CoSyn

Implementation of CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network.

🧱 Dependencies and Setup

🧰 Dependencies

• torch 1.11.0
• geoopt 0.1.0
• dgl 1.0.0
• other required packages in requirements.txt

🛠️ Setup

# git clone this repository
git clone https://github.com/MananSuri27/CoSyn
cd CoSyn

# install python dependencies
pip3 install -r requirements.txt

We will need to move the code for Hyperbolic Graph Comvolution models/hgconv.py to the dgl library.

mv CoSyn/models/hgconv.py path-to-dgl/dgl/python/dgl/nn/pytorch/conv/

and correspondingly update the export statememt at path-to-dgl/dgl/python/dgl/nn/pytorch/conv/__init__.py /.

🔌 Dataset Processing

💬 Conversation Trees

Required Files:

• Node and edge features stored in members and interactions directory for each split train, dev, test. For each conversation tree, corresponding to a given parent node id tweet_id, there will be a members/tweet_id.csv and interactions/tweet_id.csv having the node features and edge list respectively.
• A file, username2id.csv which maps usernames to an id between [0,n) where n is number of users.
• Post embeddings for each post saved in the embeds directory.

To generate the conversation trees and load them as a pickle file, run the following code:

python3 utils/graphs.py

🌐 Social Graph

Required Files:

• User relation matrices stored as [test/train/val]/matrix/file, where multiple files can exist in each split, and each file is an adjacency list representation of edges between given users.
• Post embeddings of the last m(=100 in our paper) posts posted by the user in embeds directory, referenced by user ID.
• A file, username2id.csv which maps usernames to an id between [0,n) where n is number of users.

To generate the conversation trees and load them as a pickle file, run the following code:

python3 utils/socialgraph.py

🏋️ Training

To run the training script, run the following code:

python3 main.py

The arguments for main.py are as follows:

 Arguments:  
  --x-size DIM          Embedding Dimension of Post
  --u-size DIM          Embedding Dimension of User 
  --g-size DIM          Output Dimension of HGCN
  --h-size DIM          Hidden Dimension of CHST
  --c C                 Curvature of Hyperbolic Space
  --batch-size  BS      Batch size
  --data-dir DIR        Directory for data
  --device DEVICE       Device
  --lr LR               Learning rate
  --dropout DROPOUT     Dropout probability
  --epochs EPOCHS       Maximum number of epochs to train for
  --weight-decay WEIGHT_DECAY
                        L2 regularization strength
  --optimizer OPTIMIZER
                        Which optimizer to use
  --patience PATIENCE   Patience for early stopping
  --save                Save computed results
  --save-dir SAVE_DIR   Path to save results
  --min-epochs MIN_EPOCHS
                        Do not early stop before min-epochs

🪛 Bias Invarient Encoder

📑 Citation

@misc{ghosh2023cosyn,
      title={CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network}, 
      author={Sreyan Ghosh and Manan Suri and Purva Chiniya and Utkarsh Tyagi and Sonal Kumar and Dinesh Manocha},
      year={2023},
      eprint={2303.03387},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}