In recent years, situation awareness has been recognised as a critical part of effective decision making, in particular for crisis management. One way to extract value and allow for better situation awareness is to develop a system capable of analysing a dataset of multiple posts, and clustering consistent posts into different views or stories (or, world views). However, this can be challenging as it requires an understanding of the data, including determining what is consistent data, and what data corroborates other data. Attempting to address these problems, this article proposes Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on utilising the Subject-Verb-Object (SVO) typology in order to construct semantically consistent world views, in which individuals---particularly those involved in crisis response---might achieve an enhanced picture of a situation from social media data. To evaluate our system and its ability to provide enhanced situation awareness, we tested it against existing approaches, including human data analysis, using a variety of real-world scenarios. The results indicated a noteworthy degree of evidence (e.g., in cluster granularity and meaningfulness) to affirm the suitability and rigour of our approach. Moreover, these results highlight this article's proposals as innovative and practical system contributions to the research field.
The proliferation of social media has made it a practicable medium to acquire insights about events and their development in environments @cite_47 . The role of social media in natural disaster crisis management became clear during the 2010 Haiti earthquake @cite_2 , and has increased significantly since then @cite_17 . The research community has focused on two general areas to gain the most value from social media especially with such situations in mind. These include, tackling misinformation and its spread, and broad approaches to understanding situations.
@cite_47 @cite_17 @cite_2
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In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims’ requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them. 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 helplessness with dignity, control, as well as personal and collective responsibility. However, widespread use of social media also involves several important challenges for disaster management. Although social media is growing rapidly, it remains less widespread and accessible than traditional media. Also, public officials often view P2P communications as "backchannels" with potential to spread misinformation and rumor. In addition, in absence of the normal checks and balances that regulate traditional media, privacy rights violations can occur as people use social media to describe personal events and circumstances. Language: en 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. 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.