41 research outputs found
Twigraph: Discovering and Visualizing Influential Words between Twitter Profiles
The social media craze is on an ever increasing spree, and people are
connected with each other like never before, but these vast connections are
visually unexplored. We propose a methodology Twigraph to explore the
connections between persons using their Twitter profiles. First, we propose a
hybrid approach of recommending social media profiles, articles, and
advertisements to a user.The profiles are recommended based on the similarity
score between the user profile, and profile under evaluation. The similarity
between a set of profiles is investigated by finding the top influential words
thus causing a high similarity through an Influence Term Metric for each word.
Then, we group profiles of various domains such as politics, sports, and
entertainment based on the similarity score through a novel clustering
algorithm. The connectivity between profiles is envisaged using word graphs
that help in finding the words that connect a set of profiles and the profiles
that are connected to a word. Finally, we analyze the top influential words
over a set of profiles through clustering by finding the similarity of that
profiles enabling to break down a Twitter profile with a lot of followers to
fine level word connections using word graphs. The proposed method was
implemented on datasets comprising 1.1 M Tweets obtained from Twitter.
Experimental results show that the resultant influential words were highly
representative of the relationship between two profiles or a set of profile
What to Read Next? Challenges and Preliminary Results in Selecting Representative Documents
The vast amount of scientific literature poses a challenge when one is trying to understand a previously unknown topic. Selecting a representative subset of documents that covers most of the desired content can solve this challenge by presenting the user a small subset of documents. We build on existing research on representative subset extraction and apply it in an information retrieval setting. Our document selection process consists of three steps: computation of the document representations, clustering, and selection of documents. We implement and compare two different document representations, two different clustering algorithms, and three different selection methods using a coverage and a redundancy metric. We execute our 36 experiments on two datasets, with 10 sample queries each, from different domains. The results show that there is no clear favorite and that we need to ask the question whether coverage and redundancy are sufficient for evaluating representative subsets
Rethinking summarization and storytelling for modern social multimedia
Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to re-focus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanatio
The Structure of the EU Mediasphere
Background.
A trend towards automation of scientific research has recently resulted in what has been termed “data-driven inquiry” in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the humanities and the social sciences, particularly in those disciplines concerned with the analysis of text, due to the recent availability of millions of books and news articles in digital format. In the social sciences, the analysis of news media is done largely by hand and in a hypothesis-driven fashion: the scholar needs to formulate a very specific assumption about the patterns that might be in the data, and then set out to verify if they are present or not.
Methodology/Principal Findings.
In this study, we report what we think is the first large scale content-analysis of cross-linguistic text in the social sciences, by using various artificial intelligence techniques. We analyse 1.3 M news articles in 22 languages detecting a clear structure in the choice of stories covered by the various outlets. This is significantly affected by objective national, geographic, economic and cultural relations among outlets and countries, e.g., outlets from countries sharing strong economic ties are more likely to cover the same stories. We also show that the deviation from average content is significantly correlated with membership to the eurozone, as well as with the year of accession to the EU.
Conclusions/Significance.
While independently making a multitude of small editorial decisions, the leading media of the 27 EU countries, over a period of six months, shaped the contents of the EU mediasphere in a way that reflects its deep geographic, economic and cultural relations. Detecting these subtle signals in a statistically rigorous way would be out of the reach of traditional methods. This analysis demonstrates the power of the available methods for significant automation of media content analysis
Artificial boundary conditions for a transmission boundary-value problem for the time-harmonic Maxwell equations without displacement currents
Considered is a transmission boundary-value problem for the time- harmonic Maxwell equations without displacement currents. As transmission conditions the continuity of the tangential parts of the magnetic field H and the continuity of the normal components of the magnetization B=#mu#H are used herein. This problem, which is posed over all R"3, is then restricted to a bounded domain by introducing artificial boundary conditions. The author presents uniqueness and existence proofs for this problem using an integral equation approach and compare the results with those obtained in the ubounded case. (orig./AKF)SIGLEAvailable from TIB Hannover: RO 5810(85) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman