CrisisTracker: Crowdsourced social media curation for disaster awareness

Rogstadius, Jakob; Vuković, Maja; Teixeira, Carlos Antonio; Kostakos, Vassilis; Karapanos, Evangelos; Laredo, Jim

doi:10.1147/jrd.2013.2260692

Cited by 175 publications

(113 citation statements)

References 18 publications

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“…Vieweg et al (2010), for instance, used terms like "grass fire" or "red river" to collect tweets that contained terms related to the Oklahoma grass fires and Red River floods in spring 2009. The selection of the keywords affects the number and quality of the returned tweets; this has been shown by e.g., Rogstadius et al (2013) or Joseph et al (2014). The geolocation of posts from social networks can be used as an alternative to filtering using disaster-related and language-specific keywords.…”

Section: State Of the Art And Related Workmentioning

confidence: 95%

Social media as an information source for rapid flood inundation mapping

Fohringer

Dransch

Kreibich

et al. 2015

Nat. Hazards Earth Syst. Sci.

206

129

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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.

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Section: State Of the Art And Related Workmentioning

confidence: 95%

Social media as an information source for rapid flood inundation mapping

Fohringer

Dransch

Kreibich

et al. 2015

Nat. Hazards Earth Syst. Sci.

206

129

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“…Once these clusters were generated, Petrović was able to track their growth over time to determine impact for a given event. This research was unique in that it was one of the early methods that did not require pre-specified seed tokens for detecting events and has been very influential in the field, resulting in a number of additional publications that demonstrate its utility in breaking news and high-impact crisis events [21,51,52]. An open issue in Petrović's work, however, is its reliance on semantic similarity between tweets, which limits its ability to operate in mixed-language environments.…”

Section: Social Media Terrorism and Crisis Informaticsmentioning

confidence: 99%

Discovering Credible Events in Near Real Time from Social Media Streams

Buntain

2015

Proceedings of the 24th International Conference on World Wide Web

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Recent reliance on social media platforms as major sources of news and information, both for journalists and the larger population and especially during times of crisis, motivate the need for better methods of identifying and tracking high-impact events in these social media streams. Social media's volume, velocity, and democratization of information (leading to limited quality controls) complicate rapid discovery of these events and one's ability to trust the content posted about these events. This dissertation addresses these complications in four stages, using Twitter as a model social platform. The first stage analyzes Twitter's response to major crises, specifically terrorist attacks in Western countries, showing these high-impact events do not significantly impact message or user volume. Instead, these events drive changes in Twitter's topic distribution, with conversation, retweets, and hashtags relevant to these events experiencing significant, rapid, and short-lived bursts in frequency.Furthermore, conversation participants tend to prefer information from local authorities/organizations/media over national or international sources, with accounts for local police or local newspapers often emerging as central in the networks of interaction. Building on these results, the second stage in this dissertation presents and evaluates a set of features that capture these topical bursts associated with crises by modeling bursts in frequency for individual tokens in the Twitter stream. The resulting streaming algorithm is capable of discovering notable moments across a series of major sports competitions using Twitter's public stream without relying on domain-or language-specific information or models. Furthermore, results demonstrate models trained on sporting competition data perform well when transferred to earthquake identification. This streaming algorithm is then extended in this dissertation's third stage to support real-time event tracking and summarization. This real-time algorithm leverages new distributed processing technology to operate at scale and is evaluated against a collection of other community-developed information retrieval systems, where it performs comparably. Further experiments also show this real-time burst detection algorithm can be integrated with these other information retrieval systems to increase overall performance. The final stage then investigates automated methods for evaluating credibility in social media streams by leveraging two existing data sets. These two data sets measure different types of credibility (veracity versus perception), and results show veracity is negatively correlated with the amount of disagreement in and length of a conversation, and perceptions of credibility are influenced by the amount of links to other pages, shared media about the event, and the number of verified users participating in the discussion. Contributions made across these four stages are then usable in the relatively new fields of computational journalism and crisis informatics, which seek ...

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“…In particular, during emergency situations such as natural disasters, information shared on social media has been shown to be valuable [19]. Indeed, experts in a variety of domains have developed tools to use social media to identify and track events [1,12,14,16]. For instance, the Crisis Tracker system [16], uses a manually created set of search terms to crawl crisis-related content, which is then clustered automatically and manually annotated by volunteers.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, experts in a variety of domains have developed tools to use social media to identify and track events [1,12,14,16]. For instance, the Crisis Tracker system [16], uses a manually created set of search terms to crawl crisis-related content, which is then clustered automatically and manually annotated by volunteers. Such crisis tracking and analysis tools can collect, analyze and aggregate information from people Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Eaims

McCreadie

Macdonald

Ounis

2016

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Social media has great potential as a means to enable civil protection and law enforcement agencies to more effectively tackle disasters and emergencies. However, there is currently a lack of tools that enable civil protection agencies to easily make use of social media. The Emergency Analysis Identification and Management System (EAIMS) is a prototype service that provides real-time detection of emergency events, related information finding and credibility analysis tools for use over social media during emergencies. This system exploits machine learning over data gathered from past emergencies and disasters to build effective models for identifying new events as they occur, tracking developments within those events and analyzing those developments for the purposes of enhancing the decision making processes of emergency response agencies.

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CrisisTracker: Crowdsourced social media curation for disaster awareness

Cited by 175 publications

References 18 publications

Social media as an information source for rapid flood inundation mapping

Social media as an information source for rapid flood inundation mapping

Discovering Credible Events in Near Real Time from Social Media Streams

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