Department of Computer Science

Twitter is a popular micro-blogging service for sharing short messages called tweets. Tweets provide public opinion on various topics. Currently twitter presents search results in form of a flat list, sorted either by popularity or by recency. These search results limit the possibility of identifying diverse latent topics covered by the tweets. One way to better understand the tweets is to cluster them where each cluster depicts a latent topic. Suitable clustering algorithms are required to cluster streaming data and map new data into existing clusters. To address this, we propose in this paper a framework called INTWEEMS (INcremental clustering of TWEEt streaMS) which clusters tweets in real-time, adjusts new tweets into existing clusters (incrementally), and provides visualization of clusters that helps in identifying latent topics and sub-topics within the tweets. This paper describes the INTWEEMS framework and its implementation.

Social media has an impact on many aspects of human life ranging from sharing personal information to revolutionizing political systems of entire countries. One not so well studied aspect of social media is analyzing its usage and efficacy in healthcare, particularly in developing countries which lack state-of-the-art healthcare systems and processes. In such countries, social media may be used to facilitate patient-centric healthcare by involving the patient for fulfilling personal healthcare needs. This article provides an in-depth analysis of one such need, that is, how people use social media to request for blood donations. We study the request and dissemination behavior of people using social media to fulfill blood donation requests. We focus on twitter, and blood donation accounts in India. Our study reveals that each of the seven twitter accounts we studied have a large followership of more than 35,000 users on an average and receive a substantial number (more than 900) of donation requests in a day on an average. We analyze the requests in various ways to present an outlook for healthcare providers to make their systems more patient-centric through a better understanding of the needs of people requesting for blood donations. Our study also identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients. These systems can provide support for saving more lives by reducing the gap between blood donors and the people in need.

Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.

Wireless Sensor Networks (WSNs) are
memory and bandwidth limited networks whose main
goals are to maximize the network lifetime and
minimize the energy consumption and transmission
cost. To achieve these goals, dif ferent techniques of
compression and clustering have been used. However,
security is an open and major issue in WSNs for which
different approaches are used, both in centralized and
distributed WSNs' environments. This paper presents
an adaptive cryptographic scheme for secure
transmission of various sensitive parameters, sensed by
wireless sensors to the fusion center for further
processing in WSNs such as military networks. The
proposed method encrypts the sensitive captured data
of sensor nodes using various encryption procedures
(bitxor operation, bits shuffling, and secret key based
encryption) and then sends it to the fusion center.  At the
fusion center, the received encrypted data is decrypted
for taking further necessary actions. The experimental
results with complexity analysis, validate the
effectiveness and feasibility of the proposed method in
terms of security in WSNs.

Twitter is a popular micro-blogging service for sharing short messages called tweets. Tweets provide public opinion on various topics. Currently twitter presents search results in form of a flat list, sorted either by popularity or by recency. These search results limit the possibility of identifying diverse latent topics covered by the tweets. One way to better understand the tweets is to cluster them where each cluster depicts a latent topic. Suitable clustering algorithms are required to cluster streaming data and map new data into existing clusters. To address this, we propose in this paper a framework called INTWEEMS (INcremental clustering of TWEEt streaMS) which clusters tweets in real-time, adjusts new tweets into existing clusters (incrementally), and provides visualization of clusters that helps in identifying latent topics and sub-topics within the tweets. This paper describes the INTWEEMS framework and its implementation.