Analysis and Forecasting of Trending Topics in Online Media Streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

Using Text Analytics to Derive Customer Service Management Benefits from Unstructured Data

Deriving value from structured data is now commonplace. The value of unstructured textual data, however, remains mostly untapped and often unrecognized. This article describes the text analytics journeys of three organizations in the customer service management area. Based on their experiences, we provide four lessons that can guide other organizations as they embark on their text analytics journeys.Click here for podcast summary (mp3)Click here for free 2-page executive summary (pdf)Click here for free presentation slides (pptx

Taking the paper out of news: A case study of Taloussanomat, Europe's first online-only newspaper

Using in-depth interviews, newsroom observation, and internal documents, this case study presents and analyses changes that have taken place at Finnish financial daily Taloussanomat since it stopped printing on 28 December 2007 to focus exclusively on digital delivery via the Web, email, and mobile. It reveals the savings that can be achieved when a newspaper no longer prints and distributes a physical product; but also the revenue lost from subscriptions and print advertising. The consequences of a newspaper's decision to go online-only are examined as they relate to its business model, website traffic, and editorial practice. The findings illustrate the extent to which the medium rather than the content it carries determines news consumption patterns, show the differing attention a newspaper and its online substitute command, and reveal the changes to working patterns journalists can expect in the online-only environment

Learning to Rank Academic Experts in the DBLP Dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with arXiv:1302.041

Finding video on the web

At present very little is known about how people locate and view videos. This study draws a rich picture of everyday video seeking strategies and video information needs, based on an ethnographic study of New Zealand university students. These insights into the participants’ activities and motivations suggest potentially useful facilities for a video digital library

Border Patrol: The Rise and Role of Fact-Checkers and Their Challenge to Journalists’ Normative Boundaries

Although most research to date has focused on leading U.S. fact-checkers, similar initiatives are gaining strength all over the world. This study draws on a globally disseminated questionnaire, plus interviews with fact-checkers on four continents, to examine their role and reach in relation to other journalistic enterprises, as well as the challenges they face. A conceptual framework of journalistic boundary-setting helps guide the exploration

A Survey, Taxonomy, and Analysis of Network Security Visualization Techniques

Network security visualization is a relatively new field and is quickly gaining momentum. Network security visualization allows the display and projection of the network or system data, in hope to efficiently monitor and protect the system from any intrusions or possible attacks. Intrusions and attacks are constantly continuing to increase in number, size, and complexity. Textually reading through log files or other textual sources is currently insufficient to secure a network or system. Using graphical visualization, security information is presented visually, and not only by text. Without network security visualization, reading through log files or other textual sources is an endless and aggravating task for network security analysts. Visualization provides a method of displaying large volume of information in a relatively small space. It also makes patterns easier to detect, recognize, and analyze. This can help security experts to detect problems that may otherwise be missed in reading text based log files. Network security visualization has become an active research field in the past six years and a large number of visualization techniques have been proposed. A comprehensive analysis of the existing techniques is needed to help network security designers make informed decisions about the appropriate visualization techniques under various circumstances. Moreover, a taxonomy of the existing visualization techniques is needed to classify the existing network security visualization techniques and present a high level overview of the field. In this thesis, the author surveyed the field of network security visualization. Specifically, the author analyzed the network security visualization techniques from the perspective of data model, visual primitives, security analysis tasks, user interaction, and other design issues. Various statistics were generated from the literatures. Based on this analysis, the author has attempted to generate useful guidelines and principles for designing effective network security visualization techniques. The author also proposed a taxonomy for the security visualization techniques. To the author’s knowledge, this is the first attempt to generate a taxonomy for network security visualization. Finally, the author evaluated the existing network security visualization techniques and discussed their characteristics and limitations. For future research, the author also discussed some open research problems in this field. This research is a step towards a thorough analysis of the problem space and the solution space in network security visualization