A Study of Fake News Reading and Annotating in Social Media Context

• URL: http://arxiv.org/abs/2109.12523v1
• Date: Sun, 26 Sep 2021 08:11:17 GMT
• Title: A Study of Fake News Reading and Annotating in Social Media Context
• Authors: Jakub Simko, Patrik Racsko, Matus Tomlein, Martin Hanakova, Maria Bielikova
• Abstract summary: 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.
• Score: 1.0499611180329804
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterizing the fake news and devising automated methods for detecting them. The detection methods had so far only limited success, mostly due to the complexity of the news content and context and lack of properly annotated datasets. One possible way to boost the efficiency of automated misinformation detection methods, is to imitate the detection work of humans. It is also important to understand the news consumption behavior of online users. In this paper, 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. We present the description of both studies, characteristics of the resulting dataset (which we hereby publish) and several findings.

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)
• Interactively Learning Social Media Representations Improves News Source Factuality Detection [31.172580066204635]
  Rapidly detecting fake news, especially as new events arise, is important to prevent misinformation. We propose to approach this problem interactively, where humans can interact to help an automated system learn a better social media representation quality.
  arXiv  Detail & Related papers  (2023-09-26T14:36:19Z)
• Fake News Detection and Behavioral Analysis: Case of COVID-19 [0.22940141855172028]
  "Infodemic" due to spread of fake news regarding the pandemic has been a global issue. Readers could mistake fake news for real news, and consequently have less access to authentic information. It is challenging to accurately identify fake news data in social media posts.
  arXiv  Detail & Related papers  (2023-05-25T13:42:08Z)
• 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)
• Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [105.20743048379387]
  We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
  arXiv  Detail & Related papers  (2022-03-10T14:24:19Z)
• Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey [0.7120858995754653]
  In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue.
  arXiv  Detail & Related papers  (2022-01-20T07:43:15Z)
• 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)
• Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media [1.7616042687330642]
  We propose a model for detecting fake news using stance as one of the features along with the content of the article. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.
  arXiv  Detail & Related papers  (2021-05-21T10:46:43Z)
• 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)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)
• Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
  This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
  arXiv  Detail & Related papers  (2020-07-24T05:42:29Z)

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.