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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We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques. This paper examines microblogging information diffusion activity during the 2011 Egyptian political uprisings. Specifically, we examine the use of the retweet mechanism on Twitter, using empirical evidence of information propagation to reveal aspects of work that the crowd conducts. Analysis of the widespread contagion of a popular meme reveals interaction between those who were "on the ground" in Cairo and those who were not. However, differences between information that appeals to the larger crowd and those who were doing on-the-ground work reveal important interplay between the two realms. Through both qualitative and statistical description, we show how the crowd expresses solidarity and does the work of information processing through recommendation and filtering. We discuss how these aspects of work mutually sustain crowd interaction in a politically sensitive context. In addition, we show how features of this retweet-recommendation behavior could be used in combination with other indicators to identify information that is new and likely coming from the ground. Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems. Today's crises attract great attention on social media, from local and distant citizens as well as from news media. This study investigates the possibilities of real-time and automated analysis of Twitter messages during crises. The analysis was performed through application of an information extraction tool to nearly 97,000 tweets that were published shortly before, during and after a storm hit the Pukkelpop 2011 festival in Belgium. As soon as the storm hit the festival tweet activity increased exponentially, peaking at 576 tweets per minute. The extraction tool enabled analyzing tweets through predefined (geo)graphical displays, message content filters (damage, casualties) and tweet type filters (e.g., retweets). Important topics that emerged were 'early warning tweets', 'rumors' and the 'self-organization of disaster relief' on Twitter. Results indicate that automated filtering of information provides valuable information for operational response and crisis communication. Steps for further research are discussed. © 2012 ISCRAM. Environmental Systems Research Institute, Inc. (ESRI)