Arnold, Christa L, Ph.D.

Richard, F Dan, Ph.D.

Stewart, Margaret L, Ph.D.

English

UNF Digital Commons

RESEARCH NEED & SIGNIFICANCE ❖ Misinformation has become “the major moral crisis of our times” (Berg, 2017). Deception and misinformation are ubiquitous online (Drounin, 2016).   ❖ Deception/misinformation is knowingly transmitting messages to a receiver with the intent to promote an untruthful belief or conclusion. (AI-Rodhan, 2017).  “… Misinform, mislead... deceive … distort…falsify…misrepresent…hedge, spin, lie (Walters, 2000).❖ The assault on truth including misinformation, fake news, and alternative facts have created a “moral panic and a threat to democratic life” (Berg, 2017).   ❖ Online users agree that deception/misinformation are prevalent on the Internet and are easily perpetrated without being caught (Caspi & Gorsky, 2006).❖ When a person creates an untruthful story or statement, it often results in a different pattern of language use (Newman, et al., 2003).❖ This has been a UNF DHI-Affiliated Project since 2021.DEVELOPING MEDIATED STATEMENT ANALYSIS❖ There is a lack of standard methodology to identify misinformation and suspicious, deceptive content online (Tsikerdekis, & Zeadally, 2014). ❖ Our research introduces Mediated Statement Analysis, or MSA, as a methodology to potentially identify deceptive interactions and digital misinformation when exchanging messages via mediated communication channels, including social media, mobile apps, and similar platforms. DEVELOPING MEDIATED STATEMENT ANALYSIS❖ Our project’s purpose is to explore mediated deception online by applying our methodology called Mediated Statement Analysis, or MSA. ❖ Nine (9) Mediated Statement Analysis (MSA) coding categories are developed (1) Word count, (2) detail & description, (3) exaggerated claims, (4) emotional & affective language, (5) excessive punctuation,  (6) +/- paradigm, (7) direct numerical references, (8) qualifiers, hedges, & absolutes, and (9) combination of  categories. CURRENT PROJECT STATUS❖ We have completed a pilot study analyzing 1,476 truthful and untruthful social media posts from 246 participants.❖ Our Phase 2 data set is gathered and comprises of roughly 3,480+ truthful and untruthful social media posts from 580+ participants for further analysis to test and validate our MSA methodology for deception/misinformation detection online.  NEXT STEPS & IMPLICATIONS❖ Using a combination of human coding and innovative artificial intelligence (AI) and machine learning techniques (e.g., classification models, decision-tree models and BERTopic models), these large sets of truthful-untruthful social media posts will be analyzed.❖ Individuals, organizations, and communities – online & offline -  have an increasing need to flag deception/misinformation online. ❖ Our project explores a potential identifier through MSA application.DECEPTION    DETECTIONONLINEWHAT’S NEXTJane Smith, Ph.D. University of North FloridaCollege NameJacksonville, FloridaApplying Mediated Statement An lysis to Detect Digital Deception and Misinformation OnlinePROJECT CONTACTS:Christa L. Arnold, Ph.D.       Margaret C. Stewart , Ph.D.              F. Dan Richard, Ph.D.Associate Professor, Communication Studies      Associate Professor, Communication Studies           Associate Professor, PsychologySchool of Communication      School of Communication              Department of PsychologyChrista.Arnold@unf.edu      m.c.stewart@unf.edu              drichard@unf.edu 

Applying Mediated Statement Analysis to Detect Digital Deception and Misinformation Online

https://digitalcommons.unf.edu/context/dhi/article/1052/viewcontent/DHI_Poster_2024_Detect_Digital_Deception_and_Misinformation_Online_.pdf

Applying Mediated Statement Analysis to Detect Digital Deception and Misinformation Online

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