Nargis Pervin

Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams

Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query “what are people talking about?” Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a high-velocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find “hot” stories from high-rate Twitter-scale text streams.

Information Diffusion on Twitter: Everyone Has Its Chance, But All Chances Are Not Equal

Twitter is a Web 2.0 social network which attracted much attention recently for its usage as an alternative media for information diffusion. From the recent events in Arab countries, to natural disaster such as earthquakes or tsunamis, Twitter has proven to be a credible alternative to traditional means of information diffusion. Relatively few works have been done on this question of information diffusion, and in particular on the relative importance of different kind of users on this question. In this paper, we show that all users are not equal on the aspect of information diffusion. By investigating thoroughly the retweet chain lengths of users on a large dataset, we found that the number of followers of users plays an important role in their capacity to propagate information. From our observations we propose a very simple model, which is accurate enough to generate realistic length of retweet chains on the network. We consequently show, by studying a Twitter dataset centered on the Japanese Earthquake and Tsunami in March 2011, that such a crisis impact greatly the propagation of information. Finally, we use our results to discuss on the means of improving information diffusion to reach targeted users.

Localized algorithm for connected set cover partitioning in wireless sensor networks

In this paper, given a random distribution of sensor nodes, we pose the problem of finding maximum number of connected set covers such that each set can guarantee the required coverage of the region of interest. It requires just a one-time computation during initialization. Once the connected set covers are known, the sets may remain active in a round robin fashion to cover the region enhancing the life time of the network significantly. Firstly, two centralized greedy algorithms have been proposed to solve the problem from two different view points. But since centralized algorithms are not suitable for large self-organized sensor networks, a localized algorithm has been proposed finally that uses only local information at individual nodes to find a solution. Simulation studies show that these algorithms can enhance the network lifetime manifold, and most interestingly the performance of the distributed algorithm is comparable with the centralized ones in terms of number of partitions though it requires much less computation and communication overhead.

A Mobile App Search Engine

With the popularity of mobile apps on mobile devices based on iOS, Android, Blackberry and Windows Phone operating systems, the numbers of mobile apps in each of the respective native app stores are increasing in leaps and bounds. Currently there are close to one million mobile apps across these four major native app stores. Due to the enormous number of apps, both the constituents in the app ecosytem, consumers and app developers, face problems in ‘app discovery’. For consumers, it is a daunting task to discover the apps they like and need among the huge number of available apps. Likewise, for developers, enabling their apps to be discovered is a challenge. To address these issues, Mobilewalla (MW) an app search engine provides an independent unbiased search for mobile apps with semantic search capabilities. It has also developed an objective scoring mechanism based on user and developer involvement with an app. The scoring mechanism enables MW to provide a number of other ways to discover apps—such as dynamically maintained ‘hot’ lists and ‘fast rising’ lists. In this paper, we describe the challenges of developing the MW platform and how these challenges have been mitigated. Lastly, we demonstrate some of the key functionalities of MW.

Hashtag Popularity on Twitter: Analyzing Co-occurrence of Multiple Hashtags

Hashtags increase the reachability of a tweet to manifolds and consequently, has the potential to create a wider market for brands. The frequent use of a hashtag features it in the Twitter trending list. In this study we want to understand what contributes to the popularity of a hashtag. Further, hashtags generally come in groups in a tweet. In fact, an investigation on a real world dataset of Great Eastern Japan Earthquake reveals that 50 % of hashtags appear in a tweet with at least another hashtag. How this co-occurrence of hashtags affects its popularity is also not addressed heretofore, which is the focus herein. Results indicate that if a hashtag appears with one or more other similar hashtags, popularity of the hashtag increases. In contrast, if a hashtag appears with dissimilar hashtags, popularity of the focal hashtag decreases. The results reverse when dissimilar hashtags come along with a URL.

Food for Thought: Managing Secondary Data for Research

Social science researchers are strongly motivated to understand the world of business and its associated phenomena. In contrast to pure science research, social science studies combine strong narratives with empirical or analytical investigation. Such research is enticing, invigorating, and essential in current academic and practitioner domains, but each aspect is challenging and resembles a maze. Researchers must navigate diverse paths to identify appropriate theory, concepts, data sources, and knowledge for analysing and understanding social science phenomena. In this chapter, the authors explore new sources of data and the challenges of using them. Drawing upon personal experiences, as empirical researchers, the authors make recommendations about the use of secondary data.