Social networks content analysis for peacebuilding application
Peace provides the freedom to express our views, to relate with others people and create co-operation and Social Networks (SNs)provides that platform. SNs can play a very important role to improve Peacebuilding (Pb) applications as current peace related studies witness that violence and Pb related...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer International Publishing
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/49540/ http://irep.iium.edu.my/49540/ http://irep.iium.edu.my/49540/ http://irep.iium.edu.my/49540/1/Zaharah_1.pdf http://irep.iium.edu.my/49540/7/49540-Social_Networks_Content_Analysis_Fullpaper.pdf |
| Summary: | Peace provides the freedom to express our views, to relate
with others people and create co-operation and Social Networks (SNs)provides that platform. SNs can play a very important role to improve Peacebuilding (Pb) applications as current peace related studies witness that violence and Pb related reports are communicated through different SNs applications. People and victims of the conflicts make use of SNs and its applications to cast their concerns. However, the major setback of these SNs is to manage the huge amount of SNs data and to extract the topic specific (Pb related) information. There is lack of research done on SNCA by Pb perspective. Therefore the objective of this research is to perform CA, means to identify which (SN) what (data) how (to extract)? Furthermore, what features and techniques should be used for CA of Pb related data? This research proposes framework for automatic SNs data extraction (DE) and content analysis (CA) to achieve our objective. The proposed framework shows that twitter is most popular SN for Pb CA purpose and proposed framework presents the searching criteria and custom filters to extract the topic specific data. Moreover, the research proposes to use lexical analysis (LA) method to extract the SNs features, 1st order context representation (CR) technique to represent the context of the extracted features, DBSCAN clustering algorithm for data management by making different clusters, ranking algorithm, Log likelihood ratio and SVM techniques for content analysis and classification. The proposed framework aims to help in conducting SNCA to support Pb application in order to take important information from the sea of SNs data to predict violence related information or incidents that will help peacekeepers for communicating and maintaining peace related
news (may it be natural disaster or man-made terrorism activities) around the world.
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