| Challenge: | Existing research on spam and scams on Instagram is limited to theoretical concepts and only a recall of 11.51%. |
| Approach: | They propose a system that includes a browser extension, a fine-tuned BERT model and a REST API to solve the problem of spam and fraudulent messages in the comment sections of Instagram pages. |
| Outcome: | The proposed system includes a browser extension, a fine-tuned BERT model and a REST API. |
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Shu Yang, Shenzhe Zhu, Zeyu Wu, Keyu Wang, Junchi Yao, Junchao Wu, Lijie Hu, Mengdi Li, Derek F. Wong, Di Wang
| Challenge: | Existing fraud detection benchmarks focus on single-turn classification tasks, failing to capture dynamic nature of real-world fraud attempts. |
| Approach: | They propose a bilingual benchmark to assess LLMs' ability to resist fraud and phishing attacks across five key fraud categories: Fraudulent Services, Impersonation, Phishing Scams, Fake Job Postings, and Online Relationships. |
| Outcome: | The proposed model improves in role-play settings and in e-commerce and recommendation systems. |
Financial Opinion Mining (2021.emnlp-tutorials)
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| Challenge: | This tutorial will provide an overview of financial opinion mining and provide research directions. |
| Approach: | This tutorial will introduce financial opinion mining and examine possible research directions. |
| Outcome: | This tutorial aims to provide an overview of financial opinion mining and figure out research directions. |
MM-Claims: A Dataset for Multimodal Claim Detection in Social Media (2022.findings-naacl)
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Gullal Singh Cheema, Sherzod Hakimov, Abdul Sittar, Eric Müller-Budack, Christian Otto, Ralph Ewerth
| Challenge: | Using image and text, we investigate the role of image and texts in fake news detection . claim detection is a step in fighting misinformation and as a precursor to prioritize potentially false information for fact-checking. |
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Are You for Real? Detecting Identity Fraud via Dialogue Interactions (D19-1)
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| Challenge: | Existing methods to detect identity fraud are prone to errors and are not based on real data. |
| Approach: | They propose to use a KG constructor and structured dialogue management to detect identity fraud in loan applications to generate questions based on personal information. |
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Detecting and Reducing Bias in a High Stakes Domain (D19-1)
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| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
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Issues and Perspectives from 10,000 Annotated Financial Social Media Data (2020.lrec-1)
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| Challenge: | In the NLP community, many researchers have begun to use machine learning on financial and economic data. |
| Approach: | They present a dataset with 10,000 financial tweets annotated by experts from the front desk and the middle desk in a bank’s treasury. |
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What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
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| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
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Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena (L18-1)
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| Challenge: | a growing number of people consume news online, but there are different types of "fake news" many online news outlets use the same journalistic principles that have been in use for newspapers for decades, especially factchecking. |
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Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models (2025.emnlp-main)
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| Challenge: | Large language models can be used to attack content filtering algorithms in social media platforms. |
| Approach: | They propose to generate adversarial examples to test the robustness of social media content filtering algorithms. |
| Outcome: | The proposed model outperforms existing models in the case of propaganda, false claims, rumours and hyperpartisan news. |
Countering Misinformation via Emotional Response Generation (2023.emnlp-main)
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| Challenge: | Social media platforms (SMPs) are one of the most effective ways to spread misinformation by engaging in constructive dialogue with users who spread – often in good faith – misleading messages. |
| Approach: | They propose to use social correction to engage in constructive dialogue with users who spread misleading messages. |
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