Remarks by Adama Dieng

Dieng, Adama

doi:10.1017/amp.2021.36

Cited by 1 publication

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“…The summit's goal was to produce a Plan of Action to oppose hate speech and protect religious minorities and migrants (United Nations, 2019) [ 7 ]. Adama Dieng the UN's Speech Adviser for the prevention of Genocide state that “Political opportunism is fueling an increase in the hate speech.” This statement draws the analogies between 1930s Europe and present political climate on the continent [ 8 ]. He warned that “big atrocities often begin with little actions and language.” Dieng is worried about the far-right parties' political plans in Europe following the 2015 refugee crisis.…”

Section: Literature Surveymentioning

confidence: 99%

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Hasan

Sharma²,

Khan

et al. 2022

Computational Intelligence and Neuroscience

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Twitter’s popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an increase in the number of people who express their views in demeaning ways to others. As a result, hate speech has piqued interest in the subject of sentiment analysis, which has developed various algorithms for detecting emotions in social networks using intuitive means. This paper proposes the deep learning model to classify the sentiments in two separate analyses. In the first analysis, the tweets are classified based on the hate speech against the migrants and the women. In the second analysis, the detection is performed using a deep learning model to organise whether the hate speech is performed by a single or a group of users. During the text analysis, word embedding is implemented using the combination of deep learning models such as BiLSTM, CNN, and MLP. These models are integrated with word embedding methods such as inverse glove (global vector), document frequency (TF-IDF), and transformer-based embedding.

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Section: Literature Surveymentioning

confidence: 99%