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Hollerit B.,University of Graz | Kroll M.,Know Center | Strohmaier M.,University of Graz
WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web | Year: 2013

Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in tweets, this work is considered a first step to bring together buyers and sellers. In this work, we present an automatic method for detecting commercial intent in tweets where we achieve reasonable precision 57% and recall 77% scores. In addition, we provide insights into the nature and characteristics of tweets exhibiting commercial intent thereby contributing to our understanding of how people express commercial activities on Twitter.

Korner C.,Knowledge Management Institute | Kern R.,Know Center | Grahsl H.-P.,University of Graz | Strohmaier M.,Knowledge Management Institute and Know Center
HT'10 - Proceedings of the 21st ACM Conference on Hypertext and Hypermedia | Year: 2010

While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they inuence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems. Copyright 2010 ACM.

Krogstie B.R.,Norwegian University of Science and Technology | Prilla M.,Ruhr University Bochum | Pammer V.,Know Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Reflective learning is a mechanism to turn experience into learning. As a mechanism for self-directed learning, it has been found to be critical for success at work. This is true for individual employees, teams and whole organizations. However, most work on reflection can be found in educational contexts, and there is only little work regarding the connection of reflection on individual, group and organization levels. In this paper, we propose a model that can describe cases of reflective learning at work (CSRL). The model represents reflective learning processes as intertwined learning cycles. In contrast to other models of reflective learning, the CSRL model can describe both individual and collaborative learning and learning that impacts larger parts of an organization. It provides terminology to describe and discuss motivations for reflective learning, including triggers, objectives for and objects of reflective learning. The paper illustrates how the model helps to analyse and differentiate cases of reflective learning at work and to design tool support for such settings. © 2013 Springer-Verlag.

Strohmaier M.,Knowledge Management Institute | Korner C.,Knowledge Management Institute | Kern R.,Know Center
Journal of Web Semantics | Year: 2012

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users' motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems. © 2012 Elsevier B.V. All rights reserved.

Parra D.,PUC Chile | Brusilovsky P.,University of Pittsburgh | Trattner C.,Know Center
International Conference on Intelligent User Interfaces, Proceedings IUI | Year: 2014

Research in recommender systems has traditionally focused on improving the predictive accuracy of recommendations by developing new algorithms or by incorporating new sources of data. However, several studies have shown that accuracy does not always correlate with a better user experience, leading to recent research that puts emphasis on Human- Computer Interaction in order to investigate aspects of the interface and user characteristics that influence the user experience on recommender systems. Following this new research this paper presents SetFusion, a visual user-controllable interface for hybrid recommender system. Our approach enables users to manually fuse and control the importance of recommender strategies and to inspect the fusion results using an interactive Venn diagram visualization. We analyze the results of two field studies in the context of a conference talk recommendation system, performed to investigate the effect of user controllability in a hybrid recommender. Behavioral analysis and subjective evaluation indicate that the proposed controllable interface had a positive effect on the user experience. © 2014 ACM.

Stocker A.,Joanneum Research | Tochtermann K.,Know Center
Communications in Computer and Information Science | Year: 2011

In this paper we present the results of our explorative multiple-case study investigating enterprise wikis in three Austrian cases. Our contribution was highly motivated from the ongoing discussion on Enterprise 2.0 in science and practice, but the lack of well-grounded empirical research on how enterprise wikis are actually designed, implemented and more importantly utilized. We interviewed 7 corporate experts responsible for wiki operation and about 150 employees supposed to facilitate their daily business by using the wikis. The combination of qualitative data from the expert interviews and quantitative data from the user survey allows generating very interesting insights. Our cross-case analysis reveals commonalities and differences on usage motives, editing behaviour, individual and collective benefits, obstacles, and more importantly, derives a set of success factors guiding managers in future wiki projects. © 2011 Springer-Verlag.

Stocker A.,Joanneum Research | Richter A.,University of Federal Defense Munich | Hoefler P.,Know Center | Tochtermann K.,German National Library of Economics Leibniz Information Center for Economics
Computer Supported Cooperative Work | Year: 2012

The purpose of this paper is to provide both application-oriented researchers and practitioners with detailed insights into conception, implementation, and utilization of intraorganizational wikis to support knowledge management and group work. Firstly, we report on three case studies and describe how wikis have been appropriated in the context of a concrete practice. Our study reveals that the wikis have been used as Knowledge Base, Encyclopedia and Support Base, respectively.We present the identified practices as a result of the wiki appropriation process and argue that due to their open and flexible nature these wikis have been appropriated according to the users' needs. Our contribution helps to understand how platforms support working practices that have not been supported by groupware before, or at least not in the same way. Secondly, three detailed implementation reports uncover many aspects of wiki projects, e.g., different viewpoints of managers and users, an investigation of other sources containing business-relevant information, and perceived obstacles to wiki projects. In this context, our study generates a series of lessons learned for people who intend to implement wikis in their own organizations, including the awareness of usage potential, the need for additional managerial support, and clear communication strategies to promote wiki usage. © 2012 Springer.

Kienreich W.,Know Center | Seifert C.,Know Center
Proceedings of the International Conference on Information Visualisation | Year: 2010

The advent of consumer-generated and social media has led to a continuous expansion and diversification of the media landscape. Media consumers frequently find themselves assuming the role of media analysts in order to satisfy personal information needs. We propose to employ Knowledge Visualization methods in support of complex media analysis tasks. In this paper, we describe an approach which depicts semantic relationships between key political actors using node-link diagrams. Our contribution comprises a force-directed edge bundling algorithm which accounts for semantic properties of edges, a technical evaluation of the algorithm and a report on a real-world application of the approach. The resulting visualization fosters the identification of high-level edge patterns which indicate strong semantic relationships. It has been published by the Austrian Press Agency APA in 2009. © 2010 IEEE.

Hoefler P.,Know Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Linked Data has become an essential part of the Semantic Web. A lot of Linked Data is already available in the Linked Open Data cloud, which keeps growing due to an influx of new data from research and open government activities. However, it is still quite difficult to access this wealth of semantically enriched data directly without having in-depth knowledge about SPARQL and related semantic technologies. The presented dissertation explores Linked Data interfaces for non-expert users, especially keyword search as an entry point and tabular interfaces for filtering and exploration. It also looks at the value chain surrounding Linked Data and the possibilities that open up when people without a background in computer science can easily access Linked Data. © 2013 Springer-Verlag Berlin Heidelberg.

Our work on author identification and author profiling is based on the question: Can the number and the types of grammatical errors serve as indicators for a specific author or a group of people? In order to detect the grammatical errors we base our approach on the output of the open-source library Language Tool. In the case of the author identification we transform the problem into a statistical test, where an unknown document is written by another author when the distribution of grammatical errors deviated from documents of a reference corpus. For author profiling we implemented an instance based classification approach, namely a k-NN classifier, in combination with a Language Model where a text is assigned to a specific age or gender group where the according reference corpus contains the closest match. In the evaluation we found that for both scenarios grammatical errors do perform better than the baseline and do capture an aspect of a writing style, which is not contained in more traditional features, like stylometric features or word n-grams.

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