Using Social Media to Enhance Emergency Situation Awareness

Yin, Jie; Lampert, Andrew; McCarthy, Cameron; Robinson, Bella; Power, Robert

doi:10.1109/mis.2012.6

Cited by 547 publications

(337 citation statements)

References 9 publications

Supporting

Mentioning

307

Contrasting

Unclassified

Order By: Relevance

“…For instance, Sakaki et al (2010) used a support vector machine (SVM) based on linguistical and statistical features such as keywords, the number of words and the context of target-event words for the detection of earthquake events in Japan. Yin et al (2012) developed a classifier that automatically identifies tweets including information about the condition of certain infrastructure components like buildings, roads or energy supplies during the Christchurch earthquake in February 2011 by utilizing additional Twitter-specific statistical features like the number of hashtags and user mentions. Other important features as observed by Verma et al (2011) are subjectivity and sentiment that can also help to find information contributing to situational awareness.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Social media as an information source for rapid flood inundation mapping

Fohringer

Dransch

Kreibich

et al. 2015

Nat. Hazards Earth Syst. Sci.

202

129

View full text Add to dashboard Cite

Abstract. During and shortly after a disaster, data about the hazard and its consequences are scarce and not readily available. Information provided by eyewitnesses via social media is a valuable information source, which should be explored in a more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eyewitnesses extracted from social media posts and their integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, areas affected by a flood, for example, can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information, as well as to interpret and integrate the posts into mapping procedures in a timely manner. To support rapid inundation mapping we propose a methodology and develop "PostDistiller", a tool to filter geolocated posts from social media services which include links to photos. This spatial distributed contextualized in situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.

show abstract

Section: State Of the Art And Related Workmentioning

confidence: 99%

Social media as an information source for rapid flood inundation mapping

Fohringer

Dransch

Kreibich

et al. 2015

Nat. Hazards Earth Syst. Sci.

202

129

View full text Add to dashboard Cite

show abstract

“…The work of [7] [34]. Feature extraction consisted of word unigrams, word bigrams, word length, hashtag count, number of username mentions, retweets and replies.…”

Section: Twitter Applications and Architecturesmentioning

confidence: 99%

SVM-Based Domain Adaptation Machine Learned Models for the Automatic Classification of Disaster-Related Tweets

2017

IJCCIE

View full text Add to dashboard Cite

Abstract-Twitter is a well-known microblogging service used by many users to quickly acquire disaster-related information. Since there is an influx of tweet during the occurrence of a disaster event, the need to automatically filter informative disaster-related tweets is imperative. In this study, the researchers used the Habagat tweets and tweets gathered during the Zamboanga Siege event as training corpora for training two Support Vector Machine (SVM) machine classifiers. The first machine classifier is used to filter informative tweets while the second model is used to classify informative tweets as Donation, Traffic update, Weather update, Class suspension, Rescue and Relief and River level/condition. These subclasses were based on the latent topics extracted from the corpus of informative tweets using Principal Component Analysis(PCA) and Latent Dirichlet Allocation(LDA). Several experiments using different preprocessing techniques, vector space of unigrams/bigrams with TF-IDF weightings on balanced and unbalanced data were applied to test and train the classifiers. SVM and Expectation Maximization (EM) algorithms were used for the training and testing of the machine classifiers. The models were evaluated based on the performance metrics of accuracy, precision and recall. Experimental results show that for a binary classifier, using unigram with a corpus of unbalanced labeled corpus returned best classification results and applying bigram to a balanced corpus gave high performance results. Furthermore, the multiclass model yielded better performance results with unigrams as features.

show abstract

“…Its use first started organically among citizens and was then quickly adopted by the government as a communication and intelligencegathering platform during the response (Tseng et al 2011). While an important issue with the use of interactive social media, such as Twitter, is that unreliable retweets may give incorrect information (Acar and Muraki 2011), social media is Brought to you by | EP Ipswich Authenticated Download Date | 9/1/15 6:38 PM said to enhance situational awareness of victims during a crisis because the near instant updates that it provides (Yin et al 2012). …”

Section: Theoretical Foundation: Citizen-centric E-governance and Socmentioning

confidence: 99%

Understanding Risk Communication Gaps through E-Government Website and Twitter Hashtag Content Analyses: The Case of Indonesia’s Mt. Sinabung Eruption

Chatfield

Reddick

2015

Journal of Homeland Security and Emergency Management

View full text Add to dashboard Cite

Despite operational volcano surveillance using meteorological satellite, thermal remote sensing and infrared camera technologies, predicting a volcano's hazardous activity and explosive eruption, with any accuracy, remains nearly impossible. Volcanic hazards and risk literature identifies risk communication as critical but insufficiently understood. This paper, therefore, investigates disaster risk communications during Indonesia's Mt. Sinabung eruptions in February 2014, causing 16 deaths and continuous evacuations of 30,000 residents. We critically examined one-way government-to-citizens communications via e-government websites and Twitter and citizens' multi-directional communications via #sinabung hashtag for sharing volcano risk perceptions about affected villagers. Our Twitter analysis of #sinabung found evidence for the absence of government engagement in risk perception communications and leadership in responding to the Indonesian public's expressed concerns for the Mt. Sinabung disaster. For governments that would implement citizencentric e-governance, Twitter can be used effectively as a multi-directional risk communication tool to quickly and effectively share risk perceptions and disaster information with the public. Abstract: Despite operational volcano surveillance using meteorological satellite, thermal remote sensing and infrared camera technologies, predicting a volcano's hazardous activity and explosive eruption, with any accuracy, remains nearly impossible. Volcanic hazards and risk literature identifies risk communication as critical but insufficiently understood. This paper, therefore, investigates disaster risk communications during Indonesia's Mt. Sinabung eruptions in February 2014, causing 16 deaths and continuous evacuations of 30,000 residents. We critically examined one-way government-to-citizens communications via e-government websites and Twitter and citizens' multi-directional communications via #sinabung hashtag for sharing volcano risk perceptions about affected villagers. Our Twitter analysis of #sinabung found evidence for the absence of government engagement in risk perception communications and leadership in responding to the Indonesian public's expressed concerns for the Mt. Sinabung disaster. For governments that would implement citizen-centric e-governance, Twitter can be used effectively as a multi-directional risk communication tool to quickly and effectively share risk perceptions and disaster information with the public.

show abstract

Using Social Media to Enhance Emergency Situation Awareness

Cited by 547 publications

References 9 publications

Social media as an information source for rapid flood inundation mapping

Social media as an information source for rapid flood inundation mapping

SVM-Based Domain Adaptation Machine Learned Models for the Automatic Classification of Disaster-Related Tweets

Understanding Risk Communication Gaps through E-Government Website and Twitter Hashtag Content Analyses: The Case of Indonesia’s Mt. Sinabung Eruption

Contact Info

Product

Resources

About