The Veracity Problem: Detecting False Information and its Propagation on Online Social Media Networks

• URL: http://arxiv.org/abs/2409.03948v1
• Date: Fri, 6 Sep 2024 00:13:05 GMT
• Title: The Veracity Problem: Detecting False Information and its Propagation on Online Social Media Networks
• Authors: Sarah Condran,
• Abstract summary: This work aims to develop methods for effective detection of false information and its propagation. Firstly, we propose a framework that leverages multiple aspects of false information. Secondly, we propose a method to identify actors and their intent when working in coordination to manipulate a narrative.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting false information on social media is critical in mitigating its negative societal impacts. To reduce the propagation of false information, automated detection provide scalable, unbiased, and cost-effective methods. However, there are three potential research areas identified which once solved improve detection. First, current AI-based solutions often provide a uni-dimensional analysis on a complex, multi-dimensional issue, with solutions differing based on the features used. Furthermore, these methods do not account for the temporal and dynamic changes observed within the document's life cycle. Second, there has been little research on the detection of coordinated information campaigns and in understanding the intent of the actors and the campaign. Thirdly, there is a lack of consideration of cross-platform analysis, with existing datasets focusing on a single platform, such as X, and detection models designed for specific platform. This work aims to develop methods for effective detection of false information and its propagation. To this end, firstly we aim to propose the creation of an ensemble multi-faceted framework that leverages multiple aspects of false information. Secondly, we propose a method to identify actors and their intent when working in coordination to manipulate a narrative. Thirdly, we aim to analyse the impact of cross-platform interactions on the propagation of false information via the creation of a new dataset.

Related papers

• DisTrack: a new Tool for Semi-automatic Misinformation Tracking in Online Social Networks [46.38614083502535]
  DisTrack is designed to combat the spread of misinformation through a combination of Natural Language Processing (NLP) Social Network Analysis (SNA) and graph visualization. The tool is tailored to capture and analyze the dynamic nature of misinformation spread in digital environments.
  arXiv  Detail & Related papers  (2024-08-01T15:17:33Z)
• Online Auditing of Information Flow [4.557963624437785]
  We consider the problem of online auditing of information flow/propagation with the goal of classifying news items as fake or genuine. We propose a probabilistic Markovian information spread model over networks modeled by graphs. We find the optimal detection algorithm minimizing the aforementioned risk and prove several statistical guarantees.
  arXiv  Detail & Related papers  (2023-10-23T06:03:55Z)
• TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection [0.5261718469769447]
  Existing datasets often fall short, lacking the necessary diversity and alignment with the contemporary network environment. This paper introduces TII-SSRC-23, a novel and comprehensive dataset designed to overcome these challenges.
  arXiv  Detail & Related papers  (2023-09-14T05:23:36Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Analyzing and modeling network travel patterns during the Ukraine invasion using crowd-sourced pervasive traffic data [3.2198211356287674]
  In 2022, Ukraine is suffering an invasion which has resulted in acute impacts playing out over time and geography. This paper examines the impact of the ongoing disruption on traffic behavior using analytics as well as zonal-based network models.
  arXiv  Detail & Related papers  (2022-08-08T17:36:35Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets [11.911055871045715]
  This survey paper specially analyzed computer vision-based object detection challenges and solutions by different techniques. We examined general as well as tiny object detection-related challenges and offered solutions by historical and comparative analysis.
  arXiv  Detail & Related papers  (2021-07-16T14:33:31Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

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.