15,922 research outputs found
Open Access Publishing: A Literature Review
Within the context of the Centre for Copyright and New Business Models in the Creative Economy (CREATe) research scope, this literature review investigates the current trends, advantages, disadvantages, problems and solutions, opportunities and barriers in Open Access Publishing (OAP), and in particular Open Access (OA) academic publishing. This study is intended to scope and evaluate current theory and practice concerning models for OAP and engage with intellectual, legal and economic perspectives on OAP. It is also aimed at mapping the field of academic publishing in the UK and abroad, drawing specifically upon the experiences of CREATe industry partners as well as other initiatives such as SSRN, open source software, and Creative Commons. As a final critical goal, this scoping study will identify any meaningful gaps in the relevant literature with a view to developing further research questions. The results of this scoping exercise will then be presented to relevant industry and academic partners at a workshop intended to assist in further developing the critical research questions pertinent to OAP
Predicting the Effects of News Sentiments on the Stock Market
Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.Comment: 4 page
Social Interactions vs Revisions, What is important for Promotion in Wikipedia?
In epistemic community, people are said to be selected on their knowledge
contribution to the project (articles, codes, etc.) However, the socialization
process is an important factor for inclusion, sustainability as a contributor,
and promotion. Finally, what does matter to be promoted? being a good
contributor? being a good animator? knowing the boss? We explore this question
looking at the process of election for administrator in the English Wikipedia
community. We modeled the candidates according to their revisions and/or social
attributes. These attributes are used to construct a predictive model of
promotion success, based on the candidates's past behavior, computed thanks to
a random forest algorithm.
Our model combining knowledge contribution variables and social networking
variables successfully explain 78% of the results which is better than the
former models. It also helps to refine the criterion for election. If the
number of knowledge contributions is the most important element, social
interactions come close second to explain the election. But being connected
with the future peers (the admins) can make the difference between success and
failure, making this epistemic community a very social community too
Stigmergy in Web 2.0: a model for site dynamics
Building Web 2.0 sites does not necessarily ensure the success of the site. We aim to better understand what improves the success of a site by drawing insight from biologically inspired design patterns. Web 2.0 sites provide a mechanism for human interaction enabling powerful intercommunication between massive volumes of users. Early Web 2.0 site providers that were previously dominant are being succeeded by newer sites providing innovative social interaction mechanisms. Understanding what site traits contribute to this success drives research into Web sites mechanics using models to describe the associated social networking behaviour. Some of these models attempt to show how the volume of users provides a self-organising and self-contextualisation of content. One model describing coordinated environments is called stigmergy, a term originally describing coordinated insect behavior. This paper explores how exploiting stigmergy can provide a valuable mechanism for identifying and analysing online user behavior specifically when considering that user freedom of choice is restricted by the provided web site functionality. This will aid our building better collaborative Web sites improving the collaborative processes
Explicit diversification of event aspects for temporal summarization
During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness
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