Papers with online social media

5 papers
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model (D19-50)

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Challenge: a new task is needed to detect propaganda in news articles . the need for communication has increased in online social media platforms . a proposed solution to the problem of sentence-level propaganda classification is ranked 12th .
Approach: They propose to build a binary classifier able to provide corresponding propaganda labels . their solution ranks 12th among 26 teams in the NLP4IF-2019 Shared Task SLC .
Outcome: The proposed model outperforms baseline approach and the winning system on a similar task.
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer (P18-2)

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Challenge: Existing methods to tackle the problem of offensive language in social media are based on machine learning.
Approach: They propose a method for training encoder-decoders using non-parallel data . they use a collaborative classifier, attention and the cycle consistency loss .
Outcome: The proposed method outperforms state-of-the-art text style transfer systems on Twitter and Reddit . it produces reliable non-offensive transferred sentences, the authors show .
Multimodal Knowledge Learning for Named Entity Disambiguation (2022.findings-emnlp)

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Challenge: Existing attempts to model multimodal information at the knowledge level are lacking multimodal annotation data against the large-scale unlabeled corpus.
Approach: They propose to use multimodal knowledge learning to link ambiguous mentions with textual and visual contexts to a predefined knowledge graph.
Outcome: The proposed method achieves improvements over the state-of-the-art methods on two public MNED datasets.
Efficient Cross-modal Prompt Learning with Semantic Enhancement for Domain-robust Fake News Detection (2025.coling-main)

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Challenge: Existing MFND methods conduct cross-modal information interaction at later stage, resulting in weak generalization ability.
Approach: They propose an automatic multi-modal fake news detection method that exploits cross-modal information interaction at later stage.
Outcome: The proposed method outperforms state-of-the-art methods on three MFND benchmarks.
SPARK: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with LLM-based Agents (2025.emnlp-main)

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Challenge: a new framework for topic evolution and stance dynamics is needed to understand online discourse . topic evolution is central to understanding fragmentation of debates, spread of misinformation .
Approach: They propose a stance and topic evolution reasoning framework for co-evolution of topics and stances through natural language interactions.
Outcome: The proposed framework captures key empirical patterns across five real-world domains.

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