Detection of fake news on CoViD-19 on Web Search Engines

• URL: http://arxiv.org/abs/2103.11804v1
• Date: Mon, 22 Mar 2021 13:07:26 GMT
• Title: Detection of fake news on CoViD-19 on Web Search Engines
• Authors: V. Mazzeo, A. Rapisarda and G. Giuffrida
• Abstract summary: After China reported the first cases of the new coronavirus (SARS-CoV-2), unreliable and not fully accurate information has started spreading faster than the virus itself. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through Search Engines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which misleading or even harmful, that has widely spread around the globe. Although Social Media are increasingly being used as information source, Web Search Engines, like Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through Search Engines. By using a real-world dataset associated with recent CoViD-19 pandemic, we first apply re-sampling techniques for class imbalance, then we use existing Machine Learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated Uniform Resource Locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URLs detection, can improve the efficiency and performance of classifiers. Based on these findings, we think that usage of both textual and URLs features can improve the effectiveness of fake news detection methods.

Related papers

• Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
  We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
  arXiv  Detail & Related papers  (2023-11-02T08:39:45Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
  Multiverse is a new feature based on multilingual evidence that can be used for fake news detection. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
  arXiv  Detail & Related papers  (2022-11-25T18:24:17Z)
• A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [3.42658286826597]
  Fake news detection is one of the most analyzed and prominent areas of research. Facebook, Reddit, WhatsApp, YouTube, and other social applications are gradually gaining attention in this emerging field. This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text.
  arXiv  Detail & Related papers  (2021-11-23T16:02:28Z)
• Cross-lingual COVID-19 Fake News Detection [54.125563009333995]
  We make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English) We propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content. Empirical results on our dataset demonstrate the effectiveness of CrossFake under the cross-lingual setting.
  arXiv  Detail & Related papers  (2021-10-13T04:44:02Z)
• A Study of Fake News Reading and Annotating in Social Media Context [1.0499611180329804]
  We present an eye-tracking study, in which we let 44 lay participants to casually read through a social media feed containing posts with news articles, some of which were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We also describe a follow-up qualitative study with a similar scenario but this time with 7 expert fake news annotators.
  arXiv  Detail & Related papers  (2021-09-26T08:11:17Z)
• Tackling COVID-19 Infodemic using Deep Learning [0.0]
  We try to detect and classify fake news on online media to detect fake information relating to COVID-19 and coronavirus. The dataset contained fake posts, articles and news gathered from fact checking websites like politifact whereas real tweets were taken from verified twitter handles.
  arXiv  Detail & Related papers  (2021-07-01T11:07:47Z)
• SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread? [5.64512235559998]
  Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. Unlike traditional media channels, social networks facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviors, attitudes and beliefs of the public.
  arXiv  Detail & Related papers  (2021-05-22T09:26:13Z)
• User Preference-aware Fake News Detection [61.86175081368782]
  Existing fake news detection algorithms focus on mining news content for deceptive signals. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
  arXiv  Detail & Related papers  (2021-04-25T21:19:24Z)
• Transformer based Automatic COVID-19 Fake News Detection System [9.23545668304066]
  Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic. We report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.
  arXiv  Detail & Related papers  (2021-01-01T06:49:27Z)
• Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News [67.53424807783414]
  Social media has greatly enabled people to participate in online activities at an unprecedented rate. This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
  arXiv  Detail & Related papers  (2020-04-03T18:26:33Z)

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.