Detection and Extracting of Emergency Knowledge from Twitter Streams

Klein, Bernhard; Laiseca, Xabier; Casado-Mansilla, Diego; López-de-Ipiña, Diego; Nespral, Alejandro Prada

doi:10.1007/978-3-642-35377-2_64

Cited by 22 publications

(12 citation statements)

References 7 publications

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“…New opportunities arise to use this tool to detect and extract crucial information about the scope and nature of certain emerging events. A major challenge for the extraction of such event information from Twitter is represented by the unstructured and noisy nature of tweets which requires a graph based representation that can accommodates such complexity and be practical to be used for extracting such relations [3,4].…”

Section: Figure 3 Comparing the Performance Of Neo4j With Three Relamentioning

confidence: 99%

Social Recommendation using Graph Database Neo4j: Mini Blog, Twitter Social Network Graph Case Study

Zhang¹,

Fiaidhi²,

Mohammed³

2017

IJFGCN

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Section: Figure 3 Comparing the Performance Of Neo4j With Three Relamentioning

confidence: 99%

Social Recommendation using Graph Database Neo4j: Mini Blog, Twitter Social Network Graph Case Study

Zhang¹,

Fiaidhi²,

Mohammed³

2017

IJFGCN

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“…The relevance of metadata data is highlighted in [8]. [9,10] discuss the relevance of metadata and its applications. Extracting facets from the tweets in order to facilitate multi-faceted search was explored [9].…”

Section: Related Workmentioning

confidence: 99%

“…Adding Twitter specific metadata to the social network graph, can lend rich expressiveness for later analysis. Node information is enhanced with the user name, the status count and friends/followers count whenever the tweets are parsed by the social network analyzer [10]. Information credibility of a Twitter tweet can be determined on the basis of the presence of URL, the number of retweets and the length of the tweets [11].…”

Section: Related Workmentioning

confidence: 99%

Automating Corpora Generation with Semantic Cleaning and Tagging of Tweets for Multi-dimensional Social Media Analytics

Javed¹,

L²

2015

IJCA

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Developing corpora from social media content involves convoluted cleaning. In this paper we propose and implement the automation of corpora building for facilitating social media mining and analytics. This automation process incorporates: a) metadata extraction and structuring b) semantic cleaning with tagging and c) learning domain terms/entities. The implementation performs comprehensive cleaning including abbreviation and slang correction, phonetic matching using metaphone algorithm, splitting joined words and identifying/learning entities. It identifies the entities, tags them and creates/updates a knowledgebase (KB) comprising of domain terms. The corpus thus constructed, facilitates multidimensional analysis and summarization. This proposed technique was tested with an experiment in which real world streaming tweets pertaining to Indian politics were collected, structured, cleaned and tagged. The results of the automation experiment can be stated as follows: a) the tweets although primarily in English, contained at times words from the regional languages. The algorithm does not recognize these words and they are construed as domain terms. An accuracy of 85.55% was achieved in identifying the correct domain terms and entities. b) The automation required human feedback and intervention which progressively reduced and reached a figure of 18% with the update and enhancement of the KB. This paper assumes relevance because the implementation automates the entire process of collecting and cleaning the tweets and yields a corpus suitable for multifaceted analysis.

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“…Some products already exist to facilitate such second order validation of crimes. Products like SensePlace2 [148], Twitter-based event detection and analysis system (TEDAS) [149], DataSift, Gnip, SABESS [150], and others, enable those interested in crime or emergency detection to gather and aggregate publicly-available, geo-located, time-stamped information in real time about where and when an incident may have occurred, who was involved and how serious it was.…”

Section: New Possibilities For Validating the Geography Of Crimementioning

confidence: 99%

Validating the Remotely Sensed Geography of Crime: A Review of Emerging Issues

Kelly

2014

Remote Sensing

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This paper explores the existing literature on the active detection of crimes using remote sensing technologies. The paper reviews sixty-one studies that use remote sensing to actively detect crime. Considering the serious consequences of misidentifying crimes or sites of crimes (e.g., opening that place and its residents up to potentially needless intrusion, intimidation, surveillance or violence), the authors were surprised to find a lack of rigorous validation of the remote sensing methods utilized in these studies. In some cases, validation was not mentioned, while in others, validation was severely hampered by security issues, rough terrain and weather conditions. The paper also considers the potential hazards of the use of Google Earth to identify crimes and criminals. The paper concludes by considering alternate, "second order" validation techniques that could add vital context and understanding to remotely sensed images in a law enforcement context. With this discussion, the authors seek to initiate a discussion on other potential "second order" validation techniques, as well as on the exponential growth of surveillance in our everyday lives.

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Detection and Extracting of Emergency Knowledge from Twitter Streams

Cited by 22 publications

References 7 publications

Social Recommendation using Graph Database Neo4j: Mini Blog, Twitter Social Network Graph Case Study

Social Recommendation using Graph Database Neo4j: Mini Blog, Twitter Social Network Graph Case Study

Automating Corpora Generation with Semantic Cleaning and Tagging of Tweets for Multi-dimensional Social Media Analytics

Validating the Remotely Sensed Geography of Crime: A Review of Emerging Issues

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