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Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling
通过潜在主题和话语建模对多方在线对话的成功新进入预测
潜在的話題と談話モデリングによるマルチパーティオンライン会話のための新規参入予測の成功
잠재 적 인 화제 와 언어 모델 링 을 통 해 다방면의 온라인 대화의 새로운 항목 을 성공 적 으로 예측 하 다
Predicción exitosa de nuevos ítems en conversaciones multipartidistas en línea a través del modelado de temas y discursos potenciales
Prédiction réussie de nouvelles entrées dans les conversations en ligne multipartites grâce à la modélisation de sujets potentiels et de discours
Успешное прогнозирование новых элементов многостороннего интерактивного диалога с помощью возможных тем и речевых моделей
Lingzhi Wang ¹, Jing Li 李菁 ², Xingshan Zeng 曾幸山 ³, Kam-Fai Wong 黄锦辉 ¹
¹ The Chinese University of Hong Kong, Hong Kong, China
中国 香港 香港中文大学
² The Hong Kong Polytechnic University, Hong Kong, China
中国 香港 香港理工大学
³ Huawei Noah’s Ark Lab, Hong Kong, China
中国 香港 华为诺亚方舟实验室
arXiv, 18 August 2021
Abstract

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people's everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group.

To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer's message will be responded to by other participants in a multi-party conversation (henceforth \textbf{Successful New-entry Prediction}). The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (\textbf{VAE}), to examine the topic content and discourse behavior from newcomer's chatting history and conversation's ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries.

Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others' discussions.
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