Conversation Modeling on Reddit Using a Graph-Structured LSTM

Conversation Modeling on Reddit Using a Graph-Structured LSTM Victoria Zayats Electrical Engineering Department University of Washington vzayats@uw.edu Mari Ostendorf Electrical Engineering Department University of Washington ostendor@uw.edu Abstract This paper presents a novel approach for mod- eling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and temporal conversation structure. In experiments with a task of predicting popularity of comments in Reddit discussions, the proposed model outperforms a node-independent architecture for different sets of input features. Analyses show a bene- fit to the model over the full course of the dis- cussion, improving detection in both early and late stages. Further, the use of language cues with the bidirectional tree state updates helps with identifying controversial comments. 1 Introduction Social media provides a convenient and widely used platform for discussions among users. When the comment-response links are preserved, those con- versations can be represented in a tree structure where comments represent nodes, the root is the original post, and each new reply to a previous com- ment is added as a child of that comment. Some examples of popular services with tree-like struc- tures include Facebook, Reddit, Quora, and Stack- Exchange. Figure 1 shows an example conversa- tion on Reddit, where bigger nodes indicate higher upvoting of a comment.1 In services like Twitter, 1The tool https://whichlight.github.io/ reddit-network-vis was used to obtain this visualiza- tion. Figure 1: Visualization of a sample thread on Reddit. tweets and their retweets can also be viewed as form- ing a tree structure. When time stamps are avail- able with a contribution, the nodes of the tree can be ordered and annotated with that information. The tree structure is useful for seeing how a discussion unfolds into different subtopics and showing differ- ences in the level of activity in different branches of the discussion. Predicting popularity of comments in social me- dia is a task of growing interest. Popularity has been defined in terms of the volume of the re- sponse, but when the social media platform has a mechanism for readers to like or dislike com- ments (or, upvote/downvote), then the difference in positive/negative votes provides a more informative score for popularity prediction. This definition of 121 Transactions of the Association for Computational Linguistics, vol. 6, pp. 121–132, 2018. Action Editor: Ani Nenkova. Submission batch: 11/2016; Revision batch: 3/2017; Published 2/2018. c⃝2018 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license. (a) Forward hierarchical and timing structure (b) Backward hierarchical and timing structure Figure 2: An example of model propagation in a graph-structured LSTM. Here, the node name are shown in a chrono- logical order, e.g. comment t1 was made earlier than t2. 2(a) Propagation of graph-structured LSTM in the forward direction. Blue arrows represent hierarchical propagation, green arrows represent timing propagation.2(b) Backward hierarchical (blue) and timing (green) propagation of graph-LSTM. popularity, which has also been called community endorsement (Fang et al., 2016), is the task of inter- est in our work on tree-structured modeling of dis- …

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