Verbal Aggression on Social Media: How, why and its Automatic Identification

Kumar, Ritesh

doi:10.31235/osf.io/qcmez

Cited by 11 publications

(13 citation statements)

References 6 publications

(7 reference statements)

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“…From a current observation, there are some works of detecting hate speech vs. vulgarity in Kumar et al (2018b), which leads to the scope to differentiate speech vs. vulgarity into covert and overt aggression. Dinakar et al (2011) had completed the work relating to cyber-bullying.…”

Section: Text-based Sentiment Analysismentioning

confidence: 96%

“…The online platform is not considered just a matter of nuisance but has been marked as a significant criminal activity that can be dangerous for many people (Kumar et al 2018b). So, it is essential to take some preventive action to provide a safeguard to the people of the web.…”

Section: Related Workmentioning

confidence: 99%

“…An Aggression dataset (Kumar et al 2018b) has been used for text, which is divided into three classes: non-aggressive (NAG, no-pain), covertly aggressive (CAG, Low-pain), and overtly aggressive (OAG, high-pain). As mentioned in Sect.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

Ghosh

Dhara²,

Pero³

et al. 2023

J Ambient Intell Human Comput

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This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-based sentiment analysis system to perform classification tasks such as predicting the classes into non-aggressive, covertly aggressive, and overtly aggressive classes. The remaining two components are related to an image-based sentiment analysis system. A deep learning-based approach has been employed to do feature learning and predict the three types of pain classes. An aggression dataset for the text-based system and the UNBC-McMaster database for an image-based system has been employed, respectively. Experimental results have been compared with the state-of-the-art methods, showing the superiority of the proposed approach. Finally, the scores due to text-based and image-based sentiment analysis systems are fused to obtain the performance for the proposed multimodal sentiment analysis system.

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Section: Text-based Sentiment Analysismentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

Ghosh

Dhara²,

Pero³

et al. 2023

J Ambient Intell Human Comput

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show abstract

“…Some of the models can capture the true meaning of the sentence very well; some are good at less computation process. R. kumar et al showed an LSTM model for detecting aggressive comments on social media [1]. They used the combined data of both Trac-1 and Trac-2 workshops.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data

akter

Ahmed

2022

Preprint

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Aggressive comments on social media negatively impact human life. Such offensive contents are responsible for depression and suicidal-related activities. Since online social networking is increasing day by day, the hate content is also increasing. Several investigations have been done on the domain of cyberbullying, cyberaggression, hate speech, etc. The majority of the inquiry has been done in the English language. Some languages (Hindi and Bangla) still lack proper investigations due to the lack of a dataset. This paper particularly worked on the Hindi, Bangla, and English datasets to detect aggressive comments and have shown a novel way of generating machine-translated data to resolve data unavailability issues. A fully machine-translated English dataset has been analyzed with the models such as Long Short term memory model (LSTM), Bidirectional Long-short term memory model (BiLSTM), LSTM-Autoencoder, word2vec, Bidirectional Encoder Representations from Transformers (BERT), and generative pre-trained transformer (GPT-2) to make an observation on how the models perform on a machine-translated noisy dataset. We have compared the performance of using the noisy data with two more datasets, such as raw data, which does not contain any noises, and semi-noisy data, which contains a certain amount of noisy data. We have classified both the raw and semi-noisy data using the aforementioned models. To evaluate the performance of the models, we have used evaluation metrics such as F1-score, accuracy, precision, and recall. We have achieved the highest accuracy on raw data using the gpt2 model, semi-noisy data using the BERT model, and fully machine-translated data using the BERT model. Since many languages do not have proper data availability, our approach will help researchers create machine-translated datasets for several analysis purposes.

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“…At the point when we incorporate the messages posted by bots and phony records, disdain discourse turns out to be too normal to ever be distinguished and directed physically. [7] Meanings of online disdain: Instead of one single common meaning, the writing is contained with numerous definitions with particular ways to deal with online disdain. https://doi.org/10.1051/itmconf/20224403034 ICACC-2022 Table 1: Definition of Online Hate [8] The issue of distinguishing disdain discourse has been tended to by different analysts in various ways.…”

Section: Literature Surveymentioning

confidence: 99%

Hate Classifier for Social Media Platform Using Tree LSTM

Raut¹,

Mhatre²,

Jha³

et al. 2022

ITM Web Conf.

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Social-media without a doubt is one of the most noteworthy developments ever. From associating with individuals across the globe for sharing of data and information in an infinitesimal of a second, online media stages have enormously altered the method of our lives. This is joined by a steadily expanding utilization of social media, less expensive cell phones, and the simplicity of web access which have additionally prepared for the huge development of social media. To place this into numbers, according to an ongoing report, billions of individuals all over the planet presently utilize web-based media every month, and a normal amount of almost 2 million people new clients are going along with them consistently. While web-based media stages have permitted us to interface with others and fortify connections in manners that were not conceivable previously. Unfortunately, they have additionally turned into the default gatherings for can’t stand discourse. Online hate is a wild issue, with the adverse result of disallowing client support in web-based conversations and causing mental mischief to people. Since hate is pervasive across friendly, media stages, our objective was to foster a classifier that is feasible to train classifiers that can identify hateful remarks with strong execution and with the portion of misleading up-sides and negatives staying inside sensible limits.

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Verbal Aggression on Social Media: How, why and its Automatic Identification

Cited by 11 publications

References 6 publications

A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data

Hate Classifier for Social Media Platform Using Tree LSTM

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