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Erschienen in:
Recent Advances in Computational Intelligence in Defense and Security Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Text Mining in Social Media for Security Threats

verfasst von : Diana Inkpen

Erschienen in: Recent Advances in Computational Intelligence in Defense and Security

Verlag: Springer International Publishing

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Abstract

We discuss techniques for information extraction from texts, and present two applications that use these techniques. We focus in particular on social media texts (Twitter messages), which present challenges for the information extraction techniques because they are noisy and short. The first application is extracting the locations mentioned in Twitter messages, and the second one is detecting the location of the users based on all the tweets written by each user. The same techniques can be used for extracting other kinds of information from social media texts, with the purpose of monitoring the topics, events, emotions, or locations of interest to security and defence applications.

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Vorheriges Kapitel Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) Using Fuzzy Co-occurrence Matrix Texture Features
Nächstes Kapitel DroidAnalyst: Synergic App Framework for Static and Dynamic App Analysis
Fußnoten
1
[25] recently released a dataset of various kinds of social media data annotated with generic location expressions, but not with cities, states/provinces, and countries.
 
4
The number of countries is larger than 200 because alternative names are counted; the same for states/provinces and cities.
 
8
Explained in Sect. 5.2.
 
9
Not all of these 5000 n-grams are necessarily good location indicators, we don’t manually distinguish them; a machine learning model after training should be able to do so.
 
10
Alternatively, we also tried the loss function defined as the average squared error of output numbers, which is equivalent to the average Euclidean distance between the estimated location and the true location; this alternative model did not perform well.
 
14
We are unable to conduct t-tests on the Eisenstein models, because of the unavailability of the details of the results produced by these models.
 
15
We are unable to conduct t-tests on the other models, because of the unavailability of the details of the results produced by these models.
 
16
Only this metric was reported by the author in the top 3 % features configuration.
 
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Metadaten
Titel
Text Mining in Social Media for Security Threats
verfasst von
Diana Inkpen
Copyright-Jahr
2016
Buch
Recent Advances in Computational Intelligence in Defense and Security

Print ISBN: 978-3-319-26448-6

Electronic ISBN: 978-3-319-26450-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-26450-9_19

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