Automated Hate Speech Detection and the Problem of Offensive Language

Replication for results in Davidson et al. 2017. "Automated Hate Speech Detection and the Problem of Offensive Language"

import pandas as pd import numpy as np import pickle import sys from sklearn.feature_extraction.text import TfidfVectorizer import nltk from nltk.stem.porter import * import string import re from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as VS from textstat.textstat import * from sklearn.linear_model import LogisticRegression from sklearn.feature_selection import SelectFromModel from sklearn.metrics import classification_report from sklearn.svm import LinearSVC import matplotlib.pyplot as plt import seaborn %matplotlib inline
## Loading the data
df = pd.read_csv("../data/labeled_data.csv")
### Columns key: count = number of CrowdFlower users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable by CF). hate_speech = number of CF users who judged the tweet to be hate speech. offensive_language = number of CF users who judged the tweet to be offensive. neither = number of CF users who judged the tweet to be neither offensive nor non-offensive. class = class label for majority of CF users. 0 - hate speech 1 - offensive language 2 - neither tweet = raw tweet text
This histogram shows the imbalanced nature of the task - most tweets containing "hate" words as defined by Hatebase were only considered to be offensive by the CF coders. More tweets were considered to be neither hate speech nor offensive language than were considered hate speech.
## Feature generation
stopwords=stopwords = nltk.corpus.stopwords.words("english") other_exclusions = ["#ff", "ff", "rt"] stopwords.extend(other_exclusions) stemmer = PorterStemmer() def preprocess(text_string): """ Accepts a text string and replaces: 1) urls with URLHERE 2) lots of whitespace with one instance 3) mentions with MENTIONHERE This allows us to get standardized counts of urls and mentions Without caring about specific people mentioned """ space_pattern = '\s+' giant_url_regex = ('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|' '[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+') mention_regex = '@[\w\-]+' parsed_text = re.sub(space_pattern, ' ', text_string) parsed_text = re.sub(giant_url_regex, '', parsed_text) parsed_text = re.sub(mention_regex, '', parsed_text) return parsed_text def tokenize(tweet): """Removes punctuation & excess whitespace, sets to lowercase, and stems tweets. Returns a list of stemmed tokens.""" tweet = " ".join(re.split("[^a-zA-Z]*", tweet.lower())).strip() tokens = [stemmer.stem(t) for t in tweet.split()] return tokens def basic_tokenize(tweet): """Same as tokenize but without the stemming""" tweet = " ".join(re.split("[^a-zA-Z.,!?]*", tweet.lower())).strip() return tweet.split() vectorizer = TfidfVectorizer( tokenizer=tokenize, preprocessor=preprocess, ngram_range=(1, 3), stop_words=stopwords, use_idf=True, smooth_idf=False, norm=None, decode_error='replace', max_features=10000, min_df=5, max_df=0.75 )
import warnings warnings.simplefilter(action='ignore', category=FutureWarning)
#Construct tfidf matrix and get relevant scores tfidf = vectorizer.fit_transform(tweets).toarray() vocab = {v:i for i, v in enumerate(vectorizer.get_feature_names())} idf_vals = vectorizer.idf_ idf_dict = {i:idf_vals[i] for i in vocab.values()} #keys are indices; values are IDF scores
#Get POS tags for tweets and save as a string tweet_tags = [] for t in tweets: tokens = basic_tokenize(preprocess(t)) tags = nltk.pos_tag(tokens) tag_list = [x[1] for x in tags] tag_str = " ".join(tag_list) tweet_tags.append(tag_str)
#We can use the TFIDF vectorizer to get a token matrix for the POS tags pos_vectorizer = TfidfVectorizer( tokenizer=None, lowercase=False, preprocessor=None, ngram_range=(1, 3), stop_words=None, use_idf=False, smooth_idf=False, norm=None, decode_error='replace', max_features=5000, min_df=5, max_df=0.75, )
#Construct POS TF matrix and get vocab dict pos = pos_vectorizer.fit_transform(pd.Series(tweet_tags)).toarray() pos_vocab = {v:i for i, v in enumerate(pos_vectorizer.get_feature_names())}
#Now get other features sentiment_analyzer = VS() def count_twitter_objs(text_string): """ Accepts a text string and replaces: 1) urls with URLHERE 2) lots of whitespace with one instance 3) mentions with MENTIONHERE 4) hashtags with HASHTAGHERE This allows us to get standardized counts of urls and mentions Without caring about specific people mentioned. Returns counts of urls, mentions, and hashtags. """ space_pattern = '\s+' giant_url_regex = ('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|' '[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+') mention_regex = '@[\w\-]+' hashtag_regex = '#[\w\-]+' parsed_text = re.sub(space_pattern, ' ', text_string) parsed_text = re.sub(giant_url_regex, 'URLHERE', parsed_text) parsed_text = re.sub(mention_regex, 'MENTIONHERE', parsed_text) parsed_text = re.sub(hashtag_regex, 'HASHTAGHERE', parsed_text) return(parsed_text.count('URLHERE'),parsed_text.count('MENTIONHERE'),parsed_text.count('HASHTAGHERE')) def other_features(tweet): """This function takes a string and returns a list of features. These include Sentiment scores, Text and Readability scores, as well as Twitter specific features""" sentiment = sentiment_analyzer.polarity_scores(tweet) words = preprocess(tweet) #Get text only syllables = textstat.syllable_count(words) num_chars = sum(len(w) for w in words) num_chars_total = len(tweet) num_terms = len(tweet.split()) num_words = len(words.split()) avg_syl = round(float((syllables+0.001))/float(num_words+0.001),4) num_unique_terms = len(set(words.split())) ###Modified FK grade, where avg words per sentence is just num words/1 FKRA = round(float(0.39 * float(num_words)/1.0) + float(11.8 * avg_syl) - 15.59,1) ##Modified FRE score, where sentence fixed to 1 FRE = round(206.835 - 1.015*(float(num_words)/1.0) - (84.6*float(avg_syl)),2) twitter_objs = count_twitter_objs(tweet) retweet = 0 if "rt" in words: retweet = 1 features = [FKRA, FRE,syllables, avg_syl, num_chars, num_chars_total, num_terms, num_words, num_unique_terms, sentiment['neg'], sentiment['pos'], sentiment['neu'], sentiment['compound'], twitter_objs[2], twitter_objs[1], twitter_objs[0], retweet] #features = pandas.DataFrame(features) return features def get_feature_array(tweets): feats=[] for t in tweets: feats.append(other_features(t)) return np.array(feats)
other_features_names = ["FKRA", "FRE","num_syllables", "avg_syl_per_word", "num_chars", "num_chars_total", \ "num_terms", "num_words", "num_unique_words", "vader neg","vader pos","vader neu", \ "vader compound", "num_hashtags", "num_mentions", "num_urls", "is_retweet"]
feats = get_feature_array(tweets)
#Now join them all up M = np.concatenate([tfidf,pos,feats],axis=1)
#Finally get a list of variable names variables = ['']*len(vocab) for k,v in vocab.items(): variables[v] = k pos_variables = ['']*len(pos_vocab) for k,v in pos_vocab.items(): pos_variables[v] = k feature_names = variables+pos_variables+other_features_names
# Running the model The best model was selected using a GridSearch with 5-fold CV.
X = pd.DataFrame(M) y = df['class'].astype(int)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.1)
from sklearn.model_selection import StratifiedKFold, GridSearchCV from sklearn.pipeline import Pipeline
pipe = Pipeline( [('select', SelectFromModel(LogisticRegression(class_weight='balanced', penalty="l1", C=0.01))), ('model', LogisticRegression(class_weight='balanced',penalty='l2'))])
param_grid = [{}] # Optionally add parameters here
grid_search = GridSearchCV(pipe, param_grid, cv=StratifiedKFold(n_splits=5, random_state=42).split(X_train, y_train), verbose=2)
model =, y_train)
y_preds = model.predict(X_test)
## Evaluating the results
report = classification_report( y_test, y_preds )
***Note: Results in paper are from best model retrained on the entire dataset (see the other notebook). Here the results are reported after using cross-validation and only for the held-out set.***
from sklearn.metrics import confusion_matrix confusion_matrix = confusion_matrix(y_test,y_preds) matrix_proportions = np.zeros((3,3)) for i in range(0,3): matrix_proportions[i,:] = confusion_matrix[i,:]/float(confusion_matrix[i,:].sum()) names=['Hate','Offensive','Neither'] confusion_df = pd.DataFrame(matrix_proportions, index=names,columns=names) plt.figure(figsize=(5,5)) seaborn.heatmap(confusion_df,annot=True,annot_kws={"size": 12},cmap='gist_gray_r',cbar=False, square=True,fmt='.2f') plt.ylabel(r'True categories',fontsize=14) plt.xlabel(r'Predicted categories',fontsize=14) plt.tick_params(labelsize=12) #Uncomment line below if you want to save the output #plt.savefig('confusion.pdf')
#True distribution y.hist()

Run this article as a notebook

Deepnote is a new kind of data science notebook. Jupyter-compatible and with real-time collaboration.

Sign-up for the waitlist below, or find out more here.

To be continued...