63,583 research outputs found
The Impact of Crowds on News Engagement: A Reddit Case Study
Today, users are reading the news through social platforms. These platforms
are built to facilitate crowd engagement, but not necessarily disseminate
useful news to inform the masses. Hence, the news that is highly engaged with
may not be the news that best informs. While predicting news popularity has
been well studied, it has not been studied in the context of crowd
manipulations. In this paper, we provide some preliminary results to a longer
term project on crowd and platform manipulations of news and news popularity.
In particular, we choose to study known features for predicting news popularity
and how those features may change on reddit.com, a social platform used
commonly for news aggregation. Along with this, we explore ways in which users
can alter the perception of news through changing the title of an article. We
find that news on reddit is predictable using previously studied sentiment and
content features and that posts with titles changed by reddit users tend to be
more popular than posts with the original article title.Comment: Published at The 2nd International Workshop on News and Public
Opinion at ICWSM 201
The Social World of Content Abusers in Community Question Answering
Community-based question answering platforms can be rich sources of
information on a variety of specialized topics, from finance to cooking. The
usefulness of such platforms depends heavily on user contributions (questions
and answers), but also on respecting the community rules. As a crowd-sourced
service, such platforms rely on their users for monitoring and flagging content
that violates community rules.
Common wisdom is to eliminate the users who receive many flags. Our analysis
of a year of traces from a mature Q&A site shows that the number of flags does
not tell the full story: on one hand, users with many flags may still
contribute positively to the community. On the other hand, users who never get
flagged are found to violate community rules and get their accounts suspended.
This analysis, however, also shows that abusive users are betrayed by their
network properties: we find strong evidence of homophilous behavior and use
this finding to detect abusive users who go under the community radar. Based on
our empirical observations, we build a classifier that is able to detect
abusive users with an accuracy as high as 83%.Comment: Published in the proceedings of the 24th International World Wide Web
Conference (WWW 2015
The use of learning management platforms in school context - a national study
This report results from a national study carried out under the Project “Educational application of learning management platforms”, supported and funded by the Computers, Networks, and Internet in Schools department of the Portuguese Ministry of Education- General Directorate for Innovation and Educational Development. This report has been developed by the ICT Competence Centre of the Faculty of Sciences- University of Lisbon, during the school year 2007/2008
Accessibility assessment of MOOC platforms in Spanish: UNED COMA, COLMENIA and Miriada X
This article develops a methodology for the assessment of MOOC courses, focusing on the degree of accessibility of three Spanish MOOC platforms: UNED COMA, COLMENIA and Miriada X. Four different criteria have been
used in this context: automatic tools, disability simulators, testing tools and educational conten
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