NDLI: Characterizing Topics in Social Media Using Dynamics of Conversation

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Characterizing Topics in Social Media Using Dynamics of Conversation

Content Provider	MDPI
Author	Flamino, James Gong, Bowen Buchanan, Frederick Szymanski, Boleslaw K.
Copyright Year	2021
Description	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.
Starting Page	1642
e-ISSN	10994300
DOI	10.3390/e23121642
Journal	Entropy
Issue Number	12
Volume Number	23
Language	English
Publisher	MDPI
Publisher Date	2021-12-07
Access Restriction	Open
Subject Keyword	Entropy Information and Library Science Information Dynamics Topic Modeling Semantic Analysis Network Entropy
Content Type	Text
Resource Type	Article

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