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

Wang, Lingzhi; Li, Jing; Zeng, Xingshan; Wong, Kam-Fai

doi:10.1145/3485447.3512285

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Especially when evaluating the engagement between users, the number of replies (comments generated under a post) works as a significant predictive indicator. More replies mean more engagement among users, indicating the topic will continue in further discussion [23], [33].…”

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

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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.

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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

Predicting continuity of online conversations on Reddit

Kim

Han

Choi

2023

Telematics and Informatics

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A Unified Framework for Analyzing Textual Context and Intent in Social Media

2024

ACM Trans. Intell. Syst. Technol.

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In the realm of natural language processing, tasks like emotion recognition, irony detection, hate speech detection, offensive language identification, and stance detection are pivotal for understanding user-generated content. While several task-specific and multitask learning models have been proposed, there remains a need for a unified framework that can effectively address these tasks simultaneously. This research introduces a novel unified framework designed to tackle multiple NLP tasks concurrently, aiming to outperform existing task-specific and multitask models in terms of accuracy, F1-score, and AUC-ROC. We compared our proposed framework against several baseline models, including task-specific models like SVM, RF, LSTM, CNN, and BERT, as well as multitask learning frameworks such as Hard Parameter Sharing, Soft Parameter Sharing, Cross-stitch Networks, MMoE, and T5. The performance was evaluated across various tasks, and statistical significance was assessed using the Wilcoxon signed-rank test. Additionally, an ablation study was conducted to determine the contribution of individual components within our proposed method. The proposed framework consistently outperformed other models across all tasks. For instance, in emotion recognition, our model achieved an accuracy of 0.899, F1-score of 0.883, and AUC-ROC of 0.971, surpassing all baseline models. The Wilcoxon signed-rank test further confirmed the statistical superiority of our model over the baselines across all datasets.

show abstract

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

Cited by 3 publications

References 34 publications

A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

Predicting continuity of online conversations on Reddit

A Unified Framework for Analyzing Textual Context and Intent in Social Media

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