No abstract

With unemployment amongst graduates in Saudi Arabia, social media platforms especially Twitter play a big part in communication. However, do universities utilise Twitter enough when engaging with employers and the community? This study analyses the post content in the main Twitter accounts of 13 private universities and 20 public universities in Saudi Arabia. Automated data extraction via Twitter API was used to gather the data. From the extracted tweets, 350 tweets were selected from private university accounts and 400 tweets from public university accounts to code for content analysis. The post content from both university types was informative and there was no presence of opinions. The most popular category of content for private universities was university events whereas for public universities it was general advertisements. References to employer engagement were more present in private university tweets compared to public university tweets. The study did highlight that more material needs to be posted relating to knowledge and technology transfer.

No abstract

Abstract: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).Keywords: online social network; OSN; vulnerability; information disclosure; automated data retrieval.Reference to this paper should be made as follows: Alim, S., Abdulrahman, R., Neagu, D. and Ridley, M. (2011) 'Online social network profile data extraction for vulnerability analysis ', Int. J. Internet Technology and Secured Transactions, Vol. 3, No. 2, Biographical notes: Sophia Alim graduated in 2006 with BSc (Hons.) in Business Information Systems from the University of Salford, UK. In 2007, she received her MSc in Computing from the University of Bradford UK. At the same university, currently, she is working towards a PhD with Dr. Daniel Neagu and Mr. Mick Ridley as her supervisors. Her research focuses on the ever evolving area of social networking and how the issue of privacy is going to affect the structure and information disclosure of these networks. Her motivation for her research comes from her desire to reflect the multidisciplinary areas of computing. Her research interests include web accessibility and social networking. Online social network profile data extraction for vulnerability analysis 195Ruqayya Abdulrahman is a Lecturer in Computer Science at Taibah University, Saudi Arabia. In 2002, she obtained her BSc (Hons.) in Computer Science from King Abdulaziz University in Saudi Arabia. In 2007; she was awarded an MSc with distinction in Software Engineering by the University of Bradford, UK. Currently, she is a PhD student at the School of Computing, Informatics and Media of the University of Bradford. Her research addresses software agents, web database processing, data retrieval, online social network and software engineering.Daniel Neagu is a Senior Lecturer in Computing at the University of Bradford. His research interests include knowledge discovery, information retrieval, data mining applications in multidisciplinary projects (with a focus in online social networks, healthcare and web profiling) by fusion of human experts knowledge and...

No abstract