Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

Hand and Mind: What Gestures Reveal about Thought

The Generation of Deceptive Messages: Laying the Groundwork for a Viable Theory of Interpersonal Deception

Online reviews influence consumers’ purchasing decisions. However, identifying fake online reviews automatically remains a complex problem, and current detection approaches are inefficient in preventing the spread of fake reviews. The literature on fake reviews detection lacks a comprehensive and interpretable theory-based model with high performance, which enables us to understand the phenomenon from a psychological perspective and analyze reviews based on user-generated content as well as consumer behavior. In this research, we synthesized ten well-founded deception theories from psychology, namely leakage theory, four-factor theory, interpersonal deception theory, self-presentational theory, reality monitoring theory, criteria-based content analysis, scientific content analysis, verifiability approach, truth-default theory, and information manipulation theory, and selected nine relevant constructs to develop a unified model for detecting fake online reviews. These constructs include specificity, quantity, non-immediacy, affect, uncertainty, informality, consistency, source credibility, and deviation in behavior. We characterized the selected constructs using verbal and non-verbal features to validate the proposed model empirically. Subsequently, we extracted features from the Yelp datasets and used them to train four machine learning algorithms, specifically Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. We demonstrated that quantity, non-immediacy, affect, informality, consistency, source credibility, and deviation in behavior are essential constructs for detecting fake reviews. To our surprise, we discovered that non-verbal features are more important than verbal features and that combining features from both types improves the prediction performance. Our theory-based model outperformed most of the state-of-the-art fake review detection models and yielded high interpretability and low complexity.

Fake Online Reviews: A Unified Detection Model Using Deception Theories

People are sometimes deceptive, meaning that they “intentionally, knowingly, or purposefully mislead another person” (Levine, 2014, p. 37), despite potential negative relational consequences (McCornack & Levine, 1990; Millar & Tesser, 1988) or harsh societal condemnation (Kleinmuntz & Szucko, 1984). In fact, people encounter deception on a daily basis (Serota, Levine, & Boster 2010). Sometimes this deception is especially destructive (Van Swol, Braun, & Malhotra, 2012). Therefore, many law enforcement agencies and academic disciplines are invested in the study of deception. Much of this research is conducted in order to uncover ways to detect deceptive messages and distinguish them from truthful ones. This deception detection research consistently yields the following three findings. First, that people are notoriously bad at distinguishing truthful messages from deceptive ones (Bond & DePaulo, 2006). Second, that people often over-estimate their ability to detect deception accurately (Burgoon & Levine, 2010). Lastly, people are more likely to judge a message as truthful rather than deceptive regardless of the message’s veracity (Levine, Kim, Park, & Hughs, 2006). However, despite these three consistent findings, deception detection research is primarily bifurcated into two different perspectives researchers take when examining the phenomenon of deception (Burgoon & Levine, 2010) which this report labels the dominant and new perspectives. These two perspectives greatly affect the results and implications of the deception detection research being conducted. Therefore, this report examines and discusses each perspective as well as their divergent and sometimes intersecting research streams. Following this discussion, some of the most notable implications for each perspective are listed. Then some remarkable applications of each perspective are also discussed. Finally, some future research directions are suggested. Such discussions lead to an enhanced understanding of deception detection research.

The state of deception detection research: two perspectives used to uncover deception detection methods

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Levi L. Smith

Author Tools

Chat about Author

Papers

The state of deception detection research: two perspectives used to uncover deception detection methods