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

Horne, Benjamin D.; Adalı, Sibel; Sikdar, Sujoy

doi:10.1109/icccn.2017.8038388

Cited by 48 publications

(24 citation statements)

References 21 publications

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“…On Reddit forums, for example, community rules (injunctive norms) are often displayed in a sidebar next to a comment board [27] or in a 'sticky' comment at the top of a thread [41], and active discussions (descriptive norms) appear near the top of the page. If the behaviors exhibited in these active discussions reflect the injunctive norms displayed in the community rules, the injunctive and descriptive norms of the forum can be said to be congruent.…”

Section: Normative Congruencementioning

confidence: 99%

“…Unlike rules in Reddit sub-communities [38], the Teahouse Methods themselves cannot be enforced by banning users or deleting content. Also unlike Subreddit rules [27], the Methods are neither not posted on or linked to from the Q&A board itself. Instead, the Teahouse Methods are only shown to experienced Wikipedians who choose to sign up as project members, or "hosts".…”

Section: 32mentioning

confidence: 99%

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'Welcome' Changes?

Morgan

Filippova²

2018

Proc. ACM Hum.-Comput. Interact.

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Open online communities rely on social norms for behavior regulation, group cohesion, and sustainability. Research on the role of social norms online has mainly focused on one source of influence at a time, making it difficult to separate different normative influences and understand their interactions. In this study, we use the Focus Theory to examine interactions between several sources of normative influence in a Wikipedia sub-community: local descriptive norms, local injunctive norms, and norms imported from similar subcommunities. We find that exposure to injunctive norms has a stronger effect than descriptive norms, that the likelihood of performing a behavior is higher when both injunctive and descriptive norms are congruent, and that conflicting social norms may negatively impact pro-normative behavior. We contextualize these findings through member interviews, and discuss their implications for both future research on normative influence in online groups and the design of systems that support open collaboration.

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Section: Normative Congruencementioning

confidence: 99%

Section: 32mentioning

confidence: 99%

'Welcome' Changes?

Morgan

Filippova²

2018

Proc. ACM Hum.-Comput. Interact.

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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%

“…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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“…However, this stable representation of bias is only sparsely applied by the authors, being primarily used for the prediction of topic-specific influencers and the high-level analysis of Twitter’s follower network. Moreover, while extracting user reactions has been seen as an essential tool for social media analysis for a while [ 2 , 3 , 4 , 5 ], most implementations tend to be limited to analyzing verbal responses using natural language processing (NLP) and the quantification of baseline interest. Furthermore, whatever structural response analysis does exist [ 6 ] is often used to study individual and aggregate users, rather than examine the characteristics of topics.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Topics in Social Media Using Dynamics of Conversation

Flamino

Gong

Buchanan

et al. 2021

Entropy

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Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing “microscopic” trends to describe “macroscopic” phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.

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

Cited by 48 publications

References 21 publications

'Welcome' Changes?

'Welcome' Changes?

Models for Predicting Community-Specific Interest in News Articles

Characterizing Topics in Social Media Using Dynamics of Conversation

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