Vom Gatekeeping zum Gatewatching : Modelle der journalistischen Vermittlung im Internet

Traditionelle journalistische Prozeduren der Inhaltserstellung und -vermittlung lassen sich in Presse und Rundfunk in erster Linie durch den Prozess des Gatekeeping charakterisieren. Im Internet findet sich jedoch zunehmend ein anderer Ansatz, der in Analogie zu dem traditionellen Begriff als Gatewatching beschrieben werden kann. In diesem Text werden die Besonderheiten des Gatewatchings herausgearbeitet, vor allem die multiperspektivische Form der Berichterstattung, und die wichtigsten Implikationen einer Bewegung vom Gatekeeping zum Gatewatching im Nachrichtenjournalismus analysiert.

Bringing the internet down to earth : emerging spaces of locative media

Web 1.0 referred to the early, read-only internet; Web 2.0 refers to the ‘read-write web’ in which users actively contribute to as well as consume online content; Web 3.0 is now being used to refer to the convergence of mobile and Web 2.0 technologies and applications. One of the most important developments in mobile 3.0 is geography: with many mobile phones now equipped with GPS, mobiles promise to “bring the internet down to earth” through geographically-aware, or locative media. The internet was earlier heralded as “the death of geography” with predictions that with anyone able to access information from anywhere, geography would no longer matter. But mobiles are disproving this. GPS allows the location of the user to be pinpointed, and the mobile internet allows the user to access locally-relevant information, or to upload content which is geotagged to the specific location. It also allows locally-specific content to be sent to the user when the user enters a specific space. Location-based services are one of the fastest-growing segments of the mobile internet market: the 2008 AIMIA report indicates that user access of local maps increased by 347% over the previous 12 months, and restaurant guides/reviews increased by 174%. The central tenet of cultural geography is that places are culturally-constructed, comprised of the physical space itself, culturally-inflected perceptions of that space, and people’s experiences of the space (LeFebvre 1991). This paper takes a cultural geographical approach to locative media, anatomising the various spaces which have emerged through locative media, or “the geoweb” (Lake 2004). The geoweb is such a new concept that to date, critical discourse has treated it as a somewhat homogenous spatial formation. In order to counter this, and in order to demonstrate the dynamic complexity of the emerging spaces of the geoweb, the paper provides a topography of different types of locative media space: including the personal/aesthetic in which individual users geotag specific physical sites with their own content and meanings; the commercial, like the billboards which speak to individuals as they pass in Minority Report; and the social, in which one’s location is defined by the proximity of friends rather than by geography.

Creativity, Innovation and the Internet

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Mining Negative Relevance Feedback for Information Filtering

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

An effective model of using negative relevance feedback for information filtering

Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.

State-of-the-Art Review on Opinion Mining from Online Customers’ Feedback

Dealing with the ever-growing information overload in the Internet, Recommender Systems are widely used online to suggest potential customers item they may like or find useful. Collaborative Filtering is the most popular techniques for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating about a service provider, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service providers’. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a state of the art review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and sub-categories. Each of those steps is analyzed with their strength and limitations in this paper.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Tag based collaborative filtering for recommender systems

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user’s important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.

Collaborative Filtering Recommender Systems based on Popular Tags

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Internetalisation : the Internet's influence on international market growth in the firm's outward internationalisation process

It has been suggested that the Internet is the most significant driver of international trade in recent years to the extent that the term =internetalisation‘ has been coined (Bell, Deans, Ibbotson & Sinkovics, 2001; Buttriss & Wilkinson, 2003). This term is used to describe the Internet‘s affect on the internationalisation process of the firm. Consequently, researchers have argued that the internationalisation process of the firm has altered due to the Internet, hence is in need of further investigation. However, as there is limited research and understanding, ambiguity remains in how the Internet has influenced international market growth. Thus, the purpose of this study was to explore how the Internet influences firms‘ internationalisation process, specifically, international market growth. To this end, Internet marketing and international market growth theories are used to illuminate this ambiguity in the body of knowledge. Thus, the research problem =How and why does the Internet influence international market growth of the firm’ is justified for investigation. To explore the research question a two-stage approach is used. Firstly, twelve case studies were used to evaluate key concepts, generate hypotheses and to develop a model of Internetalisation for testing. The participants held key positions within their firm, so that rich data could be drawn from international market growth decision makers. Secondly, a quantitative confirmation process analysed the identified themes or constructs, using two hundred and twenty four valid responses. Constructs were evaluated through an exploratory factor analysis, confirmatory factor analysis and structural equation modelling process. Structural equation modelling was used to test the model of =internetalisation‘ to examine the interrelationships between the internationalisation process components: information availability, information usage, interaction communication, international mindset, business relationship usage, psychic distance, the Internet intensity of the firm and international market growth. This study found that the Internet intensity of the firm mediates information availability, information usage, international mindset, and business relationships when firms grow in international markets. Therefore, these results provide empirical evidence that the Internet has a positive influence on international information, knowledge, entrepreneurship and networks and these in turn influence international market growth. The theoretical contributions are three fold. Firstly, the study identifies a holistic model of the impact the Internet has had on the outward internationalisation of the firm. This contribution extends the body of knowledge pertaining to Internet international marketing by mapping and confirming interrelationships between the Internet, internationalisation and growth concepts. Secondly, the study highlights the broad scope and accelerated rate of international market growth of firms. Evidence that the Internet influences the traditional and virtual networks for the pursuit of international market growth extends the current understanding. Thirdly, this study confirms that international information, knowledge, entrepreneurship and network concepts are valid in a single model. Thus, these three contributions identify constructs, measure constructs in a multi-item capacity, map interrelationships and confirm single holistic model of ‗internetalisation‘. The main practical contribution is that the findings identified information, knowledge and entrepreneurial opportunities for firms wishing to maximise international market growth. To capitalise on these opportunities suggestions are offered to assist firms to develop greater Internet intensity and internationalisation capabilities. From a policy perspective, educational institutions and government bodies need to promote more applied programs for Internet international marketing. The study provides future researchers with a platform of identified constructs and interrelationships related to internetalisation, with which to investigate. However, a single study has limitations of generalisability; thus, future research should replicate this study. Such replication or cross validation will assist in the verification of scales used in this research and enhance the validity of causal predications. Furthermore, this study was undertaken in the Australian outward-bound context. Research in other nations, as well as research into inbound internationalisation would be fruitful.