421,415 research outputs found
Why We Read Wikipedia
Wikipedia is one of the most popular sites on the Web, with millions of users
relying on it to satisfy a broad range of information needs every day. Although
it is crucial to understand what exactly these needs are in order to be able to
meet them, little is currently known about why users visit Wikipedia. The goal
of this paper is to fill this gap by combining a survey of Wikipedia readers
with a log-based analysis of user activity. Based on an initial series of user
surveys, we build a taxonomy of Wikipedia use cases along several dimensions,
capturing users' motivations to visit Wikipedia, the depth of knowledge they
are seeking, and their knowledge of the topic of interest prior to visiting
Wikipedia. Then, we quantify the prevalence of these use cases via a
large-scale user survey conducted on live Wikipedia with almost 30,000
responses. Our analyses highlight the variety of factors driving users to
Wikipedia, such as current events, media coverage of a topic, personal
curiosity, work or school assignments, or boredom. Finally, we match survey
responses to the respondents' digital traces in Wikipedia's server logs,
enabling the discovery of behavioral patterns associated with specific use
cases. For instance, we observe long and fast-paced page sequences across
topics for users who are bored or exploring randomly, whereas those using
Wikipedia for work or school spend more time on individual articles focused on
topics such as science. Our findings advance our understanding of reader
motivations and behavior on Wikipedia and can have implications for developers
aiming to improve Wikipedia's user experience, editors striving to cater to
their readers' needs, third-party services (such as search engines) providing
access to Wikipedia content, and researchers aiming to build tools such as
recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
A customisable pipeline for continuously harvesting socially-minded Twitter users
On social media platforms and Twitter in particular, specific classes of
users such as influencers have been given satisfactory operational definitions
in terms of network and content metrics.
Others, for instance online activists, are not less important but their
characterisation still requires experimenting.
We make the hypothesis that such interesting users can be found within
temporally and spatially localised contexts, i.e., small but topical fragments
of the network containing interactions about social events or campaigns with a
significant footprint on Twitter.
To explore this hypothesis, we have designed a continuous user profile
discovery pipeline that produces an ever-growing dataset of user profiles by
harvesting and analysing contexts from the Twitter stream.
The profiles dataset includes key network and content-based users metrics,
enabling experimentation with user-defined score functions that characterise
specific classes of online users.
The paper describes the design and implementation of the pipeline and its
empirical evaluation on a case study consisting of healthcare-related campaigns
in the UK, showing how it supports the operational definitions of online
activism, by comparing three experimental ranking functions. The code is
publicly available.Comment: Procs. ICWE 2019, June 2019, Kore
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