Vulnerable to misinformation?

Karduni, Alireza; Cho, Isaac; Wesslen, Ryan; Santhanam, Sashank; Volkova, Svitlana; Arendt, Dustin; Shaikh, Samira; Dou, Wenwen

doi:10.1145/3301275.3302320

Cited by 30 publications

(12 citation statements)

References 39 publications

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“…As suggested by Karduni et al [10], we aimed to allow users to evaluate bias and credibility on a spectrum as opposed to a binary choice. We elicit users' perceived bias as a continuous number between 0 (unbiased) and 1 (biased).…”

Section: Dependent Variables and Elicitation Methodsmentioning

confidence: 99%

“…In this study, we started with the same datasets used in [10,13] which included a collection of tweets from multiple mainstream sources of news, as well as multiple accounts labeled by third-party sources as being likely to produce hoaxes, propaganda, or in general fake news [36]. To create a dataset of tweets that include facial expressions, we processed the dataset mentioned above through several computational methods.…”

Section: Datasetmentioning

confidence: 99%

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Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Karduni¹,

Wesslen²,

Markant³

et al. 2021

Preprint

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Images are an indispensable part of the news content we consume. Highly emotional images from sources of misinformation can greatly influence our judgements. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of happy and angry facial images on users' decisions. In study 2, we focus on sources' systematic emotional treatment of specific politicians. Our results show that depending on the political orientation of the source, the cumulative effect of angry facial emotions impacts users' perceived content bias and source credibility. When sources systematically portray specific politicians as angry, users are more likely to find those sources as less credible and their content as more biased. These results highlight how implicit visual propositions manifested by emotions in facial expressions might have a substantial effect on our trust of news content and sources.

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Section: Dependent Variables and Elicitation Methodsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Karduni¹,

Wesslen²,

Markant³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In IB, misinformation is a good example of this principle’s application. A key characteristic of misinformation is its resemblance to real news (Karduni et al , 2019). Zhang et al (2019) stated that the believability of fake news is increased by mimicking reputable authors’ writing styles and using a tone often used in real news.…”

Section: Parallels Between Levine’s Truth-default Theory and Informat...mentioning

confidence: 99%

Default to truth in information behavior: a proposed framework for understanding vulnerability to deceptive information

Zimmerman

Njeri

Khader

et al. 2022

ILS

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Purpose This study aims to recognize the challenge of identifying deceptive information and provides a framework for thinking about how we as humans negotiate the current media environment filled with misinformation and disinformation. Design/methodology/approach This study reviews the influence of Wilson’s (2016) General Theory of Information Behavior (IB) in the field of information science (IS) before introducing Levine’s Truth-Default Theory (TDT) as a method of deception detection. By aligning Levine’s findings with published scholarship on IB, this study illustrates the fundamental similarities between TDT and existing research in IS. Findings This study introduces a modification of Wilson’s work which incorporates truth-default, translating terms to apply this theory to the broader area of IB rather than Levine’s original face-to-face deception detection. Originality/value False information, particularly online, continues to be an increasing problem for both individuals and society, yet existing IB models cannot not account for the necessary step of determining the truth or falsehood of consumed information. It is critical to integrate this crucial decision point in this study’s IB models (e.g. Wilson’s model) to acknowledge the human tendency to default to truth and thus providing a basis for studying the twin phenomena of misinformation and disinformation from an IS perspective. Moreover, this updated model for IB contributes the Truth Default Framework for studying how people approach the daunting task of determining truth, reliability and validity in the immense number of news items, social media posts and other sources of information they encounter daily. By understanding and recognizing our human default to truth/trust, we can start to understand more about our vulnerability to misinformation and disinformation and be more prepared to guard against it.

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“…When we consider the identification of deceptive news online -where humans often disagree on or challenge the judgements of others (Karduni et al, 2018(Karduni et al, , 2019Ott et al, 2011) -we need more rigorous evaluations of model decisions, with a focus on expected performance across varied or manipulated inputs. Our work examining reliability of performance when faced with linguistic variations is a step towards comprehensively understanding model robustness that may highlight inequalities in cases of failure.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

2021

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The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the nuanced stances and partisan sentiment towards entities of US politicians using Moral Foundation Theory, on two politically divisive issues -Gun Control and Immigration. We define the nuanced stances of the US politicians on these two topics by the grades given by related organizations to the politicians. To conduct this study, we first filter out 74k and 87k tweets on the topics Gun Control and Immigration, respectively, from an existing tweet corpus authored by US parliament members. Then, we identify moral foundations in these tweets using deep relational learning. Finally, doing qualitative and quantitative evaluations on this dataset, we found out that there is a strong correlation between moral foundation usage and politicians' nuanced stances on a particular topic. We also found notable differences in moral foundation usage by different political parties when they address different entities. c : SameIdeology(t1, t2) ∧ SameTopic(t1, t2)∧ SameTime(t1, t2) ∧ HasMF(t1, m) ⇒ HasMF(t2, m) 10 Rujun Han, Qiang Ning, and Nanyun Peng. 2019. Joint event and temporal relation extraction with shared representations and structured prediction. In

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Vulnerable to misinformation?

Cited by 30 publications

References 39 publications

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Default to truth in information behavior: a proposed framework for understanding vulnerability to deceptive information

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

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