Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats (2024.lrec-main)
Copied to clipboard
Philipp Heinrich, Andreas Blombach, Bao Minh Doan Dang, Leonardo Zilio, Linda Havenstein, Nathan Dykes, Stephanie Evert, Fabian Schäfer
| Challenge: | Existing methods to identify and track conspiracy narratives are difficult to track and use because of their short-lived nature. |
| Approach: | They analysed 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. |
| Outcome: | The proposed methods compare well with off-the-shelf methods and human performance. |
Similar Papers
Multimodal Pipeline for Collection of Misinformation Data from Telegram (2022.lrec-1)
Copied to clipboard
| Challenge: | a large portion of misinformation is spread via multimodal means, such as images and videos . a new pipeline for collecting misinformation from Telegram allows us to collect a greater variety of mis-information examples . |
| Approach: | They propose to use AI to understand misinformation flow across social media platforms . they collect data from Telegram groups which promote COVID-19 misinformation . |
| Outcome: | The proposed dataset contains almost one million messages from 2k different public channels related to spreading COVID-19 misinformation. |
COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter (2021.acl-srw)
Copied to clipboard
| Challenge: | Social media is used by individuals and organisations as a platform to spread misinformation. |
| Approach: | They compile a large corpus of tweets related to coronavirus and perform an analysis to discover patterns with respect to vocabulary usage. |
| Outcome: | The proposed model based on lexical features is effective in identifying misinformation-related tweets with accuracy over 80%. |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
Copied to clipboard
Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)
Copied to clipboard
| Challenge: | a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus . |
| Approach: | They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots. |
| Outcome: | The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks. |
Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset (2021.acl-long)
Copied to clipboard
| Challenge: | Using supervised machine learning, we assess how solidarity discourses changed before and during the COVID-19 crisis. |
| Approach: | They use social scientific concept of solidarity and its contestation, anti-solidarity, as problem setting to assess how European solidarity discourses changed before and during COVID-19. |
| Outcome: | The proposed model outperforms the baseline classifier with expert annotations by 25 points, from 58% macro-F1 to almost 85%. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
Copied to clipboard
Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns (2024.acl-srw)
Copied to clipboard
| Challenge: | Existing methods for multilingual framing differ from those used in English-speaking world . framers often use loaded vocabularies to create political images or favor a particular point of view . |
| Approach: | They use eight years of Russian-backed disinformation campaigns to examine framing . they find that disinformation campaign consistently favors specific framers . |
| Outcome: | The proposed method underperforms and shows high disagreements in Russian-language articles . the proposed method is based on eight years of Russian-backed disinformation campaigns . |
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Various deep learning models have been successfully employed for this type of NLP task of text classification. |
| Approach: | They propose a mixed-domain transfer learning approach that only captures local context and exhibits poor generalization. |
| Outcome: | The proposed model captures local and global contexts, but lacks generalization . a combination of shallow network-based domain-specific models and convolutional neural networks can extract local and globally context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution. |
Document Classification for COVID-19 Literature (2020.findings-emnlp)
Copied to clipboard
| Challenge: | a global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide variety of fields. |
| Approach: | They analyze a LitCovid dataset to find out how classification models can help organize COVID-19 research papers. |
| Outcome: | The proposed model outperforms all baseline models on the LitCovid dataset . it also outperformed BioBERT and other models with micro-F1 and accuracy scores of 86% and 75% . |
Emotion analysis and detection during COVID-19 (2022.lrec-1)
Copied to clipboard
| Challenge: | 3,000 English tweets labeled with emotions are used to predict emotions during crises . authors propose semi-supervised learning to bridge this gap . |
| Approach: | They propose to use a dataset of 3,000 English tweets labeled with emotions . they propose semi-supervised learning to bridge this gap by analyzing unlabeled data . |
| Outcome: | The proposed model can be used to predict emotions in the context of COVID-19 . the proposed model performs better than other models using unlabeled data . |