منابع مشابه
Unbiased Learning of Controversial Topics
When presented with many relevant documents about a controversial topic, humans do not always read and trust them uniformly. Instead, they tend to follow and agree with articles and sources that hold similar viewpoints as theirs, a phenomenon known as confirmation bias. This suggests that when acquiring additional knowledge about a controversial topic, human biases and viewpoints about the topi...
متن کاملThe most controversial topics in Wikipedia:
We present, visualize and analyse the similarities and differences between the controversial topics related to “edit wars” identified in 10 different language versions of Wikipedia. After a brief review of the related work we describe the methods developed to locate, measure, and categorize the controversial topics in the different languages. Visualizations of the degree of overlap between the ...
متن کاملTelling Apart Tweets Associated with Controversial versus Non-Controversial Topics
In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and other features that have been previously ...
متن کاملOvercoming bias to learn about controversial topics
Deciding whether a claim is true or false often requires a deeper understanding of the evidence supporting and contradicting the claim. However, when presented with many evidence documents, users do not necessarily read and trust them uniformly. Psychologists and other researchers have shown that users tend to follow and agree with articles and sources that hold viewpoints similar to their own,...
متن کاملModeling the Dynamic Framing of Controversial Topics in Online Communities
Understanding how online communities frame controversial topics can have significant impacts in public perception, policy action, and social change. I present two neural-network based approaches for automatically detecting how a community frames a topic and how this framing changes over time. The diachronic word embedding model, trained on individual months of data from the online community Red...
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ژورنال
عنوان ژورنال: Annals of The Royal College of Surgeons of England
سال: 2004
ISSN: 0035-8843,1478-7083
DOI: 10.1308/147870804461