Group News Research Articles

The Korean Independence News group who were summoned to the hearings of the House Un-American Activities Committee (HUAC) and their deportation cases in the 1950s

The Korean Independence News group who were summoned to the hearings of the House Un-American Activities Committee (HUAC) and their deportation cases in the 1950s

Hunting rare rusts in NW Wales

An earlier version of this article appeared in the Herefordshire Fungus Survey Group's News Sheet 15 (2008). We are grateful to the editor for permission to use it here.

Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature

Increase in the number of documents in the corpuses like News groups, government organizations, internet and digital libraries, have led to greater complexity in categorizing and retrieving them. Incorporating semantic features will improve the accuracy of retrieving documents through the method of clustering and which will also pave the way to organize and retrieve the documents more efficiently, from the large available corpuses. Even though clustering based on semantics enhances the quality of clusters, scalability of the system still remains complicated. In this paper, three dynamic document clustering algorithms, namely: Term frequency based MAximum Resemblance Document Clustering (TMARDC), Correlated Concept based MAximum Resemblance Document Clustering (CCMARDC) and Correlated Concept based Fast Incremental Clustering Algorithm (CCFICA) are proposed. From the above three proposed algorithms the TMARDC algorithm is based on term frequency, whereas, the CCMARDC and CCFICA are based on Correlated terms (Terms and their Related terms) concept extraction algorithm. The proposed algorithms were compared with the existing static and dynamic document clustering algorithms by conducting experimental analysis on the dataset chosen from 20Newsgroups and scientific literature. F-measure and Purity have been considered as metrics for evaluating the performance of the algorithms. The experimental results demonstrate that the proposed algorithm exhibit better performance, compared to the four existing algorithms for document clustering.

Cryptography: a Mathematical Approach

Networking is a way of connecting geographically distributed devices so as to communicate and share resources such as printers, files etc. Nowadays, Networking is extensively used in banking sector, e–commerce, social networking sites, news groups, sharing of software, sharing of valuable information among others and so many more. The unauthorized access to these networks is restrained by the administrator who apply and use various provisions and policies which is termed as network securities. Network security refers to any activity designed to protect network. Network security is an extremely valuable and procreative amenity. A threat exists whenever the security is perceived to get breached and vulnerability likely to be exploited. To enhance and strengthen a safeguard mechanism in protecting information multiple layers of security are implemented in our algorithm instead of adopting a single layer which at times may fall through.

Modeling and broadening temporal user interest in personalized news recommendation

User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user’s fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user’s preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user’s profile. To activate users’ reading motivations, a successful recommender needs to introduce “somewhat novel” articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user–item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.

How did the meanings constructed by Taiwanese pre-service teachers from educational news media affect their beliefs?

The study investigates how Taiwanese pre-service teachers make meaning after reading positive or negative news coverage of educational events and how they construct meaning impact their pedagogical beliefs. An experiment with a two-group pre-test–post-test design, consisting of a positive and negative news group, is conducted. Multimedia news stories of seven educational events were selected and evaluated as positive or negative materials. In total, 215 pre-service teachers were assigned randomly to one of the two groups. Analytical results reveal that pre-service teachers who read positive news preferred inference to construct meaning. Their pedagogical beliefs did not differ when they used interpretation or inference. For negative news, pre-service teachers preferred interpretation; however, belief scores using interpretation were lower than those using inference. Specifically, meanings constructed using interpretation when reading negative news about school administrative engagement severely weakens their pedagogical beliefs. Pre-service teachers should be given sufficient material and training to rationally deal with a wide range of news stories, especially negative educational news stories.

Semi-supervised learning via manifold regularization

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization (EM) algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

Language use depending on news frame and immigrant origin

The purpose of this study was to analyze the effect of the media on individuals' specific language use in relation to a news story on immigration: the influence of the news frame and group cue. Abstraction, complexity of language use, and negative affective language were evaluated. The 523 participants were randomly distributed to each of the four experimental conditions: news frame (crime versus economic contribution) by group cue (geographical origin of the immigrants involved: Moroccans versus Latin Americans). Through content analysis of the ideas and reflections that arose after the participants read the different news stories, using the Linguistic Category Model (LCM; Semin & Fiedler, 1991) to measure abstract language and the Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, & Francis, 2007) to analyze complex language and negative affective language, it emerged that abstract language and negative affective language were more frequent in the participants assigned to the news frame on crime. Complex language was more commonly used when the news frame referred to the economic contribution of immigrants. Regression analyses showed the mediating role of attitude to immigration in the effects of news frame on negative affective language. The bootstrap method was used to assess the magnitude of the indirect effect. A significant mediator effect was also found through structural equation modeling. Analyses of covariance showed one interaction between news frame and group cue: Among those who read the news story in a frame linking immigration to crime and Moroccan origin, abstract language was more characteristic. The results are discussed from the theoretical perspective of framing.

Responses to rhetorical questions in English and German Internet public news groups

This study investigates an area of interpersonal pragmatic meaning in which the specific framing conditions of public Internet message boards seem to be particularly relevant: patterns of responses to rhetorical questions as one aspect of the negotiation of the rhetorical force of such questions among the participants in a discussion. Rhetorical questions are complex linguistic structures which are described in terms of a set of characteristic pragmatic features, including their answerhood (Ilie 1994, 1999). An analysis of approximately 900 postings to 22 English and 22 German Internet discussion threads on a range of comparable topics showed that rhetorical questions are often subject to negotiation by the participants in an interaction. The responses to such rhetorical questions play a crucial role in this process. This work examines all textual responses to questions that were interpreted as rhetorical and describes their structural patterns and frequency of use as well as different types and functions in the English- and the German-speaking groups. It also discusses the different patterns of use against the background of more general observations on communicative styles and practices across the two language communities.

On the Anomalous Stock Price Response to Management Earnings Forecasts

Abstract: This paper examines stock price formation subsequent to management forecasts of quarterly earnings. In the post‐announcement period, we find a significant upward price drift for both good news forecasts and bad news forecasts. Combined with the asymmetry in the initial market response, the upward post‐guidance drift in stock prices is consistent with a reversal of an initial overreaction to managers’ bad news forecasts and a continuation of an initial underreaction to managers’ good news forecasts. This interpretation is supported by a negative (positive) relationship between the initial market response and the post‐guidance drift in the bad news (good news) group. The drift pattern is robust to issues arising from measurement. Trading strategies exploiting the post‐announcement drift suggest the existence of economically significant trading profits, net of estimated trading costs.

Table of Contents

Abstract NEWS Groups identify key steps to successful pro bono projects HSBC launches 5‐year, $100 million water/sanitation initiative SCUTTLEBUTT SPOTLIGHT Utilities industry Exelon Corporation Florida Power & Light Company Southern California Edison Company NV Energy Inc. Natural resources industry International Paper Company Shell Oil Company Louisiana‐Pacific Corp. Alpha Natural Resources Inc. GRANT ALERTS Arts and Culture Endangered Language Fund grants for tribal programs New England Native Arts grants California playwright grants Education Meemic Foundation grants for classroom projects Kauffman Dissertation Fellowships Class project grants for North Carolina teachers Environment Green Communities grants for green building design Patagonia environmental activism grants Health Shared Services Learning Community grants Religion Louisville Institute's Pastoral Study Project grants Social Science New York Community Trust's Social Work grants

On the Anomalous Stock Price Response to Management Earnings Forecasts

This paper examines stock price formation subsequent to management forecasts of quarterly earnings. In the post-announcement period, we find a significant upward price drift for both good news forecasts and bad news forecasts. Combined with the asymmetry in the initial market response, the upward post-guidance drift in stock prices is consistent with a reversal of an initial overreaction to managers’ bad news forecasts and a continuation of an initial under-reaction to managers’ good news forecasts. This interpretation is supported by a negative (positive) relationship between the initial market response and the post-guidance drift in the bad news (good news) group. The drift pattern is robust to issues arising from measurement. Trading strategies exploiting the post-announcement drift suggest the existence of economically significant trading profits, net of estimated trading costs.

‘Anyone can know’: Citizen journalism and the interpretive community of the mainstream press

In Madison, WI, two news groups – bloggers and local reporters – are squaring off, developing separate value systems and establishing protocols of intergroup activity. This study explored those framing values and documented individual role play within this Midwestern city’s information-producing community. An informal interpretive community of citizen journalists offers ways of knowing distinct from the way the press has traditionally practiced, negotiated and shared news stories. Interviews with citizens and professional journalists revealed convergences between these groups of news writers as well as dichotomies. This evidence showed that both the entrenched community of journalists and the emerging one of citizen news writers are framed by values of socially responsible missions, access to information, entitlement to knowledge and informal notions of professionalism. When ‘anyone can know’ – a quote from these interviews – the result is an adaptive organization of information producers that influence each other and redefine the aims, standards and ideology of journalism.

An Anti-Spam Engine using Fuzzy Logic with Enhanced Performance Tuning

E-mail has been considered as one of the most primary communication channels among the users with the rapid extension of internet. Unfortunately e-mail is also one of those tools, if not used properly could bring in irreparable consequences. But, recently the increasing popularity and fewer cost of sending an email makes it very easy to send unsolicited messages blindly to thousands of people at no cost at all by using easily available bulk mailing software and large lists of e-mail addresses typically harvested, even purchased or rented from web pages and news group archives. The Anti-Spam SMTP Proxy (ASSP) mail server engine is preferred one of the best from all the existing Mail filters as it is a platform independent open source-filtering engine and its file size is 565.5KB, which much less compared to other mail filters. General Terms Spam, Web security, Mail Filtering engine, Threshold analysis, Fuzzy logic, MTA

Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches

In this paper, we present two methods for classification of different social network actors (individuals or organizations) such as leaders (e.g., news groups), lurkers, spammers and close associates. The first method is a two-stage process with a fuzzy-set theoretic (FST) approach to evaluation of the strengths of network links (or equivalently, actor-actor relationships) followed by a simple linear classifier to separate the actor classes. Since this method uses a lot of contextual information including actor profiles, actor-actor tweet and reply frequencies, it may be termed as a context-dependent approach. To handle the situation of limited availability of actor data for learning network link strengths, we also present a second method that performs actor classification by matching their short-term (say, roughly 25 days) tweet patterns with the generic tweet patterns of the prototype actors of different classes. Since little contextual information is used here, this can be called a context-independent approach. Our experimentation with over 500 randomly sampled records from a twitter database consists of 441,234 actors, 2,045,804 links, 6,481,900 tweets, and 2,312,927 total reply messages indicates that, in the context-independent analysis, a multilayer perceptron outperforms on both on classification accuracy and a new F-measure for classification performance, the Bayes classifier and Random Forest classifiers. However, as expected, the context-dependent analysis using link strengths evaluated using the FST approach in conjunction with some actor information reveals strong clustering of actor data based on their types, and hence can be considered as a superior approach when data available for training the system is abundant.

Cognitive, Attitudinal, and Emotional Effects of News Frame and Group Cues, on Processing News About Immigration

This study aimed to contribute to the understanding of the mechanisms explaining the so-called framing effect. Experimental research (N = 355) was carried out with a 2 × 2 between-subjects factor design in which the news frame and group cues were manipulated in a news story on the consequences of the increase in immigration in Spain, and their impact on cognitive, attitudinal, and emotional variables was assessed. The results show that the type of news frame stressed in the report exerted significant cognitive, attitudinal, and emotional effects. It was also found that the emotional impact aroused by the news frame was conditioned by the incidental presence of information regarding the national or geographic origin of the immigrants in question. These results are consistent with the hypotheses posed and partially support the argument that the framing effect is a process governed by heuristic processing.

Newspapers’ narratives based on wire stories: Facsimiles of input?

Variation in news narratives can provide the empirical testing ground for investigating news production processes, such as selecting and adapting input stories from external sources. In taking a comparative approach this paper maps changes between final newspaper output and original input supplied by news agencies as well as differences in dealing with the same source material by various news groups. While some texts are taken over verbatim, others are scrambled, reduced in size or amended. Reasons for any divergence may be of a purely practical or an ideological nature. If particular patterns of editorial adjustments are detected with a regular occurrence, it can be assumed that they are ideologically inspired and/or that fixed editorial preferences or directives are at play. The analysis is based on a study of language and ideology in news accounts about the Hong Kong transfer of sovereignty from the British Crown to China in 1997, which as a major international news event garnered worldwide media attention and had an immediate news relevance for Taiwan. For this paper's purpose I look into a set of foreign news agencies’ articles adopted by two Taiwanese English-language newspapers, the input versions of which were retrieved from the Associated Press archives.

Text stream clustering algorithm based on adaptive feature selection

Text steam analysis is now of great importance and practical value today. It has several applications such as news group filtering, topic detection & tracking (TDT), user characterized recommendation etc. Clustering is one of the most important methods of analyzing text stream. However, most text stream clustering algorithms rarely consider the possible change of features during a long-time of clustering, which is usually the case, leading to unsatisfactory results of the clustering system. The paper mainly focuses on the problem of adaptive feature selection for clustering text stream. A validity index based method of adaptive feature selection is proposed, incorporating with which a new text stream clustering algorithm is developed. During the clustering process, threshold of cluster valid index is used to automatically trigger feature re-selection in order to ensure the validity of clustering. The experiment using Reuters-21578 text set as the text source shows that the clustering algorithm reaches reasonable results of high quality.

An Empirical Survey of Technology Application in Teaching Geography in Nigerian Secondary Schools

The main thrust of this study was to find out the extent of application of the new technologies in teaching and learning Geography in secondary schools in Nigeria. The present study is very important and necessary because many teachers are still very apprehensive about using the new technologies in instruction. In addition is the fact that, there are little empirical data on the level of preparedness and the extent of utilization of ICTs in instruction in Nigerian secondary schools. This study was therefore carried out to provide empirical data on the extent of the integration of the new technology in teaching and learning Geography in Nigerian Secondary Schools. The study employed the descriptive survey design. The sample for the study is made up of 214 Geography teachers drawn from secondary schools in Osun State. The schools and the teachers were selected through stratified sampling techniques based on school types, location, Local Education Area and gender. A structured questionnaire was used to collect data from the participants. It was divided into five sections and was validated using construct validity. The co-efficient of reliability was 0.68. The findings showed that 55% of Geography teachers had access to computer but did not have the pre-requisite ICT skills. Out of the modern technologies available for teaching Geography, the most commonly used are: instructional television (54%), instruction radio (59%) and video (59%).Other findings showed that 54% of Geography teachers do not know the instructional value of CDROM/ interactive web packages available free for teaching Geography. Not only this, 84% of the teachers also rarely use the news groups .While 42% rarely make use of multi- media presentation in teaching Geography. Where as many of these facilities are available free on the web for teachers use. Lastly, lack of skills and cost of utilization ranked highest as one of the factors preventing teachers from using the new technologies in teaching Geography. KEY WORDS Information and Communication Technologies, Multi-media, instructivist/constructivist approaches, Internet, news group

Detecting relationships among categories using text classification

AbstractDiscovering relationships among concepts and categories is crucial in various information systems. The authors' objective was to discover such relationships among document categories. Traditionally, such relationships are represented in the form of a concept hierarchy, grouping some categories under the same parent category. Although the nature of hierarchy supports the identification of categories that may share the same parent, not all of these categories have a relationship with each other—other than sharing the same parent. However, some “non‐sibling” relationships exist that although are related to each other are not identified as such. The authors identify and build a relationship network (relationship‐net) with categories as the vertices and relationships as the edges of this network. They demonstrate that using a relationship‐net, some nonobvious category relationships are detected. Their approach capitalizes on the misclassification information generated during the process of text classification to identify potential relationships among categories and automatically generate relationship‐nets. Their results demonstrate a statistically significant improvement over the current approach by up to 73% on 20 News groups 20NG, up to 68% on 17 categories in the Open Directories Project (ODP17), and more than twice on ODP46 and Special Interest Group on Information Retrieval (SIGIR) data sets. Their results also indicate that using misclassification information stemming from passage classification as opposed to document classification statistically significantly improves the results on 20NG (8%), ODP17 (5%), ODP46 (73%), and SIGIR (117%) with respect to F1 measure. By assigning weights to relationships and by performing feature selection, results are further optimized.