Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19

• URL: http://arxiv.org/abs/2006.01284v3
• Date: Tue, 30 Jun 2020 22:30:37 GMT
• Title: Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
• Authors: Zois Boukouvalas, Christine Mallinson, Evan Crothers, Nathalie Japkowicz, Aritran Piplai, Sudip Mittal, Anupam Joshi, and T\"ulay Adal{\i}
• Abstract summary: Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. We propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly.
• Score: 4.629100947762816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are based on machine learning have shown promise for the detection of misinformation, most widely used methods include approaches that rely on either handcrafted features that cannot be optimal for all scenarios, or those that are based on deep learning where the interpretation of the prediction results is not directly accessible. In this work, we propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly. To demonstrate the effectiveness of our method and compare its performance with deep learning methods, we developed a labeled COVID-19 Twitter dataset based on socio-linguistic criteria.

Related papers

• MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
  We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
  arXiv  Detail & Related papers  (2024-10-13T18:16:50Z)
• Countering Misinformation via Emotional Response Generation [15.383062216223971]
  proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and democracy. Previous research has shown how social correction can be an effective way to curb misinformation. We present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs.
  arXiv  Detail & Related papers  (2023-11-17T15:37:18Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Reinforcement Learning from Passive Data via Latent Intentions [86.4969514480008]
  We show that passive data can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions. Our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos.
  arXiv  Detail & Related papers  (2023-04-10T17:59:05Z)
• Representation Learning with Information Theory for COVID-19 Detection [18.98329701403629]
  We show how to aid deep models in discovering useful priors from data to learn their intrinsic properties. Our model, which we call a dual role network (DRN), uses a dependency approach based on Least Squared Mutual Information (LSMI) Experiments on CT based COVID-19 Detection and COVID-19 Severity Detection benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-07-04T14:25:12Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media [8.181808709549227]
  coordinated accounts on social media are used by misinformation campaigns to influence public opinion and manipulate social outcomes. We propose a coordination detection framework incorporating neural temporal point process with prior knowledge such as temporal logic or pre-defined filtering functions. Experimental results on a real-world dataset show the effectiveness of our proposed method compared to the SOTA model in both unsupervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2021-10-28T22:19:14Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media [7.194177427819438]
  This paper presents a mechanism to detect COVID-19 health-related misinformation in social media. We defined misinformation themes and associated keywords incorporated into the misinformation detection mechanism using applied machine learning techniques. Our method shows promising results with at most 78% accuracy in classifying health-related misinformation versus true information.
  arXiv  Detail & Related papers  (2021-06-12T16:26:04Z)
• From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects [4.178929174617172]
  An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for catching in advance hot information that deserves attention. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID
  arXiv  Detail & Related papers  (2020-12-13T16:12:28Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.