• Corpus ID: 11830141

Automatically Identifying Good Conversations Online (Yes, They Do Exist!)

@inproceedings{Napoles2017AutomaticallyIG,
  title={Automatically Identifying Good Conversations Online (Yes, They Do Exist!)},
  author={Courtney Napoles and Aasish Pappu and Joel R. Tetreault},
  booktitle={ICWSM},
  year={2017}
}
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… 

Figures and Tables from this paper

Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom
TLDR
A semi-automatic, holistic approach to comment moderation is proposed, which includes comment features but also their context, such as information about users and articles, for evaluation.
Prediction for the Newsroom: Which Articles Will Get the Most Comments?
TLDR
This work proposes to support manual moderation by proactively drawing the attention of moderators to article discussions that most likely need their intervention, and enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus.
A Dataset of Journalists' Interactions with Their Readership: When Should Article Authors Reply to Reader Comments?
TLDR
A dataset of dialogs in which journalists of The Guardian replied to reader comments is presented and the novel task of recommending reader comments to journalists that are worth reading or replying to is formulated, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction.
Identifying Collaborative Conversations using Latent Discourse Behaviors
TLDR
A hybrid relational model is defined in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks, which provide the information needed for predicting the overall collaborative characterization of the entire conversational thread.
Modeling Global and Local Interactions for Online Conversation Recommendation
TLDR
A novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories is presented, which significantly outperforms the state-of-the-art model.
Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions
TLDR
A Conversational Structure Aware Topic Model (CSATM) is built based on popularity and transitivity to infer topics and their assignments to comments and demonstrates improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.
Dataset Creation for Ranking Constructive News Comments
TLDR
This paper addresses directly evaluating the quality of comments on the basis of “constructiveness,” separately from user feedback, by creating a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores).
Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
TLDR
A series of new theory-inspired metrics are introduced to define prosocial outcomes such as mentoring and esteem enhancement within online discussions, using a corpus of 26M Reddit conversations to show that these outcomes can be forecasted from the initial comment of an online conversation.
X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums
TLDR
An in-depth analysis indicates that controversial posts in Reddit do not arise as troll-like behavior, but are often due to a polarizing topic, off-topic content, or mentions of individual entities such as soccer players or clubs.
A Case Study of In-House Competition for Ranking Constructive Comments in a News Service
TLDR
A case study in which an in-house competition to improve the performance of ranking constructive comments and demonstrate the effectiveness of the best obtained model for a commercial service is examined.
...
...

References

SHOWING 1-10 OF 18 REFERENCES
Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus
TLDR
This work presents a dataset and annotation scheme for the new task of identifying “good” conversations that occur online, which it is called ERICs: Engaging, Respectful, and/or Informative Conversations, which is one of the largest annotated corpora of online human dialogues, with the most detailed set of annotations.
Talking to the crowd: What do people react to in online discussions?
TLDR
A new comment ranking task is proposed based on community annotated karma in Reddit discussions, which controls for topic and timing of comments, and the relative importance of the message vs. the messenger.
Exploiting Conversational Features to Detect High-Quality Blog Comments
TLDR
This approach to classifying the quality of blog comments using Linear-Chain Conditional Random Fields (CRFs) is found to yield high accuracy on binary classification of high-quality comments, with conversational features contributing strongly to the accuracy.
Characterizing and curating conversation threads: expansion, focus, volume, re-entry
TLDR
This work develops and evaluates a range of approaches for two key sub-problems inherent in conversational curation: length prediction and the novel task of re-entry prediction, based on an analysis of the network structure and arrival pattern among the participants, as well as a novel dichotomy in the structure of long threads.
Discovering High-Quality Threaded Discussions in Online Forums
TLDR
An automatic method for distinguishing high-quality threads from low-quality ones in online discussion sites is proposed and experimental results demonstrate that this method can significantly improve the prediction performance across all four measures of thread quality on both tasks.
Quantifying Controversy in Social Media
TLDR
This paper performs a systematic methodological study of controversy detection using social media network structure and content, and finds that a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy.
Hunting for Troll Comments in News Community Forums
TLDR
In this work, two classifiers are built that can distinguish a post by such a paid troll from one by a non-troll with 81-82% accuracy; the same classifier achieves 81- 82% accuracy on so called mentioned troll vs. non-Troll posts.
Public Dialogue: Analysis of Tolerance in Online Discussions
TLDR
This work performs a computational study of tolerance in the context of online discussions to identify tolerant vs. intolerant participants and investigate how disagreement affects tolerance in discussions in a quantitative framework.
Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it
TLDR
The INTERNET ARGUMENT CORPUS 2.0 is released, one of the first larger scale resources available for opinion sharing dialogue, and the generalizablity of the schema is demonstrated by providing code to import the ConVote corpus.
Conversational Markers of Constructive Discussions
TLDR
This work proposes a framework for analyzing conversational dynamics in order to determine whether a given task-oriented discussion is worth having or not, and applies it to conversations naturally occurring in an online collaborative world exploration game developed and deployed to support this research.
...
...