Healthy Twitter discussions? Time will tell
Dmitry Gnatyshak, Dario Garcia-Gasulla, Sergio Alvarez-Napagao, Jamie, Arjona, Tommaso Venturini

TL;DR
This paper investigates the health of online discussions, especially during early COVID-19, using temporal patterns and a novel concept of ephemerality to characterize and potentially label discussion quality without relying on ground truth.
Contribution
It introduces a new approach using temporal dynamics and ephemerality to analyze discussion health in the absence of labeled data, focusing on early COVID-19 debates.
Findings
Unsupervised characterization of discussion types based on volume and timing.
Formalization of ephemerality as an indicator of discussion health.
Discussion of potential for labeling online discourse health using ephemerality.
Abstract
Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available information and sources, rigorous manual analysis of such discourses has become unfeasible. Many approaches tackle the issue by studying the semantic and syntactic properties of discussions following a supervised approach, for example using natural language processing on a dataset labeled for abusive, fake or bot-generated content. Solutions based on the existence of a ground truth are limited to those domains which may have ground truth. However, within the context of misinformation, it may be difficult or even impossible to assign labels to instances. In this context, we consider the use of temporal dynamic patterns as an indicator of…
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Taxonomy
TopicsMisinformation and Its Impacts · Opinion Dynamics and Social Influence · Social Media and Politics
