Content Similarity Analysis of Written Comments under Posts in Social Media

Mozafari, Marzieh; Farahbakhsh, Reza; Crespi, Noël

doi:10.1109/snams.2019.8931726

Cited by 6 publications

(2 citation statements)

References 18 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owning to the recent proliferation of user-generated textual contents in online social media, a wide variety of studies have been dedicated to investigating these contents in terms of hate or toxic speech, abusive or offensive languages, etc., [1][2][3][4][5][6]. With regard to the mobility and anonymous environment of online social media, suspect users, who generate abusive contents or organize the hate-based activities, exploit these online platforms to propagate hate and offensive contents towards other users and communities [2,7]; where it leads to personal trauma, hate crime, cyber-bullying, and discrimination (mainly racial and sexual discriminations) [8].…”

Section: Disclaimermentioning

confidence: 99%

Hate speech detection and racial bias mitigation in social media based on BERT model

2020

Self Cite

View full text Add to dashboard Cite

Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pretrained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.

show abstract

Section: Disclaimermentioning

confidence: 99%

Hate speech detection and racial bias mitigation in social media based on BERT model

2020

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model

Mozafari,

Farahbakhsh,

Crespi

2020

Preprint

Self Cite

View full text Add to dashboard Cite

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS.Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism in hate speech detection task to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.

show abstract

Spatial and Temporal Patterns of Government – Citizen Interaction Online. Examining the Social Media Pages of the Russian Regional Authorities

Kabanov,

Kuzmenko

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Content Similarity Analysis of Written Comments under Posts in Social Media

Cited by 6 publications

References 18 publications

Hate speech detection and racial bias mitigation in social media based on BERT model

Hate speech detection and racial bias mitigation in social media based on BERT model

Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model

Spatial and Temporal Patterns of Government – Citizen Interaction Online. Examining the Social Media Pages of the Russian Regional Authorities

Contact Info

Product

Resources

About