Uncovering Flaming Events on News Media in Social Media
Praboda Rajapaksha, Reza Farahbakhsh, Noel Crespi, Bruno Defude

TL;DR
This paper presents a deep neural network approach using word embeddings to detect flaming events in social media comments, focusing on negative sentiments to identify offensive feedback on news media posts.
Contribution
It introduces a sentiment classification model that effectively detects flaming by analyzing comments' sentiments, utilizing Word2Vec and FastText embeddings with an enhanced lexicon-based dataset.
Findings
Deep NN accurately classifies sentiment into five categories.
Negative and very negative comments are key indicators of flaming.
Model applied to news media comments on Facebook successfully detects flaming events.
Abstract
Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural…
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Taxonomy
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