Pattern matching algorithms
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Mining Web Informative Structures and Contents Based on Entropy Analysis
IEEE Transactions on Knowledge and Data Engineering
Automatic generation of agents for collecting hidden web pages for data extraction
Data & Knowledge Engineering - Special issue: WIDM 2002
Query-related data extraction of hidden web documents
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive record extraction from web pages
Proceedings of the 16th international conference on World Wide Web
An automatic data grabber for large web sites
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Age differences in online social networking
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Social networks, gender, and friending: An analysis of MySpace member profiles
Journal of the American Society for Information Science and Technology
NET – a system for extracting web data from flat and nested data records
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
| Hi-index | 0.00 |
The increase in social computing has provided the situation where large amounts of personal information are being posted online. This makes people vulnerable to social engineering attacks because their personal details are readily available. Our automated approach for personal data extraction was developed to extract personal details and top friends from MySpace profiles and place them into a repository. An online social network graph was generated from the repository data where nodes represent peoples' profiles. Analysis was carried out into what factors affect node vulnerability. The graph analysis identified structural features of the nodes, e.g., clustering coefficient, indegree and outdegree, which contribute towards vulnerability. From this, it was found that the number of neighbours and the clustering coefficient were major factors in making a node vulnerable because of the potential to spread personal details around the network. These results provide a good foundation for future work on online vulnerability in online social networks (OSNs).