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|Large Scale Network Analysis for Online Social Brand Advertising
Kunpeng Zhang, Siddhartha Bhattacharyya, and Sudha Ram; MIS Quarterly, Special Issue: Transformational Issues of Big Data and Analytics in Networked Business; Forthcoming
This paper proposes an audience selection framework for online brand advertising based on user activities on social media platforms. It is one of the first studies to our knowledge that develops and analyzes implicit brand–brand networks for online brand advertising. This paper makes several contributions. We first extract and analyze implicit weighted brand–brand networks, representing interactions among users and brands, from a large dataset. We examine network properties and community structures and propose a framework combining text and network analyses to find target audiences. As a part of this framework, we develop a hierarchical community detection algorithm to identify a set of brands that are closely related to a specific brand. This latter brand is referred to as the “focal brand.” We also develop a global ranking algorithm to calculate brand influence and select influential brands from this set of closely related brands. This is then combined with sentiment analysis to identify target users from these selected brands. To process large-scale datasets and networks, we implement several MapReduce-based algorithms. Finally, we design a novel evaluation technique to test the effectiveness of our targeting framework. Experiments conducted with Facebook data show that our framework provides significant performance improvements in identifying target audiences for focal brands.[more]
|Predicting Asthma‐Related Emergency Department Visits Using Big Data
By Ram, S. ; Zhang, W. ; Williams, M. ; Pengetnze, Y.; Journal of Biomedical and Health Informatics IEEE; July 8, 2015
Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of leveraging the non-traditional, digital information to predict the volume of asthma‐related emergency room visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. The preliminary findings show that our model can predict the number of asthma ED visits based on near‐real‐time environmental and social media data with approximately 70% precision. The findings could help hospital emergency departments plan better with regard to staffing and equipment availability in a flexible manner. They can also provide early warning signals to the people at risk for asthma adverse events, and enable timely, proactive, and targeted preventive and therapeutic interventions. [more]
|Predicting Location-Based Sequential Purchasing Events by Using Spatial, Temporal, and Social Patterns
By Yun W.; Ram, S.; Intelligent Systems, IEEE; May-June 2015.
Location-based sequential event prediction is an interesting problem with many real-world applications. For example, knowing when and where people will use certain kinds of services could enable the development of robust anticipatory systems. A key to this problem is in understanding the nature of the process from which sequential data arises. Usually, human behavior exhibits distinct spatial, temporal, and social patterns. The authors examine three kinds of patterns extracted from sequential purchasing events and propose a novel model that captures contextual dependencies in spatial sequence, customers' temporal preferences, and social influence via an implicit network. Their model outperforms existing models based on evaluations using a real-world dataset of smartcard transaction records from a large educational institution with 13,753 students during a 10-month time period. [more]
| "Differentiating Telic-Atelic Data Semantics in Conceptual Design”
IEEE Transactions on Knowledge and Data Engineering, 2013 (with V.Khatri,R Snodgrass,P Terenziani).
|“Using a Network Analysis Approach for Organizing Social Bookmarks and Enabling Web Content Discovery”
ACM Transactions on MIS, 2013 (with W.Wei).
|Sharing News Articles Using 140 Characters: A Diffusion Analysis on Twitter
By Bhattacharya, D., Ram, S., Business Applications of Social Networking (BASNA 2012), Instanbul, Turkey, Aug 2012
Is it possible to effectively spread news articles to a large audience using 140 characters? How does the microblogging website Twitter get used as a platform for the news media agencies to create awareness about the articles they publish on a daily basis? Our study of the diffusion patterns of news articles from 12 popular news sources, including BBC, New York Times, and Mashable on Twitter reveals that a large number of users not only consume and comment on these news articles but also share them in different ways. Combining the methods of network and temporal analyses, we examine and report on how news articles diffuse on Twitter, and how different propagation mechanisms result in different lifespans for news articles. To be available in the Proceedings of the ASONAM 2012 Conference via IEEE Explore.
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- "Design Science in Information Systems Research" , MIS Quarterly, Volume 28, No. 1, March 2004, pp. 75-105, (with A. Hevner, S. March, J. Park). Abstract
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- " Semantic Modeling and Decision Support in Hydrology ", Special Issue on Remote Sensing and Decision Support Systems of Journal of Photogrammetric Engineering and Remote , Vol. 66, No. 10, October 2000, (with V. Khatri, Y. Hwang, and S. Yool).
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- Journal of Database Management, Fall 2006
Dr. Ram and Dr. Vijay Khatri are co-editors of a special issue on "Defining Eliciting and Using Data Semantics for Emerging Domains" of Journal of Database Management, 2006.
- Information Technology and Management, Fall 2006
Dr. Ram and Dr. H. Zhao are co-editors of a special issue on "Semantic Web: Opportunities and Challenges" of Information Technology and Management , scheduled to come out in 2006.
- 25th ER Conference, Nov. 6-9, 2006
25th International Conference on Conceputal Modeling, ER 2006, will be held in Tucson, Arizona for four days starting from November 6th in 2006.