Hate Speech and Offensive Language Detection from Social Media

Author(s):  
Vildan Mercan ◽  
Akhtar Jamil ◽  
Alaa Ali Hameed ◽  
Irfan Ahmed Magsi ◽  
Sibghatullah Bazai ◽  
...  
2020 ◽  
Author(s):  
Mahen Herath ◽  
Thushari Atapattu ◽  
Hoang Anh Dung ◽  
Christoph Treude ◽  
Katrina Falkner

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nauman Ul Haq ◽  
Mohib Ullah ◽  
Rafiullah Khan ◽  
Arshad Ahmad ◽  
Ahmad Almogren ◽  
...  

The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD (Urdu Slang and Abusive words Detection), a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models.


2021 ◽  
Author(s):  
Nobal B. Niraula ◽  
Saurab Dulal ◽  
Diwa Koirala

2020 ◽  
Author(s):  
Hammad Rizwan ◽  
Muhammad Haroon Shakeel ◽  
Asim Karim

2021 ◽  
Author(s):  
Sünje Paasch-Colberg ◽  
Joachim Trebbe ◽  
Christian Strippel ◽  
Martin Emmer

In the past decade, the public discourse on immigration in Germany has been strongly affected by right-wing populist, racist, and Islamophobic positions. This becomes evident especially in the comment sections of news websites and social media platforms, where user discussions often escalate and trigger hate comments against refugees and immigrants and also against journalists, politicians, and other groups. In view of the threatening consequences such sentiments can have for groups who are targeted by right-wing extremist violence, we take a closer look into such user discussions to gain detailed insights into the various forms of hate speech and offensive language against these groups. Using a modularized framework that goes beyond the common “hate/no-hate” dichotomy in the field, we conducted a structured text annotation of 5,031 user comments posted on German news websites and social media in March 2019. Most of the hate speech we found was directed against refugees and immigrants, while other groups were mostly exposed to various forms of offensive language. In comments containing hate speech, refugees and Muslims were frequently stereotyped as criminals, whereas extreme forms of hate speech, such as calls for violence, were rare in our data. These findings are discussed with a focus on their potential consequences for public discourse on immigration in Germany.


2020 ◽  
Vol 34 (01) ◽  
pp. 881-889
Author(s):  
Anthony Rios

Hate speech and offensive language are rampant on social media. Machine learning has provided a way to moderate foul language at scale. However, much of the current research focuses on overall performance. Models may perform poorly on text written in a minority dialectal language. For instance, a hate speech classifier may produce more false positives on tweets written in African-American Vernacular English (AAVE). To measure these problems, we need text written in both AAVE and Standard American English (SAE). Unfortunately, it is challenging to curate data for all linguistic styles in a timely manner—especially when we are constrained to specific problems, social media platforms, or by limited resources. In this paper, we answer the question, “How can we evaluate the performance of classifiers across minority dialectal languages when they are not present within a particular dataset?” Specifically, we propose an automated fairness fuzzing tool called FuzzE to quantify the fairness of text classifiers applied to AAVE text using a dataset that only contains text written in SAE. Overall, we find that the fairness estimates returned by our technique moderately correlates with the use of real ground-truth AAVE text. Warning: Offensive language is displayed in this manuscript.


Author(s):  
Dr. Sweeta Bansal

As we know that the social crowd is increasing day by day, so is the hatred among them online. This hatred gives rise to hate speech/comments that are passed to one another online. Recently, the hate speech has increased so much that we need a way to stop them or at least contain it to minimum. Keeping this problem in mind, we have introduced a way in which we can detect the class of comments that are posted online and stop its spread if it belongs to hateful category. We have used Natural Language Processing methods and Logistic Regression algorithm to achieve our goal.


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