Characterizing Topics in Social Media Using Dynamics of Conversation.

James Flamino,Frederick Buchanan,Boleslaw K Szymanski,Bowen Gong

doi:10.3390/e23121642

Abstract

Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing “microscopic” trends to describe “macroscopic” phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.

Highlights

IntroductionConsider this question: if a person were to go to a movie theater and see a random movie without knowing the title or anything else about it beforehand, how could this person guess the movie’s genre while watching it?
We introduced a novel approach to analyzing social media information cascades using only the audience reactions triggered by a source post
We designed unique features to represent different aspects of discourse dynamics. Using these measures as response features to characterize an information cascade, we validated the effectiveness of these representative features through label classification and topic clustering, showing that response features can identify the differences between Subreddit genres without needing text

Summary

Introduction

Consider this question: if a person were to go to a movie theater and see a random movie without knowing the title or anything else about it beforehand, how could this person guess the movie’s genre while watching it? If the audience is laughing frequently, it is likely to be a comedy, but if they are mostly crying out in fear, the film is probably a horror movie. The director of a horror film needs the audience to react in fear; otherwise, their film will most likely fail

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