Nargis Pervin

Bio: Nargis Pervin is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 8, co-authored 20 publications receiving 180 citations. Previous affiliations of Nargis Pervin include National University of Singapore & Indian Institute of Information Technology Design & Manufacturing Kancheepuram.

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

Abstract: 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.

Mobilewalla: A Mobile Application Search Engine

Abstract: With the popularity of mobile apps on mobile devices based on iOS, Android, Blackberry and Windows Phone operating systems, the number of mobile apps in each of the respective native app stores are increasing in leaps and bounds. Currently there are almost 700,000 mobile apps across these four major native app stores. Due to such enormous number of apps, both the constituents in the app ecosytem, consumers and app developers, face problems in terms of ‘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, making it possible for users to discover their apps in the large number of available apps is a challenge. To address these issues, Mobilewalla(MW), provides an independent unbiased search engine 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.

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

Abstract: 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.

Factors Affecting Retweetability: An Event-Centric Analysis on Twitter

Abstract: In Twitter information primarily propagates through retweet mechanism. While a massive amount of tweets gets generated everyday, only a handful of them get retweeted widely. In this study, we have investigated the impact of user-roles in retweet phenomena. We have introduced the concept of “Information Diffusion Impact” (IDI) and identified three important user roles, namely “information starter”, “amplifier”, and “transmitter”. Retweetability has been modeled using IDI impact for different user roles along with the content features like presence of hashtag, URL etc. Further, the effect of a major event on the factors affecting retweetability has been investigated. Our findings demonstrate that retweetability is significantly affected by amplifiers and informationstarters and these effects change substantially due to event. We have also reexamined our model in another dataset of the Boston marathon bomb blast, 2013 and the outcome of this analysis is in good agreement with our findings from Japan earthquake dataset.

Takeoff and sustained success of apps in hypercompetitive mobile platform ecosystems : an empirical analysis

Abstract: Mobile smart-device ecosystems (e.g., iOS, Android) have democratized innovation and rejuvenated the mobile-device industry. Yet, two key participants of these ecosystems, developers and consumers, continue to face significant challenges. The market is inundated with more than a million apps making the discovery and assessment of apps a daunting task for consumers. The easy substitutability of apps and the ability of consumers to delete apps in their devices at the flick of a finger make app development a hypercompetitive turf for developers. This paper takes an important step in addressing competitive strategies in these hypercompetitive, but potentially lucrative innovation value chains. We conceptualized the takeoff and continued survival of apps as a function of app positioning, developer actions, and user engagement. Empirically validating our conceptualization using lifecycle data of 91,207 apps in the Apple iOS platform, we discuss our insights for competing in the hypercompetitive mobile smart-device platform ecosystems.

Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Abstract: Background: Social media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change. Objective: The aim of this study was to test the feasibility of designing, implementing, and evaluating a social media–enabled intervention for skin cancer prevention. Methods: A quasi-experimental feasibility study used social media (Twitter) to disseminate different message “frames” related to care in the sun and cancer prevention. Phase 1 utilized the Northern Ireland cancer charity’s Twitter platform (May 1 to July 14, 2015). Following a 2-week “washout” period, Phase 2 commenced (August 1 to September 30, 2015) using a bespoke Twitter platform. Phase 2 also included a Thunderclap, whereby users allowed their social media accounts to automatically post a bespoke message on their behalf. Message frames were categorized into 5 broad categories: humor, shock or disgust, informative, personal stories, and opportunistic. Seed users with a notable following were contacted to be “influencers” in retweeting campaign content. A pre- and postintervention Web-based survey recorded skin cancer prevention knowledge and attitudes in Northern Ireland (population 1.8 million). Results: There were a total of 417,678 tweet impressions, 11,213 engagements, and 1211 retweets related to our campaign. Shocking messages generated the greatest impressions (shock, n=2369; informative, n=2258; humorous, n=1458; story, n=1680), whereas humorous messages generated greater engagement (humorous, n=148; shock, n=147; story, n=117; informative, n=100) and greater engagement rates compared with story tweets. Informative messages, resulted in the greatest number of shares (informative, n=17; humorous, n=10; shock, n=9; story, n=7). The study findings included improved knowledge of skin cancer severity in a pre- and postintervention Web-based survey, with greater awareness that skin cancer is the most common form of cancer (preintervention: 28.4% [95/335] vs postintervention: 39.3% [168/428] answered “True”) and that melanoma is most serious (49.1% [165/336] vs 55.5% [238/429]). The results also show improved attitudes toward ultraviolet (UV) exposure and skin cancer with a reduction in agreement that respondents “like to tan” (60.5% [202/334] vs 55.6% [238/428]). Conclusions: Social media–disseminated public health messages reached more than 23% of the Northern Ireland population. A Web-based survey suggested that the campaign might have contributed to improved knowledge and attitudes toward skin cancer among the target population. Findings suggested that shocking and humorous messages generated greatest impressions and engagement, but information-based messages were likely to be shared most. The extent of behavioral change as a result of the campaign remains to be explored, however, the change of attitudes and knowledge is promising. Social media is an inexpensive, effective method for delivering public health messages. However, existing and traditional process evaluation methods may not be suitable for social media. [JMIR Public Health Surveill 2017;3(1):e14]

Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Abstract: Background: Social media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change. Objective: The aim of this study was to test the feasibility of designing, implementing, and evaluating a social media-enabled intervention for skin cancer prevention. Conclusions: Social media-disseminated public health messages reached more than 23% of the Northern Ireland population. A Web-based survey suggested that the campaign might have contributed to improved knowledge and attitudes toward skin cancer among the target population. Findings suggested that shocking and humorous messages generated greatest impressions and engagement, but information-based messages were likely to be shared most. The extent of behavioral change as a result of the campaign remains to be explored, however, the change of attitudes and knowledge is promising. Social media is an inexpensive, effective method for delivering public health messages. However, existing and traditional process evaluation methods may not be suitable for social media.

Social media in times of crisis: Learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response

Abstract: In recent times societal crises such as the coronavirus disease 2019 outbreak have given rise to a tension between formal ‘command and control’ and informal social media activated self-organising information and communication systems that are utilised for crisis management decision-making. Social media distrust affects the dissemination of disaster information as it entails shifts in media perception and participation but also changes in the way individuals and organisations make sense of information in critical situations. So far, a little considered notion in this domain is the concept of sense-giving. Originating from organisational theory, it is used to explain the mechanisms behind intentional information provision that fosters collective meaning creation. In our study, we seek to understand the potential impact of sense-giving from Twitter crisis communication generated during the Hurricane Harvey disaster event. Social network and content analyses performed with a dataset of 9,414,463 tweets yielded insights into how sense-giving occurs during a large-scale disaster event. Theoretically, we specified (1) perpetual sense-giving, which relies primarily on topical authority and frequency; as well as (2) intermittent sense-giving, which occurs from high value of message content and leverage of popularity, that is, retweets. Our findings emphasise the importance of information-rich actors in communication networks and the leverage of their influence in crises such as coronavirus disease 2019 to reduce social media distrust and facilitate sense-making.

Text and Context: Language Analytics in Finance

Abstract: This monograph surveys the technology and empirics of text analytics in finance. I present various tools of information extraction and basic text analytics. I survey a range of techniques of classification and predictive analytics, and metrics used to assess the performance of text analytics algorithms. I then review the literature on text mining and predictive analytics in finance, and its connection to networks, covering a wide range of text sources such as blogs, news, web posts, corporate filings, etc. I end with textual content presenting forecasts and predictions about future directions.