16 research outputs found
The egalitarian effect of search engines
Search engines have become key media for our scientific, economic, and social
activities by enabling people to access information on the Web in spite of its
size and complexity. On the down side, search engines bias the traffic of users
according to their page-ranking strategies, and some have argued that they
create a vicious cycle that amplifies the dominance of established and already
popular sites. We show that, contrary to these prior claims and our own
intuition, the use of search engines actually has an egalitarian effect. We
reconcile theoretical arguments with empirical evidence showing that the
combination of retrieval by search engines and search behavior by users
mitigates the attraction of popular pages, directing more traffic toward less
popular sites, even in comparison to what would be expected from users randomly
surfing the Web.Comment: 9 pages, 8 figures, 2 appendices. The final version of this e-print
has been published on the Proc. Natl. Acad. Sci. USA 103(34), 12684-12689
(2006), http://www.pnas.org/cgi/content/abstract/103/34/1268
Agents, Bookmarks and Clicks: A topical model of Web traffic
Analysis of aggregate and individual Web traffic has shown that PageRank is a
poor model of how people navigate the Web. Using the empirical traffic patterns
generated by a thousand users, we characterize several properties of Web
traffic that cannot be reproduced by Markovian models. We examine both
aggregate statistics capturing collective behavior, such as page and link
traffic, and individual statistics, such as entropy and session size. No model
currently explains all of these empirical observations simultaneously. We show
that all of these traffic patterns can be explained by an agent-based model
that takes into account several realistic browsing behaviors. First, agents
maintain individual lists of bookmarks (a non-Markovian memory mechanism) that
are used as teleportation targets. Second, agents can retreat along visited
links, a branching mechanism that also allows us to reproduce behaviors such as
the use of a back button and tabbed browsing. Finally, agents are sustained by
visiting novel pages of topical interest, with adjacent pages being more
topically related to each other than distant ones. This modulates the
probability that an agent continues to browse or starts a new session, allowing
us to recreate heterogeneous session lengths. The resulting model is capable of
reproducing the collective and individual behaviors we observe in the empirical
data, reconciling the narrowly focused browsing patterns of individual users
with the extreme heterogeneity of aggregate traffic measurements. This result
allows us to identify a few salient features that are necessary and sufficient
to interpret the browsing patterns observed in our data. In addition to the
descriptive and explanatory power of such a model, our results may lead the way
to more sophisticated, realistic, and effective ranking and crawling
algorithms.Comment: 10 pages, 16 figures, 1 table - Long version of paper to appear in
Proceedings of the 21th ACM conference on Hypertext and Hypermedi
Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
Traffic flow count data in networks arise in many applications, such as
automobile or aviation transportation, certain directed social network
contexts, and Internet studies. Using an example of Internet browser traffic
flow through site-segments of an international news website, we present
Bayesian analyses of two linked classes of models which, in tandem, allow fast,
scalable and interpretable Bayesian inference. We first develop flexible
state-space models for streaming count data, able to adaptively characterize
and quantify network dynamics efficiently in real-time. We then use these
models as emulators of more structured, time-varying gravity models that allow
formal dissection of network dynamics. This yields interpretable inferences on
traffic flow characteristics, and on dynamics in interactions among network
nodes. Bayesian monitoring theory defines a strategy for sequential model
assessment and adaptation in cases when network flow data deviates from
model-based predictions. Exploratory and sequential monitoring analyses of
evolving traffic on a network of web site-segments in e-commerce demonstrate
the utility of this coupled Bayesian emulation approach to analysis of
streaming network count data.Comment: 29 pages, 16 figure
Local Political Information on the Web: The Case of the 2007 Philadelphia Mayoral Campaign
Scholars and pundits have widely discussed the decline of print journalism, but there has been very little empirical research focused on examining online alternatives. This article utilizes a unique sample of online local political content related to the 2007 Philadelphia mayoral campaign to address this void. A content analysis of this dataset has three objectives: to depict the range of sources of online local political information (LPI) available to Philadelphians, to compare the LPI provided by these various sources, and to determine the amount and provenance of the original LPI that is available on the web. New media sources of LPI may be far from maturity, but this article finds that they do exist and are a viable resource for citizens
The Analysis of Research on Internet Marketing
The targeted development of internet marketing faces the dynamic environment and its alterations that occur both – in business terms and conditions and in the use of etools and applicable concepts. The growing importance of internet marketing is indicated by a growth in emarkets, an increasing number of new generation consumers, investments of organizations in ebased decisions, a strong interest of scientists representing different fields as well as by widening the base of knowledge related to the above introduced phenomenon. Despite a continuously increasing amount of scientific literature on Internet marketing, this area of research is still at the initial stage. The article is aimed at analysing the fields of ongoing researches into Internet marketing and at identifying the areas required for deeper examination. The article has applied for the methods such as a comparative analysis and summary of scientific literature, information comparison and techniques for grouping and the graphical visualization of information