Kirti Kumari


Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments
Kirti Kumari | Shaury Srivastav | Rajiv Ranjan Suman
Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)

In this paper, we presented our team "IIITRanchi” for the Trolling, Aggression and Cyberbullying (TRAC-3) 2022 shared tasks. Aggression and its different forms on social media and other platforms had tremendous growth on the Internet. In this work we have tried upon different aspects of aggression, aggression intensity, bias of different forms and their usage online and its identification using different Machine Learning techniques. We have classified each sample at seven different tasks namely aggression level, aggression intensity, discursive role, gender bias, religious bias, caste/class bias and ethnicity/racial bias as specified in the shared tasks. Both of our teams tried machine learning classifiers and achieved the good results. Overall, our team "IIITRanchi” ranked first position in this shared tasks competition.


AI_ML_NIT_Patna @ TRAC - 2: Deep Learning Approach for Multi-lingual Aggression Identification
Kirti Kumari | Jyoti Prakash Singh
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

This paper describes the details of developed models and results of team AI_ML_NIT_Patna for the shared task of TRAC - 2. The main objective of the said task is to identify the level of aggression and whether the comment is gendered based or not. The aggression level of each comment can be marked as either Overtly aggressive or Covertly aggressive or Non-aggressive. We have proposed two deep learning systems: Convolutional Neural Network and Long Short Term Memory with two different input text representations, FastText and One-hot embeddings. We have found that the LSTM model with FastText embedding is performing better than other models for Hindi and Bangla datasets but for the English dataset, the CNN model with FastText embedding has performed better. We have also found that the performances of One-hot embedding and pre-trained FastText embedding are comparable. Our system got 11th and 10th positions for English Sub-task A and Sub-task B, respectively, 8th and 7th positions, respectively for Hindi Sub-task A and Sub-task B and 7th and 6th positions for Bangla Sub-task A and Sub-task B, respectively among the total submitted systems.