Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The WNBA and the Indiana Fever announced that the allegations of "hateful comments" directed toward Angel Reese on May 17 were "not substantiated." Reese and her Chicago Sky faced the Fever and Caitlin Clark, and at one point, the two had to be separated after a flagrant foul by Clark against Reese. The association announced the next day that it would launch an investigation into the alleged comments.

User-generated replies to hate speech are promising means to combat hatred, but questions about whether they can stop incivility in follow-up conversations linger. We argue that effective replies stop incivility from emerging in follow-up conversations - replies that elicit more incivility are counterproductive. This study introduces the task of predicting the incivility of conversations following replies to hate speech. We first propose a metric to measure conversation incivility based on the number of civil and uncivil comments as well as the unique authors involved in the discourse. Our metric approximates human judgments more accurately than previous metrics. We then use the metric to evaluate the outcomes of replies to hate speech. A linguistic analysis uncovers the differences in the language of replies that elicit follow-up conversations with high and low incivility. Experimental results show that forecasting incivility is challenging. We close with a qualitative analysis shedding light into the most common errors made by the best model.

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.

Instagram believes artificial intelligence could help fight trolls and junk messages. The photo-sharing service debuted Thursday two new tools it said would help it reduce the amount of spam it receives as well block offensive comments that appear on posts and live video. People can choose to have the automatic comment filter on or off. If an out-of-line comment still appears on a certain post with the filter on, people can still report it to Instagram as they would typically do. Instagram said that the comment filter is only available in English, but will debut in other languages at later dates.