method for event detection in blogs called temporal random indexing. Blogs are less structured compared to newswire and it can be updated very fast. The main limitation of this method is that the events based around keywords need to specify the keywords explicitly and require lot of volume for semantic shift. It is not suitable for a large-scale event detection.

Support Vector Machine (SVM) for event detection in the Twitter during disasters is used in [11]. It detects specific types of events and it is more attractive for small sets of events with high precision. It is unfeasible for any type of events. If large number of events are there then it requires separate classifier which needs to be trained for each event and also requires labeled data.

In [12], the author proposed a hybrid method to detect the events from the noisy and fragmented tweet. In this paper, the author considered power outage as event and power outage detection as an event detection. Hybrid method is a combination of heterogeneous information network and supervised topic model. Heterogeneous information network contains both temporal and spatial information of tweets. Supervised topic model used heterogeneous information network for improving the accuracy of event detection compared to existing methods like SVM, Bayesian model, etc.

Table 1 Summaries of event detection on various social media
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A method was developed for extracting the traffic events like traffic, public transport, water supply, weather, sewage, and public safety from the tweets which are posted by the observations [17]. In general, for detecting the events, there is a need to understand the tweet stream which is a challenging task in social media. In this paper, hypothesize the problem of annotating the tweet stream as a sequence labeling problem. For the sequence labeling models, there is a need for manual labeled training data. The proposed method is a novel approach for creating the automatic training label based on the instance-level domain knowledge and it indicates locations in a city and possible event occurrences. It gives comparable performance to annotated tasks which is more advantageous for training and avoids the need of the manual labeled training process.

A novel framework is proposed in [4] to better the group associated web videos to events. First, the data preprocessing is performed to select features and tag relevance learning. Next, multiple correspondence analysis is applied to explore the correlations between terms and events with the assistance of visual information. Co-occurrence and visual near duplicate feature trajectory induced from near duplicate key frames (NDKs) are combined to calculate the similarity between NDKs and events. Finally, a probabilistic model is proposed for news web video event mining, where both visual temporal information and textual distribution information are integrated.

In [7], a method is explored in the view of similarity diffusion. The method uses clustering-like pattern across similarity cascades (SCs). SCs are chain of subgraphs generated by pruning the similarity graphs with a set of thresholds and capturing the topics with the help of the maximum cliques. At last, they discovered the real topics in effective manner from huge number of candidates with the use of topic-restricted similarity diffusion method. This method is experimented on three public datasets such as MCG-WEBV, YKS and Social Event Detection 2012 (SED2012). In this experiment, they considered only social event as web topics and ignored the geographical information. It gives better performance. The summary of the methods discussed for event detection on various social media is tabulated in Table 1.

3 Conclusion

The enormous amount of user-generated content (UGC) can be disseminated in social media. Analyzing and understanding the UGC are very helpful to the users at the time of disasters, power outage, traffic, etc. Event detection is one of the important tasks and targets at identifying real-world occurrences. However, event detection on social media must be efficient and accurate. In this paper, survey of different methods is explained based on event detection with various types of social media. In future, A new approach for event detection in social media will be proposed to improve the accuracy and speed.