Effective hate-speech detection in Twitter data using recurrent neural networks

Pitsilis, Georgios; Ramampiaro, Heri; Langseth, Helge

doi:10.1007/s10489-018-1242-y

Cited by 167 publications

(72 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…So, they suggested to use (LSTM) and different algorithm for the embedding for their future work. For hate speech detection, Pitsilis et al [34] used RNN model with word frequency vectorization to implement the features instead of the word embedding to break the barrier of language dependency in word embedding approach. Their results outperformed the current state of art deep learning approaches for hate speech detection.…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

103

View full text Add to dashboard Cite

In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

show abstract

Section: Deep Learningmentioning

confidence: 99%

“…They stated that their work sets a new benchmark for future researches in this area. Finally, Pitsilis et al [34] believe that deep neural networks have a high potential to solve the issue of hate speech detection. Their deep learning approach outperformed all the state-of-art approaches.…”

Section: Hate Speech Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

103

View full text Add to dashboard Cite

show abstract

“…The 20 th cluster revealed deep learning models and text classification as a viable source for identification of hate speech on Facebook groups in 2016 with a silhouette value of 1.0. The papers by Agrawal et al [52] and Pitsilis et al [53] were the most common citers of these clusters. Pitsilis et al [53] proposed recurrent neural network models to discern hateful content on social media utilizing user-related information such as their tendency toward racism and sexism [53], while Agrawal et al [52] showed that previous algorithms aiding in detection of cyberbullying have bottlenecks: specific platform, a specific topic of bullying, and thirdly, reliance on handcrafted features of the data.…”

Section: Resultsmentioning

confidence: 99%

Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate

et al. 2019

View full text Add to dashboard Cite

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society.

show abstract

“…For hate speech detection in tweets, Georgios et al [19] proposes an RNN (Recurrent Neural Network), which integrates various characteristics such as the tendency of the user to racism or sexism. For each tweet, they add the user's tendencies:…”

Section: Hate Speech Detectionmentioning

confidence: 99%

Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks

Elouali¹,

Elberrichi²,

Elouali³

2020

RIA

View full text Add to dashboard Cite

Hate speech detection on Twitter is often treated in monolingual (in English generally) ignoring the fact that Twitter is a global platform where everyone expresses himself with his natal language. In this paper, we created a model which, taking benefits of the advantages of neural networks, classifies tweets written in seven different languages (and even those that contains more than one language at the same time) to hate speech or non hate speech. We used Convolutional Neural Networks (CNN) and character level representation. We carried out several experiments in order to adjust the parameters according to our case study. Our best results were (in terms of accuracy) 0.8893 for a dataset containing five languages and 0.8300 for a dataset of seven languages. Our model solves properly the problem of hate speech on Twitter and its results are, compared to the state of the art, more than satisfactory.

show abstract

Effective hate-speech detection in Twitter data using recurrent neural networks

Cited by 167 publications

References 16 publications

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate

Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks

Contact Info

Product

Resources

About