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

Tran, Trang; Ostendorf, Mari

doi:10.18653/v1/d16-1108

Cited by 34 publications

(30 citation statements)

References 14 publications

(11 reference statements)

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“…This is analogous with previous work on computational sociolinguistics, where researchers have demonstrated that online discussion forums create community norms about language usage, and members adapt their language to conform to those norms (Nguyen et al, 2016). Especially on Reddit, language style is an essential indicator of community identity (Tran and Ostendorf, 2016;Chancellor et al, 2018). Our schemas help us make similar observations, showing that dominant user posting styles in each subreddit seem to be ones that conform to subreddit descriptions.…”

Section: Discussionsupporting

confidence: 77%

Using Functional Schemas to Understand Social Media Narratives

Yan¹,

Naik²,

Jo³

et al. 2019

Proceedings of the Second Workshop on Storytelling

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We propose a novel take on understanding narratives in social media, focusing on learning "functional story schemas", which consist of sets of stereotypical functional structures. We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits. We validate our schemas through human interpretation and evaluate their utility via a text classification task. Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4% on average. We also observe that these schemas serve as lenses that reveal community norms.

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Section: Discussionsupporting

confidence: 77%

Using Functional Schemas to Understand Social Media Narratives

Yan¹,

Naik²,

Jo³

et al. 2019

Proceedings of the Second Workshop on Storytelling

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“…Dansih et al show that a comment's timing relative to the post matter in eliciting responses in the community r/IAmA [5]. Lakkaraju et al/ [6] and Tran et al [7] show that reddit post titles and timing are important factors in the popularity of a post. However, this popularity can be delayed.…”

Section: Related Workmentioning

confidence: 99%

Identifying the Social Signals That Drive Online Discussions: A Case Study of Reddit Communities

Horne

Adalı

Sikdar

2017

2017 26th International Conference on Computer Communication and Networks (ICCCN)

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Abstract-Increasingly people form opinions based on information they consume on online social media. As a result, it is crucial to understand what type of content attracts people's attention on social media and drive discussions. In this paper we focus on online discussions. Can we predict which comments and what content gets the highest attention in an online discussion? How does this content differ from community to community? To accomplish this, we undertake a unique study of Reddit involving a large sample comments from 11 popular subreddits with different properties. We introduce a large number of sentiment, relevance, content analysis features including some novel features customized to reddit. Through a comparative analysis of the chosen subreddits, we show that our models are correctly able to retrieve top replies under a post with great precision. In addition, we explain our findings with a detailed analysis of what distinguishes high scoring posts in different communities that differ along the dimensions of the specificity of topic and style, audience and level of moderation.

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“…Further, Reddit is a widely used platform, ranking 6th in global popularity according to alexa.com in 2018. Reddit has also been shown useful in other prediction tasks, such as ranking popular comments in discussions [6] and general popularity studies [4,11,25].…”

Section: Datamentioning

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

Cited by 34 publications

References 14 publications

Using Functional Schemas to Understand Social Media Narratives

Using Functional Schemas to Understand Social Media Narratives

Identifying the Social Signals That Drive Online Discussions: A Case Study of Reddit Communities

Models for Predicting Community-Specific Interest in News Articles

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