The Trumpiest Trump? Identifying a Subject's Most Characteristic Tweets

Pethe, Charuta; Skiena, Steven

doi:10.48550/arxiv.1909.04002

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Engagement of posts: We further examine the engagement of both categories. Prior studies have revealed a high correlation between the topic continuity and engagement of posts [31], [32]. Especially when evaluating the engagement between users, the number of replies (comments generated under a post) works as a significant predictive indicator.…”

Section: ) Daily Volume Of Posts and Commentsmentioning

confidence: 99%

A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

Zhu¹,

Haq²,

Lee³

et al. 2022

Preprint

View full text Add to dashboard Cite

Reddit consists of sub-communities that cover a focused topic. This paper provides a list of relevant subreddits for the ongoing Russo-Ukrainian crisis. We perform an exhaustive subreddit exploration using keyword search and shortlist 12 subreddits as potential candidates that contain nominal discourse related to the crisis. These subreddits contain over 300,000 posts and 8 million comments collectively. We provide an additional categorization of content into two categories, "R-U Conflict", and "Military Related", based on their primary focus. We further perform content characterization of those subreddits. The results show a surge of posts and comments soon after Russia launched the invasion. "Military Related" posts are more likely to receive more replies than "R-U Conflict" posts. Our textual analysis shows an apparent preference for the Pro-Ukraine stance in "R-U Conflict", while "Military Related" retain a neutral stance.

show abstract

Section: ) Daily Volume Of Posts and Commentsmentioning

confidence: 99%

A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

Zhu¹,

Haq²,

Lee³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This task is inspired by some previous works, such as newcomers' socialization [12,20,21], social expression [16] and response prediction [3,4], etc. The reasons why we employ "receiving a reply" to represent successful engagement can be summarized as twofold: (1) Pethe and Skiena [36] shows that replies, likes (which are used to show appreciation for a post [34]), retweets are highly correlated. (2) We conduct an interesting human evaluation (see Table 6), and the results show that posts that received replies are more successful than posts with silent responses in four indicators.…”

Section: Introductionmentioning

confidence: 99%

Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling

Wang¹,

Li²,

Zeng³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people's everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer's message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction). The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer's chatting history and conversation's ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others' discussions. CCS CONCEPTS• Human-centered computing → Collaborative and social computing; • Information systems → Personalization.

show abstract

The Trumpiest Trump? Identifying a Subject's Most Characteristic Tweets

Cited by 2 publications

References 0 publications

A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling

Contact Info

Product

Resources

About