Open AccessProceedings Article
Automatically Identifying Good Conversations Online (Yes, They Do Exist!).
Courtney Napoles,Aasish Pappu,Joel Tetreault +2 more
- pp 628-631
TLDR
A new task is defined of identifying “good” conversations, which are called ERICs—Engaging, Respectful, and/or Informative Conversations, posted in response to online news articles and in debate forums.Abstract:
Online news platforms curate high-quality content for their readers and, in many cases, users can post comments in response. While comment threads routinely contain unproductive banter, insults, or users “shouting” over each other, there are often good discussions buried among the noise. In this paper, we define a new task of identifying “good” conversations, which we call ERICs—Engaging, Respectful, and/or Informative Conversations. Our model successfully identifies ERICs posted in response to online news articles with F1 = 0.73 and F1 = 0.91 in debate forums.read more
Citations
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Posted Content
Deep Learning for User Comment Moderation
TL;DR: Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of EnglishWikipedia comments, it is shown that an RNN outperforms the previous state of the art in moderation.
Proceedings ArticleDOI
Deep Learning for User Comment Moderation.
TL;DR: This paper used a deep, classification-specific attention mechanism to improve the overall performance of the RNN and compared with a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.
Journal ArticleDOI
The dark side of news community forums: opinion manipulation trolls
Todor Mihaylov,Tsvetomila Mihaylova,Preslav Nakov,Lluís Màrquez,Georgi Georgiev,Ivan Koychev +5 more
TL;DR: The idea that a user who is called a troll by several people is likely to be one further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community forums.
Journal ArticleDOI
When social media traumatizes teens: The roles of online risk exposure, coping, and post-traumatic stress
TL;DR: Empirical evidence is shown that suggests short-term coping responses are likely a stress reaction to PTSD, not a protective factor against it, which is the first study to examine situational PTSD symptoms related to four types of adolescent online risk exposure within the week exposure occurred.
Book ChapterDOI
Toxic Comment Detection in Online Discussions
Julian Risch,Ralf Krestel +1 more
TL;DR: This work describes the concept of toxicity and characterize its subclasses, and presents various deep learning approaches, including datasets and architectures, tailored to sentiment analysis in online discussions, including semi-automated comment moderation and troll detection.
References
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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Convolutional Neural Networks for Sentence Classification
TL;DR: In this article, CNNs are trained on top of pre-trained word vectors for sentence-level classification tasks and a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.
Software Framework for Topic Modelling with Large Corpora
Radim Řehůřek,Petr Sojka +1 more
TL;DR: This work describes a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion, and implements several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation in a way that makes them completely independent of the training corpus size.
Proceedings ArticleDOI
Abusive Language Detection in Online User Content
TL;DR: A machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach and a corpus of user comments annotated for abusive language, the first of its kind.
Proceedings ArticleDOI
Using Syntax to Disambiguate Explicit Discourse Connectives in Text
Emily Pitler,Ani Nenkova +1 more
TL;DR: It is demonstrated that syntactic features improve performance in both disambiguation tasks and state-of-the-art results for identifying discourse vs. non-discourse usage and human-level performance on sense disambIGuation are reported.