Joint Effects of Context and User History for Predicting Online Conversation Re-entries

Abstract

As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage with each other online, we study a challenging problem of re-entry prediction foreseeing whether a user will come back to a conversation they once participated in. We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement. Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior. We experiment with two large-scale datasets collected from Twitter and Reddit. Results show that our proposed framework with biattention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-ofthe-art methods from previous work.

Neural Conversation Recommendation with Online Interaction Modeling

“…Our work is in line with conversation behavior analysis, where studies explore user interactions in ongoing conversations (Ritter et al, 2010) and how they signal the conversations' future trajectory, such as continued activity (Backstrom et al, 2013;Jiao et al, 2018;Zeng et al, 2019) and the risk of going awry . Different from these proposals which do not model personal interests, we study conversation recommendation for a specific user, where we measure how a user's preferences match a conversation's context.…”

“…Data Collection and Preprocessing. In our experiments, we use datasets from two different platforms: the first one is released by Zeng et al (2018) containing Twitter conversations formed by tweets from the TREC 2011 microblog track data 2 covering a diverse set of topics; the other is from Zeng et al (2019), which is comprised of discussion threads about political issues on Reddit, a popular discussion website. The tweets in Twitter dataset were mainly posted from Jan 23 to Feb 8, 2011, and discussion threads in Reddit dataset were posted from Jan to Dec, 2008.…”

“…Data Collection and Preprocessing. In our experiments, we use datasets from two different platforms: the first one is released by Zeng et al (2018) data 2 covering a diverse set of topics; the other is from Zeng et al (2019), which is comprised of discussion threads about political issues on Reddit, a popular discussion website.…”

“…Furthermore, our user interaction module is inspired by prior work on conversation structure modeling. Compared with popular sequential conversation models that focus on messages' temporal features (Cheng et al, 2017;Jiao et al, 2018;Zeng et al, 2019), our module explicitly encodes the replying relationships to exploit the user conversation structure (Miura et al, 2018;Zayats and Ostendorf, 2018). It is shown that such structure indicates salient messages and can benefit various compelling applications, e.g., conversation summarization (Chang et al, 2013;Li et al, 2015) and discussion topic extraction (Li et al, 2016 However, its effect on conversation recommendation has not been explored yet, and our work aims to fill the gap.…”

A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media

“…First, recurrent neural networks, particularly LSTMs, that are natural methods to learn patterns from a sequence of a user's historical tweets (Cao et al, 2019;Zeng et al, 2019Zeng et al, , 2020, assume uniform time gaps between successive tweets. However, tweets can be posted at irregular time intervals (Lei et al, 2018), and varying time gaps can influence the assessment of a user's suicidality progression (Chen et al, 2018), as shown in Figure 1.…”