UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning

Abstract

With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such tweets and comments that are malicious in intent, which either targets an individual or a group. Our best classifier for identifying offensive tweets for SubTask A (Classifying offensive vs. nonoffensive) has an accuracy of 83.14% and a f1score of 0.7565 on the actual test data. For SubTask B, to identify if an offensive tweet is targeted (If targeted towards an individual or a group), the classifier performs with an accuracy of 89.17% and f1-score of 0.5885. The paper talks about how we generated linguistic and semantic features to build an ensemble machine learning model. By training with more extracts from different sources (Facebook, and more tweets), the paper shows how the accuracy changes with additional training data.

Nozza@LT-EDI-ACL2022: Ensemble Modeling for Homophobia and Transphobia Detection

“…Several NLP approaches have been proposed for the task of hate speech detection (Qian et al, 2018;Indurthi et al, 2019;Vidgen et al, 2021;Fersini et al, 2020a;Attanasio and Pastor, 2020;Kennedy et al, 2020;Attanasio et al, 2022b, inter alia). While ensemble modeling has been proven to be effective for several tasks in NLP (Garmash and Monz, 2016;Nozza et al, 2016;Fadel et al, 2019;Bashmal and AlZeer, 2021), a limited number of research work have investigated its potentiality for hate speech detection (Plaza-del Arco et al, 2019;Ramakrishnan et al, 2019;Zimmer-man et al, 2018).…”

Selecting and combining complementary feature representations and classifiers for hate speech detection

“…Recent papers use word embedding methods more frequently than bagof-words and n-grans because the former can extract semantic information from the text; consequently, an improvement in the performance is expected. Regarding the classifier, different paradigms have been employed; tree-based algorithms such as decision trees and random forest (RF) [17,8,18,19], artificial neural networks such as multi-layer perceptron (MLP) and convolution neural networks (CNN) [20,21,22,16,23,24,25,26,27,28,29],…”

“…Bayesian as the naive bayes (NB) [17,8], support vector machines (SVM) [17,8], and ensemble learning, which is marked () in the last column of the [20] Twitter from [30,31] racism, sexism characters, words, and both CNN 2018 Zimmerman et al [21] Twitter from [30] racism, sexism embedding deep learning 2018 Pitsilis et al [22] Twitter from [30] racism, sexism defined by the authors LSTM 2018 Montani and Schuller [18] GermEval 2018 1 general TFIDF, Word2Vec, n-gram LR, RF, ET 2019 Zhang and Luo [16] Twitter from [17,30] [17]: race ethnicity, religion [30]: racism, sexism Word2Vec CNN 2019 Liu et al [32] Twitter from [17] race ethnicity, religion embedding, LDA fuzzy ensemble 2019 Ramakrishnan et al [19] OffensEval [33] general n-gram, GloVe, others LR, RF, XG 2020 Paschalides et al [23] Twitter from [8] racism, sexism, homophobia The most common social media used to extract information to compose a dataset for hate speech detection is Twitter. Despite English being the most used language, there are datasets from many other languages, such as the Arabic-Twitter dataset [26] and Hindi-English Twitter dataset [27].…”

“…Based on Table 1, we can see that more and more research works are considering an ensemble model to improve hate speech detection [24,26,23,19,17]. Some works generate an ensemble by just changing the classification model [17] employed, while others consider a homogeneous ensemble trained with different input features [25,24].…”

IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation

“…The results proved that transfer learning improves offensive language detection performance. Ramakrishnan et al (2019) used an ensemble model based on logistic regression and tree-based model to identify offensive language on SemEval-2019 Task 6. Char n-grams, word n-grams, part of speech and GloVe embedding were used as features.…”