import pandas as pd from datasets import load_dataset from sklearn.model_selection import train_test_split import urllib # Set binary labels HATE = 1 NOT_HATE = 0 # class mapping for the multi-class class_mapping = { 'target_gender_aggregated': 0, 'target_race_aggregated': 1, 'target_sexuality_aggregated': 2, 'target_religion_aggregated': 3, 'target_origin_aggregated': 4, 'target_disability_aggregated': 5, 'target_age_aggregated': 6, 'not_hate': 7 } # map continuous score to classes def map_label(x): if x >= -1 and x <= 0.5: label = 999 # neutral/ambiguous elif x > 0.5: label = HATE # hate elif x < -1: label = NOT_HATE # not hate return label # format text def clean_text(text): text = text.replace('\n', ' ').replace('\r', ' ').replace('\t', ' ') new_text = [] for t in text.split(): # MAKE SURE to check lowercase t = '@user' if t.startswith('@') and len(t) > 1 and t.replace('@','').lower() not in verified_users else t t = '{URL}' if (t.startswith('http') or t.startswith('URL')) else t new_text.append(t) return ' '.join(new_text) # load data dataset = load_dataset('ucberkeley-dlab/measuring-hate-speech') df = dataset['train'].to_pandas() # get label df['annon_label'] = df['hate_speech_score'].apply(map_label) # keep only entries from Twitter df = df[df['platform'] == 2] # ignore ambiguous df = df[df['annon_label'].isin([HATE, NOT_HATE])] # count binary label df_count_label = pd.DataFrame(df.groupby('comment_id')['annon_label'].value_counts()) df_count_label = df_count_label.rename(columns={'annon_label': 'count'}) df_count_label = df_count_label.reset_index(level=1) df_count_label = df_count_label[df_count_label['count'] >= 2] # map binary label df = df.set_index('comment_id') df['label'] = None df['label'] = df_count_label['annon_label'] # drop entries with no agreement df = df[df['label'].notnull()] df = df.reset_index() # find aggrement on targets targets = ['target_race', 'target_religion', 'target_origin', 'target_gender', 'target_sexuality', 'target_age', 'target_disability'] # for each target count aggrement for t in targets: # count and consider only targets with at least 2 coders df_count_targets = pd.DataFrame(df.groupby('comment_id')[t].value_counts()) df_count_targets = df_count_targets.rename(columns={t: 'count'}) df_count_targets = df_count_targets.reset_index(level=1) df_count_targets = df_count_targets[df_count_targets['count'] >= 2] # do not consider entries with more than one target (because of more than 3 coders) df_count_targets = df_count_targets.loc[df_count_targets.index.drop_duplicates(keep=False)] # map aggregated target df = df.set_index('comment_id') df[f'{t}_aggregated'] = False df[f'{t}_aggregated'] = df_count_targets[t] df[f'{t}_aggregated'] = df[f'{t}_aggregated'].fillna(False) df = df.reset_index() # aggregate targets targets_aggregated = [f'{t}_aggregated' for t in targets] # get columns/target which are True df['target'] = df[targets_aggregated].apply(lambda row: row[row].index, axis=1) # set target only if it is unique df['target'] = df['target'].apply(lambda x: x[0] if len(x) == 1 else None) # no need of all annotators now -> keep each tweet only once df = df.groupby('comment_id').nth(0) df = df.reset_index() # clean multiclass # give target only to tweets with 1 (is hate speech) target idx_multiclass = df[df['label'] == 1].index idx_not_hate = df[df['label'] == 0].index # initialize column df['gold_label'] = None df.loc[idx_not_hate, 'gold_label'] = 'not_hate' df.loc[idx_multiclass, 'gold_label'] = df.loc[idx_multiclass]['target'] # drop entries without target df = df.dropna(subset='gold_label') # get list of known verified users verified_users = urllib.request.urlopen('https://raw.githubusercontent.com/cardiffnlp/timelms/main/data/verified_users.v091122.txt').readlines() verified_users = [x.decode().strip('\n').lower() for x in verified_users] # clean text df['text'] = df['text'].apply(clean_text) # map classes df['gold_label'] = df['gold_label'].map(class_mapping) # create splits test_size = int(0.2 * len(df)) val_size = int(0.1 * len(df)) train, test = train_test_split(df, test_size=test_size, stratify=df['gold_label'].values, random_state=4) train, val = train_test_split(train, test_size=val_size, stratify=train['gold_label'].values, random_state=4) # save splits cols_to_keep = ['gold_label', 'text'] train[cols_to_keep].to_json('../data/tweet_hate/train.jsonl', lines=True, orient='records') val[cols_to_keep].to_json('../data/tweet_hate/validation.jsonl', lines=True, orient='records') test[cols_to_keep].to_json('../data/tweet_hate/test.jsonl', lines=True, orient='records')