[PDF] Top 20 A Pragmatic Supervised Learning Methodology of Hate Speech Detection in Social Media
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A Pragmatic Supervised Learning Methodology of Hate Speech Detection in Social Media
... N-grams are one of the most used techniques in hate speech automatic detection and related tasks [1,3,14]. The most common n-grams approach consists in combining sequential words into lists with size ... See full document
8
A Dataset of Hindi English Code Mixed Social Media Text for Hate Speech Detection
... Hate speech detection in social media texts is an important Natural language Processing task, which has several crucial applications like sentiment analysis, investigating cyber ... See full document
6
Improving Hate Speech Detection with Deep Learning Ensembles
... Hate speech has become a major issue that is currently a hot topic in the domain of social ...a hate speech corpus from ...available hate speech evaluation ...deep ... See full document
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CIC at SemEval 2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter
... Currently, interest is increasing in the identifica- tion of HS against women on the web (Ging et al., 2018). Initially, Hewitt (2016) worked on the iden- tification of HS against women in social media. Fox ... See full document
5
The Risk of Racial Bias in Hate Speech Detection
... against speech by African Americans, focusing on Twit- ter as it is a particularly important space for Black activism (Williams and Domoszlai, 2013; Freelon et ...multi-faceted social construct (Sen and Wa- ... See full document
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ARHNet Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic
... Hate speech research has been conducted exten- sively for the English ...apply supervised learning to the task of hate speech detection were (Yin and Davison, 2009) who ... See full document
8
Predictive Embeddings for Hate Speech Detection on Twitter
... of social media plat- forms like Twitter for both personal and politi- cal communication (Lapowsky, 2017) has seen a well-acknowledged rise in the presence of toxic and abusive speech on these ... See full document
7
JCTICOL at SemEval 2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N gram Features, and Preprocessing Methods
... on hate speech detection is presented by Schmidt and Wiegand ...categories: hate speech, offensive language, and none of these ...separate hate speech from other offensive ... See full document
7
Leveraging Intra User and Inter User Representation Learning for Automated Hate Speech Detection
... Hate speech detection is a critical, yet chal- lenging problem in Natural Language Process- ing ...NLP hate speech detection approaches, the accu- racy is still ...that ... See full document
6
MITRE at SemEval 2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection
... of social media allows anyone to post their thoughts and opinions for all to ...of hate speech (Fortuna and Nunes, 2018; Schmidt and Wiegand, 2017) on platforms like Twitter is of particular ... See full document
7
JCTDHS at SemEval 2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N gram Features, and Preprocessing Methods
... Hate Speech is usually defined as communication that contains contempt or hatred towards a person or a group of people on the basis of some characteristic ...of hate speech in social ... See full document
5
Detecting Hate Speech in Social Media
... In future work we would like to investigate the performance of classifier ensembles and meta- learning for this task. Previous work has applied these techniques to a number of comparable text classification tasks, ... See full document
6
Sentiment Analysis in Czech Social Media Using Supervised Machine Learning
... The key point of using machine learning for senti- ment analysis lies in engineering a representative set of features. Pang et al. (2002) experimented with unigrams (presence of a certain word, frequencies of ... See full document
10
Automatic Speech Recognition Errors Detection Using Supervised Learning Techniques
... Automatic Speech Recognition ...of speech technology in real life ...errors detection system targeted towards substitution and insertion ...on supervised learning techniques and uses ... See full document
7
Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators
... Tweets belonging to a particular event domain (eg. civil unrest, disaster, presidential election) can be identified by learning various kinds of event indicators or sensors across contexts. In spite of their ... See full document
8
The Effects of User Features on Twitter Hate Speech Detection
... The English dataset by Waseem and Hovy (2016) is publicly available on GitHub. 1 The Twitter search API was used to collect the corpus, and in total 16,907 tweets (from 2,399 users) were anno- tated either as racist, ... See full document
11
A Benchmark Dataset for Learning to Intervene in Online Hate Speech
... Reinforcement Learning model, as it is too challenging for the backward model to reconstruct the complete con- versation based only on the intervention ...generative hate speech intervention task, we ... See full document
10
Grunn2019 at SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate
... (2018) observed that extremist violence tends to lead to an increase in online hate speech, partic- ularly on messages directly advocating violence. Also, Anzovino et al. (2018) contributed to the re- ... See full document
5
A Weakly Supervised Bayesian Model for Violence Detection in Social Media
... olence detection model (VDM), which en- ables the identification of text containing vio- lent content and extraction of violence-related topics over social media ... See full document
9
Taking North American White Supremacist Groups Seriously: The Scope and the Challenge of Hate Speech on the Internet
... A recent study by Chan, Ghose and Seamans (2016) found that some 14,000 Internet sites contained hate-related content. Using a large-scale dataset and econometric techniques, they found a positive relationship ... See full document
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