Article
Psychology, Multidisciplinary
Dorje C. Brody
Summary: This article proposes a modeling framework based on signal processing theory to describe the dynamics of systems driven by the unraveling of information, specifically focusing on the process of decision making. By quantifying the impact of information control, including the dissemination of disinformation, the study reveals that decision makers' perception hardly changes over time even when evidences indicate the alternative they heavily weight corresponds to a false reality, which suggests that confirmation bias and Bayesian updating can coexist. Additionally, the article introduces a new approach using noise to combat the dark forces of fake news.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Multidisciplinary Sciences
Gabor Orosz, Benedek Paskuj, Laura Farago, Peter Kreko
Summary: This online intervention study aimed to promote family-based prosocial values among young adults to build resistance against fake news. It is one of the first psychological fake news interventions in Eastern Europe, where the free press is weak and state-sponsored misinformation is prevalent. The intervention involved participants taking on an expert role and writing a letter to their less digitally competent relatives, explaining strategies to recognize fake news. The results showed immediate and sustained effects on fake news accuracy ratings and reduced receptivity to bullshit among the participants.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Review
Behavioral Sciences
Gordon Pennycook, David G. Rand
Summary: Research shows that people are better at discerning truth from falsehood when evaluating politically related news, poor truth discernment is mainly associated with lack of critical thinking and relevant knowledge. There is a notable disconnect between what people believe and what they share on social media, which can be addressed by nudging users to focus more on accuracy and leveraging crowdsourced veracity ratings to improve social media algorithms.
TRENDS IN COGNITIVE SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Pengfei Wei, Fei Wu, Ying Sun, Hong Zhou, Xiao-Yuan Jing
Summary: In this paper, a novel approach named Modality and Event Adversarial Networks (MEAN) is proposed for fake news detection. MEAN can effectively extract discriminant features from multiple modalities and improves the performance of fake news detection compared to state-of-the-art methods.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Computer Science, Information Systems
Liesbeth Allein, Marie-Francine Moens, Domenico Perrotta
Summary: The online audience of a news article provides valuable insights about its identity, but using this information for fake news classification may result in reliance on profiling. To address the increasing demand for ethical AI, a profiling-avoiding algorithm is proposed that leverages Twitter users for model optimization while excluding them during the evaluation of article veracity. This algorithm incorporates objective functions inspired by the social sciences to maximize correlation between the article and its spreaders, as well as among the spreaders. Experimental results demonstrate the positive impact of this approach in improving prediction performance and discriminatory capability between fake and true news.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Dimitrios Panagiotis Kasseropoulos, Paraskevas Koukaras, Christos Tjortjis
Summary: This paper assesses the accuracy of machine learning algorithms in detecting fake news and proposes a method of enhancing linguistic feature set using named entity recognition and association rule mining algorithm. Different training/test feature sets are provided by mixing document embeddings with linguistic features. The results show that convolutional neural network performs the best, but support vector machine achieves similar accuracy with a wider variety of input feature sets.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Psychology, Social
Alex Escola-Gascon, Neil Dagnall, Andrew Denovan, Kenneth Drinkwater, Miriam Diez-Bosch
Summary: This study investigated the psychological and psychopathological profiles of fake news consumers and found that individuals with paranoid, schizotypal, and histrionic personalities were more vulnerable to the negative effects of fake news, displaying higher levels of anxiety and cognitive biases. The study also suggested clinical and therapeutic recommendations for reducing the Barnum Effect and reinterpreting digital media sensationalism.
PERSONALITY AND INDIVIDUAL DIFFERENCES
(2023)
Article
Computer Science, Information Systems
Muhammad Imran Nadeem, Kanwal Ahmed, Zhiyun Zheng, Dun Li, Muhammad Assam, Yazeed Yasin Ghadi, Fatemah H. Alghamedy, Elsayed Tag Eldin
Summary: In recent years, there has been an increase in the number of fake news stories utilizing both textual and visual information. This paper proposes a method called Stylometric, and Semantic similarity oriented for Multimodal Fake News Detection (SSM), which consists of five distinct modules to analyze and classify the data. The results of testing the SSM framework on three standard fake news datasets show that it outperforms the baseline and state-of-the-art methods in detecting fake news in complex environments.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Shuo Li, Tao Yao, Saifei Li, Lianshan Yan
Summary: The increasing popularity of social media poses a major threat to the government and journalism due to the propagation of fake news. Detecting fake news from social media has become an urgent requirement. This study proposes a semantic-enhanced multimodal fusion network to better capture mutual features for fake news detection.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Mazhar Javed Awan, Awais Yasin, Haitham Nobanee, Ahmed Abid Ali, Zain Shahzad, Muhammad Nabeel, Azlan Mohd Zain, Hafiz Muhammad Faisal Shahzad
Summary: Before the internet, people relied on traditional media for news, but now with the rise of social media, the spread of fake news has become a significant issue. Machine learning models can successfully detect fake news and determine the accuracy of news in complex environments, with high accuracy rates when applied to different models.
Article
Chemistry, Multidisciplinary
Noman Islam, Asadullah Shaikh, Asma Qaiser, Yousef Asiri, Sultan Almakdi, Adel Sulaiman, Verdah Moazzam, Syeda Aiman Babar
Summary: This paper proposes a novel solution for detecting the authenticity of news through natural language processing techniques, consisting of three steps and utilizing various machine learning techniques, with the support vector machine algorithm achieving higher accuracy compared to other classifiers.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Tahir Ahmad, Muhammad Shahzad Faisal, Atif Rizwan, Reem Alkanhel, Prince Waqas Khan, Ammar Muthanna
Summary: The spread of rumors on social media during critical situations can have negative consequences for society. This study focuses on detecting and classifying these rumors, proposing new features and machine learning models that outperform existing baselines in terms of accuracy and effectiveness. This research provides valuable insights into combating misinformation on social media platforms.
APPLIED SCIENCES-BASEL
(2022)
Article
Multidisciplinary Sciences
Hanen Himdi, George Weir, Fatmah Assiri, Hassanin Al-Barhamtoshy
Summary: This paper addresses the issue of detecting fake news in the Arabic language. It introduces a supervised machine learning model for classifying Arabic news articles based on their context's credibility and presents the first dataset of Arabic fake news articles generated through crowdsourcing. The findings show that the performance of this model outperforms humans in the same task.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Lianwei Wu, Yuzhou Long, Chao Gao, Zhen Wang, Yanning Zhang
Summary: Fake news has a negative impact on our lives. Multimodal fake news detection has gained attention due to the growth of multimodal content in social media. Existing approaches focus on learning semantics and fusing modalities, but they have issues in shallow fusion and capturing inconsistent information. To address this, the MFIR model is proposed, which incorporates cross-modal infiltration fusion, multimodal inconsistent learning, and explanation reasoning for more effective and interpretable detection of fake news.
INFORMATION FUSION
(2023)
Article
Psychology, Multidisciplinary
Eugene Cho, S. Shyam Sundar
Summary: Online dating apps offer two different options for filtering potential partners: customization and personalization. A study found that combining customization and personalization led to users perceiving the highest proportion of date-worthy partners, surpassing both individual options.
COMPUTERS IN HUMAN BEHAVIOR
(2022)
Article
Psychology, Multidisciplinary
Jessica Gall Myrick, Katja Anne Waldron, Olivia Cohen, Carlina DiRusso, Ruosi Shao, Eugene Cho, Jessica Fitts Willoughby, Rob Turrisi
Summary: This study aimed to explore the ability of digital sun-safety interventions to affect self-control-related emotions and visual attention, as well as attitudes towards sun safety among young women. The findings revealed that sponsored stories on Instagram can promote sun-safety attitudes, depending on the emotional responses they generate.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Cybernetics
Eugene C. Snyder, Sanjana Mendu, S. Shyam Sundar, Saeed Abdullah
Summary: This study examines the impact of personalized voice assistant on user perceptions and experiences. The research finds that extroverted users prefer being matched with extroverted voice assistants, while introverted users do not have specific preferences. Users who customize their voice assistants perceive higher credibility. Automated similarity matching of voice assistants may evoke user resistance toward persuasive information.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
(2023)
Proceedings Paper
Computer Science, Cybernetics
Nasim Motalebi, Eugene Cho, S. Shyam Sundar, Saeed Abdullah
CONFERENCE COMPANION PUBLICATION OF THE 2019 COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW'19 COMPANION)
(2019)
Proceedings Paper
Computer Science, Cybernetics
S. Shyam Sundar, Jinyoung Kim, Eugene Cho
CHI EA '19 EXTENDED ABSTRACTS: EXTENDED ABSTRACTS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
(2019)
Article
Psychology, Social
Eugene Cho, Maria D. Molina, Jinping Wang
CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING
(2019)
Proceedings Paper
Computer Science, Cybernetics
Eugene Cho
CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
(2019)
Article
Psychology, Social
Eun-Ju Lee, Eugene Cho
CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING
(2018)
