REVIEW 1 cited by
Quantifying Public Response towards Islam on Twitter after Paris Attacks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The Paris terrorist attacks occurred on November 13, 2015 prompted a massive response on social media including Twitter, with millions of posted tweets in the first few hours after the attacks. Most of the tweets were condemning the attacks and showing support to Parisians. One of the trending debates related to the attacks concerned possible association between terrorism and Islam and Muslims in general. This created a global discussion between those attacking and those defending Islam and Muslims. In this paper, we provide quantitative and qualitative analysis of data collection we streamed from Twitter starting 7 hours after the Paris attacks and for 50 subsequent hours that are related to blaming Islam and Muslims and to defending them. We collected a set of 8.36 million tweets in this epoch consisting of tweets in many different of languages. We could identify a subset consisting of 900K tweets relating to Islam and Muslims. Using sampling methods and crowd-sourcing annotation, we managed to estimate the public response of these tweets. Our findings show that the majority of the tweets were in fact defending Muslims and absolving them from responsibility for the attacks. However, a considerable number of tweets were blaming Muslims, with most of these tweets coming from western countries such as the Netherlands, France, and the US.
Forward citations
Cited by 1 Pith paper
-
Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
A Socratic LLM that questions annotators during labeling improved post-deliberation accuracy and confidence compared to a prior synchronous human-deliberation benchmark.
Discussion (0). Continue with ORCID to comment.