9,377 research outputs found
When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter
Assessing political conversations in social media requires a deeper
understanding of the underlying practices and styles that drive these
conversations. In this paper, we present a computational approach for assessing
online conversational practices of political parties. Following a deductive
approach, we devise a number of quantitative measures from a discussion of
theoretical constructs in sociological theory. The resulting measures make
different - mostly qualitative - aspects of online conversational practices
amenable to computation. We evaluate our computational approach by applying it
in a case study. In particular, we study online conversational practices of
German politicians on Twitter during the German federal election 2013. We find
that political parties share some interesting patterns of behavior, but also
exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i)
how complex cultural phenomena such as online conversational practices are
amenable to quantification and (ii) the way social media such as Twitter are
utilized by political parties.Comment: 10 pages, 2 figures, 3 tables, Proc. 8th International AAAI
Conference on Weblogs and Social Media (ICWSM 2014
Facilitating Mobile Music Sharing and Social Interaction with Push!Music
Push!Music is a novel mobile music listening and
sharing system, where users automatically receive
songs that have autonomously recommended
themselves from nearby players depending on similar
listening behaviour and music history. Push!Music
also enables users to wirelessly send songs between
each other as personal recommendations. We
conducted a two-week preliminary user study of
Push!Music, where a group of five friends used the
application in their everyday life. We learned for
example that the shared music in Push!Music became
a start for social interaction and that received songs in
general were highly appreciated and could be looked
upon as âtreatsâ
Gesture-Based Input for Drawing Schematics on a Mobile Device
We present a system for drawing metro map style schematics using a gesture-based interface. This work brings together techniques in gesture recognition on touch-sensitive devices with research in schematic layout of networks. The software allows users to create and edit schematic networks, and provides an automated layout method for improving the appearance of the schematic. A case study using the metro map metaphor to visualize social networks and web site structure is described
Going beyond your personal learning network, using recommendations and trust through a multimedia question-answering service for decision-support: A case study in the healthcare.
Social learning networks enable the sharing, transfer and enhancement of knowledge in the workplace that builds the ground to exchange informal learning practices. In this work, three healthcare networks are studied in order to understand how to enable the building, maintaining and activation of new contacts at work and the exchange of knowledge between them. By paying close attention to the needs of the practitioners, we aimed to understand how personal and social learning could be supported by technological services exploiting social networks and the respective traces reflected in the semantics. This paper presents a case study reporting on the results of two co-design sessions and elicits requirements showing the importance of scaffolding strategies in personal and shared learning networks. Besides, the significance of these strategies to aggregate trust among peers when sharing resources and decision-support when exchanging questions and answers. The outcome is a set of design criteria to be used for further technical development for a social semantic question and answer tool. We conclude with the lessons learned and future work
Emerging technologies for learning (volume 1)
Collection of 5 articles on emerging technologies and trend
Eigenvector localization as a tool to study small communities in online social networks
We present and discuss a mathematical procedure for identification of small
"communities" or segments within large bipartite networks. The procedure is
based on spectral analysis of the matrix encoding network structure. The
principal tool here is localization of eigenvectors of the matrix, by means of
which the relevant network segments become visible. We exemplified our approach
by analyzing the data related to product reviewing on Amazon.com. We found
several segments, a kind of hybrid communities of densely interlinked reviewers
and products, which we were able to meaningfully interpret in terms of the type
and thematic categorization of reviewed items. The method provides a
complementary approach to other ways of community detection, typically aiming
at identification of large network modules
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
- âŠ