Athena Vakali
ABSTRACT
Social networking media generate huge content streams, which leverage, both academia and developers efforts in providing unbiased, powerful indications of users' opinion and interests. Here, we present Cloud4Trends, a framework for collecting and analyzing user generated content through microblogging and blogging applications, both separately and jointly, focused on certain geographical areas, towards the identification of the most significant topics using trend analysis techniques. The cloud computing paradigm appears to offer a significant benefit in order to make such applications viable considering that the massive data sizes produced daily impose the need of a scalable and powerful infrastructure. Cloud4Trends constitutes an efficient Cloud-based approach in order to solve the online trend tracking problem based on Web 2.0 sources. A detailed system architecture model is also proposed, which is largely based on a set of service modules developed within the VENUS-C research project to facilitate the deployment of research applications on Cloud infrastructures.
- N. S. Glance, M. Hurst, and T. Tomokiyo: BlogPulse: Automated Trend Discovery for Weblogs. In WWW Conference. 2004.Google Scholar
- M. Uchida, N. Shibata, and S. Shirayama: Identification and Visualization of Emerging Trend from Blogosphere, in proceedings of International Conference on Weblogs and Social Media (ICWSM), pp. 305--306, 2007.Google Scholar
- M. Mathioudakis, N. Bansal, and N. Koudas: Identifying, attributing and describing spatial bursts. Proc. VLDB Endow. 3, 1--2 (September 2010), 1091--1102. 2010. Google ScholarDigital Library
- B. E. Teitler, M. D. Lieberman, D. Panozzo, J. Sankaranarayanan, H. Samet, and J. Sperling: NewsStand: a new view on news. In GIS '08. ACM, Article 18, 10 pages. 2008. Google ScholarDigital Library
- J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling: TwitterStand: news in tweets. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS '09). ACM, 42--51. 2009. Google ScholarDigital Library
- M. Mathioudakis and N. Koudas: TwitterMonitor: trend detection over the twitter stream. In Proceedings of the 2010 international conference on Management of data (SIGMOD '10). ACM, 1155--1158. 2010. Google ScholarDigital Library
- V. Koutsonikola, A. Vakali, E. Giannakidou, I. Kompatsiaris: Clustering Users of a Social Tagging System: A Topic and Time Based Approach. WISE 2009: 75--86. 2009. Google ScholarDigital Library
- I. Livenson and E. Laure: Towards transparent integration of heterogeneous cloud storage platforms. In DIDC '11. ACM, 27--34. 2011. Google ScholarDigital Library
- M. Giatsoglou and A. Vakali: Capturing Social Data Evolution via Graph Clustering. IEEE Internet Computing. 2012. Preprint. DOI: 10.1109/MIC.2012.24. Google ScholarCross Ref
- Spärck Jones, Karen: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28 (1): 11--21. 1972. DOI:10.1108/eb026526.Google ScholarCross Ref
- D. Lezzi, R. Rafanell, F. Lordan, E. Tejedor, R.M. Badia: COMPSs in the VENUS-C Platform: enabling e-Science applications on the Cloud. In Proceedings of 4th Iberian Grid Infrastructure Conference, Santander, 2011, 73--84.Google Scholar
- Yogesh Simmhan, Catharine van Ingen, Girish Subramanian, and Jie Li: Bridging the Gap between Desktop and the Cloud for eScience Applications. In Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD '10). IEEE Computer Society, 474--481. 2010. Google ScholarDigital Library
- C. Taylor, (June 27, 2011): Social networking 'utopia' isn't coming. CNN.http://articles.cnn.com/2011-06--27/tech/limits.social.networking.taylor_1_twitter-users-facebook-friends-connections?_s=PM:TECH.Google Scholar
- Foster I. et. al:OGSA Basic Execution Service Version 1.0. Grid Forum Document GFD-RP. 108. 8 August 2007.Google Scholar
- Savva A (Editor). Job Submission Description Language (JSDL) Specification, Version 1.0. Grid Forum Document GFD-R.056. 7 November 2005.Google Scholar
- SNIA CDMI: http://www.snia.org/cdmiGoogle Scholar
Index Terms
- Social networking trends and dynamics detection via a cloud-based framework design
Recommendations
The Dynamics of (Not) Unfollowing Misinformation Spreaders
WWW '24: Proceedings of the ACM on Web Conference 2024Many studies explore how people "come into" misinformation exposure. But much less is known about how people "come out of" misinformation exposure.Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six months, ...
Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
Detection of emerging topics is now receiving renewed interest motivated by the rapid growth of social networks. Conventional-term-frequency-based approaches may not be appropriate in this context, because the information exchanged in social-network ...
Characterizing social cascades in flickr
WOSN '08: Proceedings of the first workshop on Online social networksOnline social networking sites like MySpace and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to the viral marketing of content, products, and political campaigns on the sites themselves. Despite the ...
Comments