Detecting Emerging Areas in Social Streams

Detecting the emerging areas becomes interest by the fast development of social networks. As the information exchanged in social networks post include not only the text but also images, URLs and video therefore conventional-term-frequency-based approaches may not be appropriate in this context. Emergence of areas is focused by social aspects of these networks. To detect the emergence of new areas from the hundreds of users based on the responds in social network posts. A probability model is proposed for mentioning behavior of social networks by the number of mentions per post and the occurrence of users taking place in the mentions. The basic assumption is that a new emerging topic is something people feel like discussing, stating or forwarding the data further to their friends. In the proposed system the link anomaly model is combined with word based and text based approach. DOI: 10.17762/ijritcc2321-8169.15039

Association-free Tracking of Two Closely Spaced Targets

This paper introduces a new concept for tracking closely spaced targets in Cartesian space based on position measurements corrupted with additive Gaussian noise. The basic idea is to select a special state representation that eliminates the target identity and avoids the explicit evaluation of association probabilities. One major advantage of this approach is that the resulting likelihood function for this special problem is unimodal. Hence, it is especially suitable for closely spaced targets. The resulting estimation problem can be tackled with a standard nonlinear estimator. In this work, we focus on two targets in two-dimensional Cartesian space. The Cartesian coordinates of the targets are represented by means of extreme values, i.e., minima and maxima. Simulation results demonstrate the feasibility of the new approach

DETERMINING EVOLVING FOCUSES IN SOCIAL TORRENTS VIA LINK-DIFFERENCE EXPOSURE

We're concerned in recognition of emerging topics from social streams that are widely-used to generate automated news, otherwise uncover hidden market needs. Within our work we advise a probability representation of mentioning performance of social networking user, and suggest realizing emergence in the novel subject from anomalies which are measured completely through model. Our work is dependent upon concentrating on social content of documents plus mixing this having a change-point analysis. We spotlight on materialization of topics which are signalled by social highlights of scalping systems and concentrate on mentions of users, links among users which are created energetically completely through replies, mentions, furthermore to re-tweets. Tracking of topics were studied broadly in subject recognition furthermore to tracking as well as in this situation major task should be to additionally classify one document into among recognized topics so that you can realize that it's associated with nobody of recognized groups. The fundamental concept of our strategy is to pay attention to on social feature of posts which are reflected in mentioning conduct of users as opposed to textual contents

Exploring the evolution of research topics during the COVID-19 pandemic

The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Dataset (CORD-19) has systematically gathered scientific contributions for 2.5 years, by collecting and indexing over one million articles. Here, we present the CORD-19 Topic Visualizer (CORToViz), a method and associated visualization tool for inspecting the CORD-19 textual corpus of scientific abstracts. Our method is based upon a careful selection of up-to-date technologies (including large language models), resulting in an architecture for clustering articles along orthogonal dimensions and extraction techniques for temporal topic mining. Topic inspection is supported by an interactive dashboard, providing fast, one-click visualization of topic contents as word clouds and topic trends as time series, equipped with easy-to-drive statistical testing for analyzing the significance of topic emergence along arbitrarily selected time windows. The processes of data preparation and results visualization are completely general and virtually applicable to any corpus of textual documents - thus suited for effective adaptation to other contexts.Comment: 16 pages, 6 figures, 1 tabl

Fostering parent–child dialog through automated discussion suggestions

The development of early literacy skills has been critically linked to a child’s later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic success. CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills by supporting parents in shared reading with their children through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and, in combination with a common sense knowledge base, leverages this information to produce suggestions. Because of the unique challenges presented by our application, the suggestion generation method relies on a novel topic modeling method that is based on semantic graph topology. We conducted a user study in which we compared how delivering suggestions generated by our approach compares to expert-crafted suggestions. Our results show that our system can successfully improve engagement and parent–child reading practices in the absence of a literacy expert’s tutoring.National Science Foundation (U.S.) (Award Number 1117584

FINDING BURST CONTENT IN ONLINE STREAM VIA URL BASED 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 posts include not only text but also images, URLs, and videos. We focus on emergence of topics signaled by social aspects of theses networks. Specifically, we focus on mentions of user links between users that are generated dynamically (intentionally or unintentionally) through replies, mentions, and retweets. We propose a probability model of the mentioning behavior of a social network user, and propose to detect the emergence of a new topic from the anomalies measured through the model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social-network posts. We demonstrate our technique in several real data sets we gathered from Twitter. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as text-anomaly-based approaches, and in some cases much earlier when the topic is poorly identified by the textual contents in posts

Early online identification of attention gathering items in social media

Activity in social media such as blogs, micro-blogs, social net-works, etc is manifested via interaction that involves text, images, links and other information items. Naturally, some items attract more attention than others, expressed with large volumes of linking, commenting or tagging activity, to name a few examples. More-over, high attention can be indicative of emerging events, breaking news or generally indicate information items of interest to a vast set of people. The numbers associated with digital social activity are astonishing: in excess of millions of blog posts, tweets and forums updates per day, millions of tags in photos, news articles or blogs. Being able to identify information items that gather much attention in such a real time information collective is a challenging task. In this paper, we consider the problem of early online identifica-tion of items that gather a lot of attention in social media. We model social media activity using ISIS, a stochastic model for Interacting Streaming Information Sources, that intuitively captures the con-cept of attention gathering information items. Given the challenge of the information overload characterizing digital social activity, we present sequential statistical tests that enable early identifica-tion of attention gathering items. This effectively reduces the set of items one has to monitor in real time in order to identify pieces of information attracting a lot of attention. Experiments on real data demonstrate the utility of our model, as well as the efficiency and effectiveness of the proposed sequential statistical tests

Data Association for Topic Intensity Tracking

We present a unified model of what was traditionally viewed as two separate tasks: data association and intensity tracking of multiple topics over time. In the data association part, the task is to assign a topic (a class) to each data point, and the intensity tracking part models the bursts and changes in intensities of topics over time