Automating Linguistics-Based Cues for Detecting Deception in Text-Based Asynchronous Computer-Mediated Communications

By Lina Zhou, Judee K. Burgoon, Jay F. Nunamaker Jr., D.P. Twitchell. 2004

451 citations recorded by the source corpus.

Subfields: Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/86b69472fea2e2b8462a3ca35ddd0b2f2ce72146

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Citations in the corpus

CLiPS Stylometry Investigation (CSI) corpus: A Dutch corpus for the detection of age, gender, personality, sentiment and deception in text (2014, background): “For a more thorough background on deception detection, see Ott et al. (2011) and Zhou et al. (2004) and references therein.”
The Dynamic Nature of Deceptive Verbal Communication (2006, methodology): “In previous research (Qin et al., 2004; Zhou et al., 2004), clusters of potential indicators, all of which could be automatically calculated with a shallow parser (Grok or Iskim) or could use a look-up dictionary, were considered.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…style of deceptive content across various types of information, e.g., online communications [Hancock et al. 2007; Pak and Zhou 2015; Rubin 2010; Zhou et al. 2004b], reviews [Li et al. 2014; Mukherjee et al. 2013b; Ott et al. 2011; Popoola 2018; Shojaee et al. 2013; Zhang et al. 2016],…”
Modelling users' trust in online health rumours: an experiment-based study in China (2021, background): “For instance, individuals may trust online rumours if there is more text and if pictures are present with that text (Zhou et al., 2004).”
Mutually Dependent: Power, Trust, Affect and the Use of Deception in Negotiation (2009, background): “Recent findings link negative emotions to increased deception (White and Burgoon, 2001; Zhou et al., 2004).”
Combating Fake News (2019, methodology): “The first cue set included was the Zhou/Burgoon set [130] comprising 14 linguistic based cues that were found effective for deception detection included percentage of first person singular and plural pronouns in the text, average word length, verb quantity, sensory ratio, spatial and temporal ratio,”
Impression Management and Retrospective Sense-Making in Corporate Narratives: A Social Psychology Perspective (2011, background): “…contain fewer references to others (Newman et al., 2003), (4) they contain more positive emotion words (Newman et al., 2003; Burgoon et al., 2003; Zhou et al., 2004), (5) they contain more negative emotion words (Newman et al., 2003; DePaulo et al., 2003), and (6) they contain fewer words…”
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (2017, background): “Linguistic aspects deception detection has been well-studied in a variety of NLP applications (Ott et al., 2011; Mihalcea and Strapparava, 2009; Jindal and Liu, 2008; Girlea et al., 2016; Zhou et al., 2004).”
The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language (2009, background): “Computational work includes the study of (Zhou et al., 2004), which studied linguistic cues for deception detection in the context of text-based asynchronous computer mediated communication, and (Hirschberg et al., 2005) who focused on de-ception in speech using primarily acoustic and prosodic…”
What Lies Beneath: The Linguistic Traces of Deception in Online Dating Profiles (2012, background): “Consistent with this prediction, previous research has documented the leakage of emotional linguistic cues even in asynchronous environments (Newman et al., 2003; Zhou et al., 2004).”
A Survey of Fake News (2020, methodology): “…1981], and readability (Table 4), which are initially developed for identifying deception in computer-mediated communications [Fuller et al. 2009; Zhou et al. 2004b] and testimonies [Afroz et al. 2012], and recently used in fake news detection [Bond et al. 2017; Pérez-Rosas et al. 2017; Potthast…”
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods (2010, unknown): “…measures, researchers found that non-naïve individuals assigned to be deceptive compared with non-naïve individuals assigned to be truthful or naïve individuals who were truthful used some language features that showed less diversity and complexity (Zhou, Burgoon, Nunamaker, & Twitchell, 2004).”