550 research outputs found
TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 2: Small World Problem
We will discuss several examples and research efforts related to the small world problem and set the ground for our discussion of network theory and social network analysis.
Readings: An Experimental Study of the Small World Problem, J. Travers and S. Milgram Sociometry 32 425-443 (1969) [Protected Access]
Optional: The Strength of Weak Ties, M.S. Granovetter The American Journal of Sociology 78 1360--1380 (1973) [Protected Access]
Optional: Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network, J. Leskovec and E. Horvitz MSR-TR-2006-186. Microsoft Research, June 2007. [Web Link, the most recent and comprehensive study on the subject!]
Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science
Improving Reachability and Navigability in Recommender Systems
In this paper, we investigate recommender systems from a network perspective
and investigate recommendation networks, where nodes are items (e.g., movies)
and edges are constructed from top-N recommendations (e.g., related movies). In
particular, we focus on evaluating the reachability and navigability of
recommendation networks and investigate the following questions: (i) How well
do recommendation networks support navigation and exploratory search? (ii) What
is the influence of parameters, in particular different recommendation
algorithms and the number of recommendations shown, on reachability and
navigability? and (iii) How can reachability and navigability be improved in
these networks? We tackle these questions by first evaluating the reachability
of recommendation networks by investigating their structural properties.
Second, we evaluate navigability by simulating three different models of
information seeking scenarios. We find that with standard algorithms,
recommender systems are not well suited to navigation and exploration and
propose methods to modify recommendations to improve this. Our work extends
from one-click-based evaluations of recommender systems towards multi-click
analysis (i.e., sequences of dependent clicks) and presents a general,
comprehensive approach to evaluating navigability of arbitrary recommendation
networks
TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 3: Network Theory and Terminology
In this class, we will discuss network theory fundamentals, including concepts such as diameter, distance, clustering coefficient and others. We will also discuss different types of networks, such as scale-free networks, random networks etc.
Readings: Graph structure in the Web, A. Broder and R. Kumar and F. Maghoul and P. Raghavan and S. Rajagopalan and R. Stata and A. Tomkins and J. Wiener Computer Networks 33 309--320 (2000) [Web link, Alternative Link]
Optional: The Structure and Function of Complex Networks, M.E.J. Newman, SIAM Review 45 167--256 (2003) [Web link]
Original course at: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science
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
Social media in academia: how the Social Web is changing academic practice and becoming a new source for research data
For the last few decades, the Internet continually has changed scholarly workflows across disciplines. It has affected how scholars search for publications, retrieve information and communicate and distribute their own research findings. Online communication and collaboration influences academic institutions as well as academic publishers, science journalists and students. Within this special issue, we focus on social media and its influence on academic practice. (auhtor's abstract
Semantic Stability in Social Tagging Streams
One potential disadvantage of social tagging systems is that due to the lack
of a centralized vocabulary, a crowd of users may never manage to reach a
consensus on the description of resources (e.g., books, users or songs) on the
Web. Yet, previous research has provided interesting evidence that the tag
distributions of resources may become semantically stable over time as more and
more users tag them. At the same time, previous work has raised an array of new
questions such as: (i) How can we assess the semantic stability of social
tagging systems in a robust and methodical way? (ii) Does semantic
stabilization of tags vary across different social tagging systems and
ultimately, (iii) what are the factors that can explain semantic stabilization
in such systems? In this work we tackle these questions by (i) presenting a
novel and robust method which overcomes a number of limitations in existing
methods, (ii) empirically investigating semantic stabilization processes in a
wide range of social tagging systems with distinct domains and properties and
(iii) detecting potential causes for semantic stabilization, specifically
imitation behavior, shared background knowledge and intrinsic properties of
natural language. Our results show that tagging streams which are generated by
a combination of imitation dynamics and shared background knowledge exhibit
faster and higher semantic stability than tagging streams which are generated
via imitation dynamics or natural language streams alone
Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach
Portrayals of history are never complete, and each description inherently
exhibits a specific viewpoint and emphasis. In this paper, we aim to
automatically identify such differences by computing timelines and detecting
temporal focal points of written history across languages on Wikipedia. In
particular, we study articles related to the history of all UN member states
and compare them in 30 language editions. We develop a computational approach
that allows to identify focal points quantitatively, and find that Wikipedia
narratives about national histories (i) are skewed towards more recent events
(recency bias) and (ii) are distributed unevenly across the continents with
significant focus on the history of European countries (Eurocentric bias). We
also establish that national historical timelines vary across language
editions, although average interlingual consensus is rather high. We hope that
this paper provides a starting point for a broader computational analysis of
written history on Wikipedia and elsewhere
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