scholarly journals Adjusting the Drafter for COVID19: Re-designing our society’s understanding of misinformation

2021 ◽  
pp. 37-47
Author(s):  
Ajay Agarwal
Keyword(s):  
Cognitive Biases ◽  
Dark Period ◽  
Detection Accuracy ◽  
Ripple Effect ◽  
Fake News ◽  
Human Behaviors ◽  
Interdisciplinary Approaches ◽  
The Usa ◽  
Healthy Growth ◽  
Machine Learning Models

The pandemic of COVID19 illuminated the presence of our society’s cognition in a low-ceiling, inhabitable room, with almost little to none illumination of truth. Such a low-ceiling doesn’t only restrict the freedom of our cognition but also inhibits its healthy growth. Subsequently, our society feels a pushing sense, which is often exaggerated by the dark periods of misinformation, disinformation, and fake news. Hence, it becomes essential to rethink the interior designs of our cognition – How can we look at these periods of misinformation from a different lens? Can we use them to our advantage to make our room looks spacious enough for the growth of our cognition? Despite the limitations imposed to the ceiling length by our existing cognitive biases, there exist multiple, unconventional interdisciplinary approaches from the fields of epistemology, phenomenology, evolutionary psychology, and finally, the mathematics that we, as researchers, can leverage to broaden our understanding about the existing “misinfodemic” that presents as a ripple effect of COVID19 on our society’s cognition. The aim of this paper shall be the same – to present a noble discourse regarding the “dark period of misinformation” – why misinformation is NOT a pandemic but a widely-used misnomer, how the source of truthful information acts a source of misinformation, why misinformation is needed for the development of a better cognitive heuristic framework for our society, and finally, why such unconventional approaches fail to see the light of research. While the existing approaches to deal with misinformation spiral around machine-learning models competing with each other for better detection accuracy, this paper will take the reader right to the epicenter of “misinfodemic” using a variety of routes. Towards the end, the author provides how the mentioned approaches not only widen our understanding regarding the universal phenomenon of misinformation but also can be leveraged and scaled for irrational human behaviors like suicide, partisanship, and even student gun violence in the USA.

scholarly journals Adjusting the Drafter for COVID19: Re-designing our society’s understanding of misinformation

2021 ◽  
Author(s):  
Ajay Agarwal
Keyword(s):  
Cognitive Biases ◽  
Dark Period ◽  
Research Society ◽  
Detection Accuracy ◽  
Ripple Effect ◽  
Fake News ◽  
Human Behaviors ◽  
Interdisciplinary Approaches ◽  
Healthy Growth ◽  
Machine Learning Models

The pandemic of COVID19 illuminated the presence of our society’s cognition in a low-ceiling, inhabitable room, with almost little to no illumination of truth. Such a low-ceiling doesn’t only restrict the freedom of our cognition but also inhibits its healthy growth. Subsequently, our society feels a pushing sense, which is often exaggerated by the dark periods of misinformation, disinformation, and fake news. Hence, it becomes essential to rethink the interior designs of our cognition – How can we look at these periods of misinformation from a different lens? Can we use them to our advantage to make our room looks spacious enough for the growth of our cognition? Despite the limitations imposed to theceiling length by our existing cognitive biases, there exist multiple, unconventional interdisciplinary approaches from the fields of epistemology, phenomenology, evolutionary psychology, and finally, the mathematics that we, as researchers, can leverage to broaden our understanding of the existing “misinfodemic” that presents as a ripple effect of COVID19 on our society’s cognition. The aim of this paper shall be the same – to present a noble discourse regarding the “dark period of misinformation” – why misinformation is NOT a pandemic but a widely-used misnomer, how the source of truthful information acts as a source of misinformation, why misinformation is needed for the development of a better cognitive heuristic framework for our society, and finally, why such unconventional approaches fail to see the light of research. While the existing approaches to deal with misinformation spiral around machine-learning models competing with each other for better detection accuracy, this paper will take the reader right to the epicenter of “misinfodemic” using a variety of routes. Towards the end, the author provides how the mentioned approaches not only widen our understanding regarding the universal phenomenon of misinformation but also can be leveraged and scaled for irrational human behaviors like suicide, partisanship, and even student gun violence in the USA.Keywords: misinformation; psychology; interdisciplinary research; society; evolution

scholarly journals Cognitive Biases in Criminal Case Evaluation: A Review of the Research

2021 ◽  
Author(s):  
Vanessa Meterko ◽  
Glinda Cooper
Keyword(s):  
Cognitive Biases ◽  
Social Science Research ◽  
Science Research ◽  
Human Cognition ◽  
Criminal Case ◽  
Supporting Evidence ◽  
Case Evaluation ◽  
Complex Stimuli ◽  
The Usa ◽  
Specific Factors

AbstractPsychological heuristics are an adaptive part of human cognition, helping us operate efficiently in a world full of complex stimuli. However, these mental shortcuts also have the potential to undermine the search for truth in a criminal investigation. We reviewed 30 social science research papers on cognitive biases in criminal case evaluations (i.e., integrating and drawing conclusions based on the totality of the evidence in a criminal case), 18 of which were based on police participants or an examination of police documents. Only two of these police participant studies were done in the USA, with the remainder conducted in various European countries. The studies provide supporting evidence that lay people and law enforcement professionals alike are vulnerable to confirmation bias, and there are other environmental, individual, and case-specific factors that may exacerbate this risk. Six studies described or evaluated the efficacy of intervention strategies, with varying evidence of success. Further research, particularly in the USA, is needed to evaluate different approaches to protect criminal investigations from cognitive biases.

scholarly journals Fake news: emotion, belief and reason in selective sharing in contexts of proximity

2019 ◽  
Vol 15 (3) ◽  
pp. 590-613 ◽  
Author(s):  
João Carlos Correia ◽  
Pedro Jerónimo ◽  
Anabela Gradim
Keyword(s):  
New Media ◽  
Selective Exposure ◽  
Geographical Proximity ◽  
Fake News ◽  
Online Groups ◽  
The Usa ◽  
Media Ecosystem ◽  
The Uk ◽  
Los Grupos ◽  
Traditional Communities

This text addresses the phenomenon of so-called fake news in the new media ecosystem, namely in contexts of increasing influence of populist discourse and action, such as Brazil, the UK, the USA, Italy, among others. It does so by way of some characteristics already implicit in the limited effects theory: a) fake news involves, in a specific way, the participation of its receivers in disseminating and sharing it; b) producers/consumers (prosumers) are involved in contexts of proximity that facilitate selective exposure, perception, and memorization; c) these phenomena are joined by another (selective sharing): the stakeholders share ideas they agree with more intensely. Information bubbles reinforce existing beliefs and predispositions; d) the phenomenon is increased in contexts of proximity, be it geographical proximity provided by regional media or thematic and ideological proximity shared in online groups. Despite this, there is a difference between contexts of proximity in traditional communities and mechanisms of propaganda that have a significant level of organization and ideological polarization.Este texto aborda o fenômeno das chamadas fake news no novo ecossistema midiático, nomeadamente em contextos de aumento da influência do discurso e das ações populistas, como Brasil, Reino Unido, EUA, Itália entre outros, através de algumas características, já implícitas na teoria dos efeitos limitados: a) as fake news implicam, de um modo especial, a participação dos seus receptores na sua divulgação e dispersão; b) os produtores/consumidores (prosumers) estão envolvidos em contextos de proximidade que facilitam a exposição, percepção, memorização seletivas; c) a estes fenômenos acrescenta-se outro (partilha seletiva): os stakeholders compartilham com mais intensidade as ideias com que estão de acordo. As bolhas de informação reforçam crenças e predisposições já existentes; d) o fenômeno agrava-se em contextos de proximidade, seja esta a proximidade geográfica e temática proporcionada nos media regionais, seja a proximidade temática e ideológica partilhadas nos grupos online. Apesar disso, há uma diferença liminar entre os contextos de proximidade em comunidades tradicionais e os mecanismos de propaganda com forte índice de organização e mobilização ideológica.Este texto aborda el fenómeno de las llamadas fake news en el nuevo ecosistema mediático, a saber, en contextos de creciente influencia del discurso y la acción populistas como Brasil, EE.UU., U.K., Italia, entre otros, mediante algunas características implícitas en la teoria de los efectos limitados: a) las fake news implican, de modo especial, la participación de sus receptores en su divulgación y dispersión; b) los productores / consumidores (prosumers) participan en contextos de proximidad que facilitan la exposición, la percepción y la memorización selectiva; c) a estos fenómenos se añade otro (compartición selectiva): los stakeholders, quienes comparten con más intensidad las ideas con que están de acuerdo. Las burbujas de información refuerzan creencias y predisposiciones ya existentes; d) el fenómeno se agrava en contextos de proximidad, es decir, la proximidad geográfica y temática proporcionada en los medios regionales, sea la proximidad temática y ideológica compartida en los grupos online. Apesar de ello, hay una diferencia entre los contextos de proximidad en comunidades tradicionales y los mecanismos de propaganda con fuerte índice de organización y movilización ideológica.

scholarly journals Fake news and unfunded beliefs in the post-truth age

Universitas ◽  
2021 ◽  
pp. 87-108
Author(s):  
Víctor Castillo-Riquelme ◽  
Patricio Hermosilla-Urrea ◽  
Juan P. Poblete-Tiznado ◽  
Christian Durán-Anabalón
Keyword(s):  
Cognitive Biases ◽  
Female Gender ◽  
Male Gender ◽  
Fake News ◽  
Cross Sectional ◽  
Social Fabric ◽  
The Social ◽  
Paranormal Beliefs ◽  
The Face ◽  
The Relationship

The dissemination of fake news embodies a pressing problem for democracy that is exacerbated by theubiquity of information available on the Internet and by the exploitation of those who, appealing to theemotionality of audiences, have capitalized on the injection of falsehoods into the social fabric. In thisstudy, through a cross-sectional, correlational and non-experimental design, the relationship betweencredibility in the face of fake news and some types of dysfunctional thoughts was explored in a sampleof Chilean university students. The results reveal that greater credibility in fake news is associated withhigher scores of magical, esoteric and naively optimistic thinking, beliefs that would be the meetingpoint for a series of cognitive biases that operate in the processing of information. The highest correlationis found with the paranormal beliefs facet and, particularly, with ideas about the laws of mentalattraction, telepathy and clairvoyance. Significant differences were also found in credibility in fake newsas a function of the gender of the participants, with the female gender scoring higher on average thanthe male gender. These findings highlight the need to promote critical thinking, skepticism and scientificattitude in all segments of society.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

Credibility Perceptions and Detection Accuracy of Fake News Headlines on Social Media: Effects of Truth-Bias and Endorsement Cues

2020 ◽  
pp. 009365022092132
Author(s):  
Mufan Luo ◽  
Jeffrey T. Hancock ◽  
David M. Markowitz
Keyword(s):  
Social Media ◽  
Deception Detection ◽  
Detection Accuracy ◽  
Fake News ◽  
Default Theory ◽  
Impact Detection ◽  
Truth Bias ◽  
News Credibility ◽  
Biased Processing ◽  
News Headlines

This article focuses on message credibility and detection accuracy of fake and real news as represented on social media. We developed a deception detection paradigm for news headlines and conducted two online experiments to examine the extent to which people (1) perceive news headlines as credible, and (2) accurately distinguish fake and real news across three general topics (i.e., politics, science, and health). Both studies revealed that people often judged news headlines as fake, suggesting a deception-bias for news in social media. Across studies, we observed an average detection accuracy of approximately 51%, a level consistent with most research using this deception detection paradigm with equal lie-truth base-rates. Study 2 evaluated the effects of endorsement cues in social media (e.g., Facebook likes) on message credibility and detection accuracy. Results showed that headlines associated with a high number of Facebook likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. These studies introduce truth-default theory to the context of news credibility and advance our understanding of how biased processing of news information can impact detection accuracy with social media endorsement cues.

scholarly journals HMBI: A New Hybrid Deep Model Based on Behavior Information for Fake News Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jian Xing ◽  
Shupeng Wang ◽  
Xiaoyu Zhang ◽  
Yu Ding
Keyword(s):  
Social Behavior ◽  
Real World ◽  
Detection Accuracy ◽  
Challenging Problem ◽  
Fake News ◽  
Real World Data ◽  
Model Based ◽  
Deep Model ◽  
The Social ◽  
First Time

Fake news can cause widespread and tremendous political and social influence in the real world. The intentional misleading of fake news makes the automatic detection of fake news an important and challenging problem, which has not been well understood at present. Meanwhile, fake news can contain true evidence imitating the true news and present different degrees of falsity, which further aggravates the difficulty of detection. On the other hand, the fake news speaker himself provides rich social behavior information, which provides unprecedented opportunities for advanced fake news detection. In this study, we propose a new hybrid deep model based on behavior information (HMBI), which uses the social behavior information of the speaker to detect fake news more accurately. Specifically, we model news content and social behavior information simultaneously to detect the degrees of falsity of news. The experimental analysis on real-world data shows that the detection accuracy of HMBI is increased by 10.41% on average, which is the highest of the existing model. The detection accuracy of fake news exceeds 50% for the first time.

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