Talking to the crowd: What do people react to in online discussions?

Jaech, Aaron; Zayats, Victoria; Fang, Hao; Ostendorf, Mari; Hajishirzi, Hannaneh

doi:10.18653/v1/d15-1239

Cited by 31 publications

(51 citation statements)

References 15 publications

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“…As baselines, we train the classifiers using only content-agnostic features, as shown in Table 1, which have strong correlations with community endorsement (Jaech et al, 2015;. In our pilot work, we experimented with several groups of features from (Jaech et al, 2015) to find the content-agnostic features used in our paper.…”

Section: Classifiersmentioning

confidence: 99%

See 1 more Smart Citation

A Factored Neural Network Model for Characterizing Online Discussions in Vector Space

Cheng¹,

Fang²,

Ostendorf³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.

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Section: Classifiersmentioning

confidence: 99%

“…Predicting the community endorsement has been studied by using either hand-crafted features (Jaech et al, 2015) or neural models Zayats and Ostendorf, 2017), but all of them focus on supervised learning. Unsupervised learning strategies have been explored for characterizing different factors in language.…”

Section: Related Workmentioning

confidence: 99%

A Factored Neural Network Model for Characterizing Online Discussions in Vector Space

Cheng¹,

Fang²,

Ostendorf³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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“…Lakkaraju et al (2013) proposed a community model to predict the popularity of a resubmitted content, revealing that its title plays a substantial role. Jaech et al (2015) considered timing and a variety of language features in ranking comments for popularity, finding significant differences across different communities. In our work, we focus on community language, but explore different models to account for it.…”

Section: Related Workmentioning

confidence: 99%

Characterizing the Language of Online Communities and its Relation to Community Reception

Tran

Ostendorf

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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This work investigates style and topic aspects of language in online communities: looking at both utility as an identifier of the community and correlation with community reception of content. Style is characterized using a hybrid word and part-of-speech tag n-gram language model, while topic is represented using Latent Dirichlet Allocation. Experiments with several Reddit forums show that style is a better indicator of community identity than topic, even for communities organized around specific topics. Further, there is a positive correlation between the community reception to a contribution and the style similarity to that community, but not so for topic similarity.

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“…Even more general, there is a large body of work on content popularity prediction, not focused on news articles. These works include predicting the popularity of comments on Reddit [6,10], predicting the popularity of tweets or hashtags on Twitter [16,26], and predicting the popularity of videos [14,23].…”

Section: Related Workmentioning

confidence: 99%

Models for Predicting Community-Specific Interest in News Articles

Horne

Dron

Adalı

2018

MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)

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In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communities on reddit.com. We train and test models over three different time periods between 2015 and 2017 to demonstrate which features degrade in performance the most due to concept drift. Our models can classify news articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0 ROC AUC. However, while we can predict the community-specific popularity of news articles with high accuracy, practitioners should approach these models carefully. Predictions are both community-pair dependent and feature group dependent. Moreover, these feature groups generalize over time differently, with some only degrading slightly over time, but others degrading greatly. Therefore, we recommend that community-interest predictions are done in a hierarchical structure, where multiple binary classifiers can be used to separate community pairs, rather than a traditional multi-class model. Second, these models should be retrained over time based on accuracy goals and the availability of training data.

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Talking to the crowd: What do people react to in online discussions?

Cited by 31 publications

References 15 publications

A Factored Neural Network Model for Characterizing Online Discussions in Vector Space

A Factored Neural Network Model for Characterizing Online Discussions in Vector Space

Characterizing the Language of Online Communities and its Relation to Community Reception

Models for Predicting Community-Specific Interest in News Articles

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