ArticlePeerJ. Computer science2022
A word embedding technique for sentiment analysis of social media to understand the relationship between Islamophobic incidents and media portrayal of Muslim communities.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A review on abusive content automatic detection: approaches, challenges and opportunities.PeerJ. Computer science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Islamophobia is a sentiment against the Muslim community; recently, atrocities towards Muslim communities witnessed this sentiment globally. This research investigates the correlation between how news stories covered by mainstream news channels impede the hate speech/Islamophobic sentiment. To examine the objective mentioned above, we shortlisted thirteen mainstream news channels and the ten most widely reported Islamophobic incidents across the globe for experimentation. Transcripts of the news stories are scraped along with their comments, likes, dislikes, and recommended videos as the users' responses. We used a word embedding technique for sentiment analysis,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.