Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data.
In this paper, we propose a semantic approach for monitoring information published on social networks about a specific event. In the era of Big Data, when an emergency occurs information posted on social networks becomes more and more helpful for emergency operators. As direct witnesses of the situation, people share photos, videos or text messages about events that call their attention. In the emergency operation center, these data can be collected and integrated within the management process to improve the overall understanding of the situation and in particular of the citizen reactions. To support the tracking and analyzing of social network activities, there are already monitoring tools that combine visualization techniques with geographical maps. However, tweets are written from the perspective of citizens and the information they provide might be inaccurate, irrelevant or false. Our approach tries to deal with data relevance proposing an innovative ontology-based method for filtering tweets and extracting meaningful topics depending on their semantic content. In this way data become relevant for the operators to make decisions. Two real cases used to test its applicability showed that different visualization techniques might be needed to support situation awareness. This ontology-based approach can be generalized for analyzing the information flow about other domains of application changing the underlying knowledge base. [ABSTRACT FROM AUTHOR]/nCopyright of SpringerPlus is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
In the Hierarchical Edge Bundle, categories are coded with different colors, as shown in Fig. 2, and terms are grouped all around a circle and linked depending on their cooccurrences in the same tweet.Observing the Bubble Chart evolution, it is possible to note how the terms with a highest relevance are mostly contained in the specific categories of emergency, evacuation, hashtags and place.
comparative study, literature review, case studies
Tweet categoriesEmergencyEvacuationMediaGeneralHashtagsPlaceTime
Two real cases used to test its applicability showed that different visualization techniques might be needed to support situation awareness.We have chosen two case studies that vary considerably in the number of information generated and they help also to illustrate potential visualization techniques that can be used to support data exploration. The visualization of the outcomes of this process plays a fundamental role in order to facilitate the interpretation by the operators.
Case Studies: Hurricane Sandy (500.000 Tweets), Nepal Earthquake event (822 tweets)
Paper specific RQ:How can emergency operators make sense of them without losing time?Case study specific researchquestions(1) Which are the most discussed topics/categories as the ones that receive the highest number of arcs (e.g. earthquake, aid or the hashtag #nepalearthquake). (2) Which terms are mostly used together for stating an opinion or a feeling as connections among different terms (e.g. people with aid and need). (3) Which kind of information is commonly shared during this event as the classes with a higher number of terms (e.g. general and emergency). (4) How the information flows from a topic to another as the categories that have the greatest number of links between each other (e.g. hashtags and emergency).
This ontology-based approach can be generalized for analyzing the information flow about other domains of application changing the underlying knowledge base.Answer to SQ: The answer is an intelligent tool able to collect, analyze and extract relevant information for them
tweets are written from the perspective of citizens and the information they provide might be inaccurate, irrelevant or false. Our approach tries to deal with data relevance proposing an innovative ontology-based method for filtering tweets and extracting meaningful topics depending on their semantic content. In this way data become relevant for the operators to make decisions.
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