321 research outputs found

    The state-of-the-art in personalized recommender systems for social networking

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    With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

    Survey on Link Prediction and Page Ranking In Blogs S.Geetha

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    This paper presents a study of the various aspects of link prediction and page ranking in blogs. Social networks have taken on a new eminence from the prospect of the analysis of social networks, which is a recent area of research which grew out of the social sciences as well as the exact sciences, especially with the computing capacity for mathematical calculations and even modelling which was previously impossible. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. Link prediction has many applications, including recommending new items in online networks (e.g., products in eBay and Amazon, and friends in Face book), monitoring and preventing criminal activities in a criminal network, predicting the next web page users will visit, and complementing missing links in automatic web data crawlers. Page Rank is the technique used by Google to determine importance of page on the web. It considers all incoming links to a page as votes for Page Rank. Our findings provide an overview of social relations and we address the problem of page ranking and link prediction in networked data, which appears in many applications such as network analysis or recommended systems. Keywords- web log, social networks analysis, readership, link prediction, Page ranking. I

    Social Media and Information Overload: Survey Results

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    A UK-based online questionnaire investigating aspects of usage of user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy

    Deliberative Democracy, Perspective from Indo-Pacific Blogosphere: A Survey

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    Deliberation and communication within the national space have had numerous implications on how citizens online and offline perceive government. It has also impacted the relationship between opposition and incumbent governments in the Indo-Pacific region. Authoritarian regimes have historically had control over the dissemination of information, thereby controlling power and limiting challenges from citizens who are not comfortable with the status quo. Social media and blogs have allowed citizens of these countries to find a way to communicate, and the exchange of information continues to rise. The quest by both authoritarian and democratic regimes to control or influence the discussion in the public sphere has given rise to concepts like cybertroopers, congressional bloggers, and commentator bloggers, among others. Cybertroopers have become the de facto online soldiers of authoritarian regimes who must embrace democracy. While commentator and congressional bloggers have acted with different strategies, commentator bloggers educate online citizens with knowledgeable information to influence the citizens. Congressional bloggers are political officeholders who use blogging to communicate their positions on ongoing national issues. Therefore, this work has explored various concepts synonymous with the Indo-Pacific public sphere and how it shapes elections and democracy

    Writing to Learn: Blogging about Language Arts and Social Studies in a Grade 5 Classroom

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    Research has shown that writing to learn can support discipline-specific learning and thought development. Traditional writing strategies such as essays and journaling have been found to have a positive impact on recall of information, concept analysis and application. However, interaction with readers is not immediate with these methods. An environment where writers can immediately adapt to their readers’ feedback and become conversation partners for one another is the blogosphere. The purpose of this chapter is to describe how fifth-grade writers engaged in blog conversations with an audience beyond the classroom walls about their learning in language arts (LA) and social studies (SS) classes. The chapter also analyzes the ways in which feedback from the audience facilitated the fledgling writers’ “learning to write and writing to learn.

    Blog Analysis: An Exploration of French Students’ Perceptions Towards Foreign Cultures During Their Overseas Internships

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      Increasingly, tourism and hospitality university programs in France include internships which add a vocational dimension to the academic aspects of the course. These internships a) provide exposure to real world professional situations, b) add market value to the student experience, and c) offer a foothold for employment. The field of blog research is increasing in an effort to understand the social and economic context and impact of blogs: however, most existing studies only provide insight into English-language content. This paper explores French tourism and hospitality undergraduate student blogs—completed during their 4-6 month internships outside of France—as a manifestation of their knowledge. Qualitative analyses by means of text mining of the students’ posts, was used to assess their experiences and perceptions. Utilizing content analysis, 28 blogs were examined with the aim of understanding blogs and blogging under the theoretical framework of cultural perceptions. Results demonstrated that these internship blogs provide rich and authentic feedback; the blogs facilitate student monitoring while allowing for a wide cross-section of readers to assess different destination-based student internship experiences. The results showed that the “image” students projected of their destinations were chiefly positive, yet tended to conform to their preconceived ideas of these places. This study underscores the importance of empathy and appreciation when working in foreign contexts, because even though student internships were located all over the world, a tendency to believe in stereotypes of peoples and places remained. The findings a) shed light into the process of perception transformation during overseas work placements, and b) have practical and methodological implications for researchers and educators who are see academic blogging as a teaching and learning tool.De plus en plus, les programmes universitaires de tourisme et d’accueil en France comprennent des stages qui ajoutent une dimension vocationnelle aux aspects académiques du cours. Ces stages fournissent une exposition à des situations réelles du monde professionnel, constituent une valeur ajoutée à l’expérience d’apprentissage et offrent une possibilité de s’implanter dans le marché du travail. La recherche portant sur les blogues est un domaine en expansion qui cherche à comprendre le contexte et l’impact des blogues sur les plans sociaux et économiques. Toutefois, la plupart des études à date ne portent que sur le contenu de langue anglaise. Cet article se penche sur les blogues d’étudiants français au premier cycle dans un programme de tourisme et d’accueil, complétés alors qu’ils faisaient des stages de 4 à 6 mois à l’étranger. Ces blogues sont interprétés comme étant une manifestation des connaissances des étudiants. Des analyses qualitatives effectuées par la fouille de textes publiés par les étudiants sur leurs blogues ont servi dans l’évaluation de leurs expériences et leurs perceptions. Une analyse du contenu de 28 blogues a été effectuée pour comprendre les blogues et le blogging selon le cadre théorique des perceptions culturelles. Les résultats indiquent que les blogues animés pendant les stages offrent une rétroaction riche et authentique. Les blogues facilitent le suivi des étudiants tout en permettant à un large éventail de lecteurs l’accès à diverses expériences vécues par les étudiants pendant leurs stages. Les résultats ont révélé que l’image qu’ont projetée les étudiants de leurs destinations était surtout positive mais qu’elle tendait à correspondre aux idées préconçues qu’ils se faisaient de ces endroits. Cette étude souligne l’importance de l’empathie et de la reconnaissance dans les milieux de travail étrangers car, malgré le fait que les stages se sont déroulés partout au monde, la tendance de croire aux stéréotypes liés aux gens et aux pays demeuraient. Les résultats mettent en lumière le processus de la transformation des perceptions lors de stages de travail à l’étranger. Ils ont des incidences pratiques et méthodologiques pour les chercheurs et les enseignants qui voient en les blogues des outils d’enseignement et d’apprentissage

    Investigating Rumor Propagation with TwitterTrails

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    Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites like Twitter can sometimes eclipse accuracy and lead to the spread of false information. As a way to study and react to this trend, we introduce {\sc TwitterTrails}, an interactive, web-based tool ({\tt twittertrails.com}) that allows users to investigate the origin and propagation characteristics of a rumor and its refutation, if any, on Twitter. Visualizations of burst activity, propagation timeline, retweet and co-retweeted networks help its users trace the spread of a story. Within minutes {\sc TwitterTrails} will collect relevant tweets and automatically answer several important questions regarding a rumor: its originator, burst characteristics, propagators and main actors according to the audience. In addition, it will compute and report the rumor's level of visibility and, as an example of the power of crowdsourcing, the audience's skepticism towards it which correlates with the rumor's credibility. We envision {\sc TwitterTrails} as valuable tool for individual use, but we especially for amateur and professional journalists investigating recent and breaking stories. Further, its expanding collection of investigated rumors can be used to answer questions regarding the amount and success of misinformation on Twitter.Comment: 10 pages, 8 figures, under revie

    Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

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    Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

    A comparison of statistical machine learning methods in heartbeat detection and classification

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    In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

    Attitude Recognition Using Multi-resolution Cochleagram Features

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