Papers by Haewoon Kwak
PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media (2026.acl-long)
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| Challenge: | Social media are shifting towards community-governed platforms where groups define their own norms. |
| Approach: | They propose a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities . they show that bigger models and increased context provide marginal gains, and universal rules like civility and self-promotion are easier to detect. |
| Outcome: | The proposed model can detect 13,371 rule violations across 1,989 Reddit communities across 2,885 rules in 9 languages. |
Vulnerability of LLMs’ Stated Belief? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly employed in question-answering tasks. |
| Approach: | They analyze how different persuasive strategies influence stated belief stability . they also examine whether verbalized confidence prompting increases vulnerability . |
| Outcome: | The proposed model exhibits extreme compliance, with 82.5% of belief changes occurring at the first persuasive turn. |
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment (P18-1)
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| Challenge: | SemAxis characterizes word semantics using many semantic axes in word-vector spaces beyond sentiment . lexicon-based text analysis assumes that meaning of words does not change across contexts . but, recent advances in vector-space representations can tackle this challenge . |
| Approach: | They propose a framework to characterize word semantics using many semantic axes beyond sentiment . they demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches in building domain-specific sentiment lexicons. |
ChatGPT Rates Natural Language Explanation Quality like Humans: But on Which Scales? (2024.lrec-main)
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| Challenge: | Traditionally, evaluating NLEs through gathering human judgments is a tedious task due to the subjective nature of human evaluations. |
| Approach: | They examine the alignment between ChatGPT and human assessments across multiple scales and compare them using paired comparisons and dynamic prompting. |
| Outcome: | The proposed model aligns better with humans in coarser scales and provides semantically similar examples in the prompt. |
Tanbih: Get To Know What You Are Reading (D19-3)
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Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, Kareem Darwish, James Glass, Preslav Nakov
| Challenge: | Nowadays, more and more readers consume news online. |
| Approach: | They propose a news platform that displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting and stance with respect to various claims and topics of a media outlet. |
| Outcome: | The proposed news platform displays news grouped into events and generates media profiles that show the factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting and stance with respect to various claims and topics of a news outlet. |
REMATCH: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity (2024.findings-naacl)
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| Challenge: | Existing AMR metrics are inefficient and struggle to capture semantic similarity . Existing metrics are not efficient and lack a systematic evaluation benchmark . |
| Approach: | They propose a new AMR similarity metric, rematch, which matches graphs structurally and semantically to each other. |
| Outcome: | The proposed metric is five times faster than the next most efficient metric. |
Predicting Anti-Asian Hateful Users on Twitter during COVID-19 (2021.findings-emnlp)
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| Challenge: | Xenophobia and polarization have accompanied widespread social media usage in many nations, attracting many researchers. |
| Approach: | They apply natural language processing techniques to characterize Twitter users who began to post anti-Asian hate messages during COVID-19. |
| Outcome: | The results show that it is possible to predict who later posted anti-Asian slurs on Twitter and Reddit. |
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context (2020.acl-main)
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Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, Preslav Nakov
| Challenge: | a growing number of fake news reports are published online, causing a trust crisis . a new study aims to predict political bias and factuality of reporting of entire news outlets . |
| Approach: | They propose to profile entire news outlets and look for those that are likely to publish fake content . they also examine what was written about the target medium and who reads it . |
| Outcome: | The proposed method improves on the current state-of-the-art in analyzing social media and what was written about the target medium. |
A Survey on Predicting the Factuality and the Bias of News Media (2024.findings-acl)
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| Challenge: | a growing number of scholars are profiling entire news outlets to profile fake content . political bias detection is also an important topic, but the two problems have been addressed separately . |
| Approach: | They argue that media profiling should be based on factuality and bias together . they argue that it is difficult to fact-check every single suspicious claim or article manually . |
| Outcome: | The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically. |