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

Abstract

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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High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus

“…To build the dataset, they relied on expert annotation using a combination of techniques to collect data from Twitter, including hateful keywords related to Spanish and Latin American contexts. Plaza-Del-Arco et al and Hasan et al used HatEval to train and compare several machine learning models to detect HS against immigrants [22,23]. Arcila-Calderón et al manually created an ad hoc dataset to train deep and shallow learning models to detect anti-immigrant speech in European Spanish [15].…”

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

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”