Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling

TL;DR

This paper introduces a novel approach to predict whether a newcomer’s message in multi-party online conversations will receive responses, using latent topic and discourse modeling with neural networks, supported by large-scale Reddit and Twitter datasets.

Contribution

It proposes a new task of successful new-entry prediction and employs an unsupervised VAE model to analyze conversation content and behavior, outperforming existing methods.

Findings

Model significantly outperforms baselines and neural models

Large-scale Reddit and Twitter datasets support the research

Explainable analysis provides insights into new-entry behavior

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 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 (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.