In previous work we reported that resources linked in tweets disappeared at the rate of 11 in the first year followed by 7.3 each year afterwards. We also found that in the first year 6.7 , and 14.6 in each subsequent year, of the resources were archived in public web archives. In this paper we revisit the same dataset of tweets and find that our prior model still holds and the calculated error for estimating percentages missing was about 4 , but we found the rate of archiving produced a higher error of about 11.5 . We also discovered that resources have disappeared from the archives themselves (7.89 ) as well as reappeared on the live web after being declared missing (6.54 ). We have also tested the availability of the tweets themselves and found that 10.34 have disappeared from the live web. To mitigate the loss of resources on the live web, we propose the use of a "tweet signature". Using the Topsy API, we extract the top five most frequent terms from the union of all tweets about a resource, and use these five terms as a query to Google. We found that using tweet signatures results in discovering replacement resources with 70+ textual similarity to the missing resource 41 of the time.
This in-depth analysis and study of the social media, its nature, the information dissemination patterns, and the user behavior and interaction paved the way for the researchers to have a better understanding of how the social media played a major role in narrating publicly significant events. These studies prove that user-generated content in social media is of crucial importance and can be considered the first draft of history. analyzed two natural hazard events (the Oklahoma grass fires and the red River floods in 2009) and how microblogging contributed in raising the situational awareness of the public @cite_5 . Starbird and Palen analyzed how the crowd interact with politically sensitive context regarding the Egyptian revolution of 2011 @cite_18 . in another study utilized collaborative filtering techniques for identifying social media users who are most likely to be on the ground during a mass disruption event @cite_0 . investigated weblogs to examine societ al interactions to a disaster over time and how they reflect the collective public view towards this disaster @cite_17 .
@cite_0 @cite_5 @cite_18 @cite_17
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Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writers attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches wh Social media are forms of information and communication technology disseminated through social interaction. Social media rely on peer-to-peer (P2P) networks that are collaborative, decentralized, and community driven. They transform people from content consumers into content producers. Popular networking sites such as MySpace, Facebook, Twitter, and Google are versions of social media that are most commonly used for connecting with friends, relatives, and employees. The role of social media in disaster management became galvanized during the world response to the 2010 Haiti earthquake. During the immediate aftermath, much of what people around the world were learning about the earthquake originated from social media sources. Social media became the new forum for collective intelligence, social convergence, and community activism. During the first 2 days following the earthquake, "texting" mobile phone users donated more than $5 million to the American Red Cross. Both public and private response agencies used Google Maps. Millions joined MySpace and Facebook discussion groups to share information, donate money, and offer comfort and support. Social media has also been described as "remarkably well organized, self correcting, accurate, and concentrated," calling into question the ingrained view of unidirectional, official-to-public information broadcasts. Social media may also offer potential psychological benefit for vulnerable populations gained through participation as stakeholders in the response. Disaster victims report a psychological need to contribute, and by doing so, they are better able to cope with their situation. Affected populations may gain resilience by replacing their h Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions. Situation awareness plays an important role in disaster response and emergency management. Displaying real-time location-based social media messages along with videos, pictures, and hashtags during a disaster event could help first responders improve their situation awareness . A geo-targeted event observation (Geo) Viewer was developed for monitoring real-time social media messages in target areas with four major functions: (1) real-time display of geo-tagged tweets within the target area; (2) interactive mapping functions; (3) spatial, text, and temporal search functions using keywords, spatial boundaries, or dates; and (4) manual labeling and text-tagging of messages. Different from traditional web GIS maps, the user interface design of GeoViewer provides the interactive display of multimedia content and maps. The front-end user interface to visualize and query tweets is built with open source programming libraries using server-side MongoDB. GeoViewer is built for assisting emergency responses and disaster management tasks by tracking disaster event impacts, recovery activities, and residents needs in the target region.