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Found 20 papers, 13 papers with code
Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences
Word senses are not static and may have temporal, spatial or corpus-specific scopes.
Thou shalt not hate: Countering Online Hate Speech
In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube.
Social and Information Networks
Deep Dive into Anonymity: A Large Scale Analysis of Quora Questions
In particular, we observe that the choice to post the question as anonymous is dependent on the user's perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity.
Spread of hate speech in online social media
The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront.
Social and Information Networks
Analyzing the hate and counter speech accounts on Twitter
We also build a supervised model for classifying the hateful and counterspeech accounts on Twitter and obtain an F-score of 0. 77.
Social and Information Networks
Hateminers : Detecting Hate speech against Women
With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content.
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs
Our system is also able to predict ~ 25% of the correct case of merges within the first month of the merge and ~ 40% of the cases within a year.
HateMonitors: Language Agnostic Abuse Detection in Social Media
In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019.
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings
We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials.
Deep Learning Models for Multilingual Hate Speech Detection
Hate speech detection is a challenging problem with most of the datasets available in only one language: English.
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities.
Ranked #3 on
Hate Speech Detection
on HateXplain
"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups
We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community.
You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
Hate speech is regarded as one of the crucial issues plaguing the online social media.
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models
To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation.
CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech
We generate counterspeech using three datasets and observe significant improvement across different attribute scores.
Rationale-Guided Few-Shot Classification to Detect Abusive Language
We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains.
HateProof: Are Hateful Meme Detection Systems really Robust?
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
On the rise of fear speech in online social media
Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community.
HateMM: A Multi-Modal Dataset for Hate Video Classification
Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world.
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Second, in a dataset extension exercise, using influence functions to automatically identify data points that have been initially `silver' annotated by some existing method and need to be cross-checked (and corrected) by annotators to improve the model performance.
