“This is not to say that exposure to a virtual extremist community drives one to offline violence. Social media is only one contributing factor and likely does not substantially alter
Month: April 2022
Review on Countering Extremism and Hate Speech. April 2022 (I/II – Easter Edition) | Policyinstitute.netReview on Countering Extremism and Hate Speech. April 2022 (I/II – Easter Edition) | Policyinstitute.net
The 50 most popular LinkedIn posts on the topics of counter-extremism and of countering hate speech posted by Thorsten Koch, MA, PgDip, during the first half of April 2022. In
What Free Speech Restrictions Would Citizens Like to Impose? (osf.io)What Free Speech Restrictions Would Citizens Like to Impose? (osf.io)
Citizens are more willing to restrict statements on the sensitive issues of economic stability, epidemics, and national security or offensive statements toward minorities and national symbols than they are to
Retweet communities reveal the main sources of hate speech (PLoS One)Retweet communities reveal the main sources of hate speech (PLoS One)
Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by
Regulating content won’t make the internet safer. We have to change the business models (The Conversation)Regulating content won’t make the internet safer. We have to change the business models (The Conversation)
Competition law must also be employed to curb risky business practices of dominant operators in order to prevent harm. The EU’s proposal for a Digital Markets Act is an example
People’s understanding of the concept of misinformation (Journal of Risk Research)People’s understanding of the concept of misinformation (Journal of Risk Research)
Relative to other sources (e.g. media, other people), experts (48.38%) and scientific evidence (60.20%) were the most common sources by which to determine that something is misinformation. Finally, looking at
An Inter and Intra Transformer for Hate Speech Detection (IEEE)An Inter and Intra Transformer for Hate Speech Detection (IEEE)
In recent years, successful hate speech detection models have been developed, such as using CNN, LSTM, but they lack a sufficient amount of discriminative power. In order to tackle the
L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models (arXiv)L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models (arXiv)
The dataset is curated from Twitter, annotated manually. Our dataset consists of over 25000 distinct tweets labeled into four major classes i.e hate, offensive, profane, and not. We present the