Abstract
This paper presents a novel approach for modeling 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 benefit to the model over the full course of the discussion, 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.
Highlights
Social media provides a convenient and widely used platform for discussions among users
In order to see how the model benefits from using the language cues in underpredicted and overpredicted scenarios, we look at the size of errors made by the graph-LSTM model with and without text features
This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM which represents both hierarchical and temporal conversation structure
Summary
Social media provides a convenient and widely used platform for discussions among users. Popularity has been defined in terms of the volume of the response, but when the social media platform has a mechanism for readers to like or dislike comments (or, upvote/downvote), the difference in positive/negative votes provides a more informative score for popularity prediction. This definition of Transactions of the Association for Computational Linguistics, vol 6, pp. The main contributions of this paper include: a novel approach for representing tree-structured language processes (e.g., social media discussions) with LSTMs; evaluation of the model on the popularity prediction task using Reddit discussions; and analysis of the performance gains, with respect to the role of language context
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