Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks
We present an automated approach to analyze the text of an online conversation and determine whether one of the participants is a cyberpredator who is preying on another participant. The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation. Each stage uses a Recurrent Neural Network (RNN) to perform the classification task.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Explainable Detection of Sarcasm in Social Media
Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions ma…
Sarcasm DetectionInterpretable Multi-Head Self-Attention model for Sarcasm Detection in social media
Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, m…
Sarcasm DetectionQuestion-Answer Selection in User to User Marketplace Conversations
Sellers in user to user marketplaces can be inundated with questions from potential buyers. Answers are often already available in the product description. We collected a dataset of around 590K such questions and answers…
Answer SelectionQuestion AnsweringSentenceDialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in liv…
Emotion ClassificationEmotion Recognition in ConversationGeneral ClassificationMultimodal Emotion RecognitionConversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detect…