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Deception Detection In Videos

The most prevalent theme, tragedy, described rare incidents which led to unfortunate outcomes of some kind, as exemplified by a story of a death after an argument:. Luck was typically described as an unexpected fortune such as winning or finding something of value:. The identified categories are not necessarily mutually exclusive: annotations were made showing that stories often consist of more than one theme.

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For instance, in some examples evidence of luck is in the avoidance of tragedy. Money—related stories were not always about fortunes:. Supernatural themes included incidents of hearing voices or of alien abductions. The deception theme described those stories where the author is said to experience lying or being lied to.

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The rest of the less populous theme categories are named according to their subject matter: humor, sex, sports, or jealousy. Description was a distinct category since it did not offer a plot but rather described the characteristics of a setting or situation:. There were six cases of deception message themes Figure 4 within truthful stories. Speaking truthfully about lying seems to create some confusion among human perceivers and add complexity to detection automation. Like someone bleed to death.

Automatic Detection of Verbal Deception (Synthesis Lectures on Human Language Technologies)

This is a complex case in which the judges task may have an increased cognitive load due to the nested question — is he lying about lying? In sum, the identified message themes orient the reader to a topic and provide a thematic landscape for our dataset. They span across everyday and serious lies, as in DePaulo Message theme variability poses distinct problem for both automation and human discernment.

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The next four Facets B—E, Figure 3 address properties of deception within messages, taking into account the variable interpretation of truth T1—T4, Table 1. Deception centrality refers to what proportion of the story is deceptive, and how significant the deceptive part is. Messages vary from being entirely deceptive, to being deceptive in its focal point or in a minor detail Figure 3 , B.

Of the 90 messages, only 18 messages were confirmed by senders as being deceptive in their entirety. In the following example the sender claims all but minor details to be true:. Deception realism refers to how much reality is mixed in with the lie. A message can be based predominantly on reality with a minor deviation, or can be based on a completely imaginary world Figure 3 , C. Out of 90, 41 senders claimed in their verbal explanations that their messages were nothing but the truth. The entirely deceptive stories self—ranked 7 were often fiction—like:.

Deception essence refers specifically to what the deception is about, its nature Figure 3 , D , not to be confused with message theme or topicality. When explaining his truthful message, one of the senders felt compelled to clarify that his story was true in many respects, verbalizing how he could have lied, in principle, about the person or events:.

Similar testimonies lead us to believe that message senders are obviously aware of the underlying possibilities. We subdivided these types of deception essence into events, entities a collective term for people, organizations, and objects , and their characteristics referring to qualifiers of both, events and entities.

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The rest of the essence categories time, location, reason, degree, amount, etc. We hypothesize that certain combinations of centrality focal point , realism reality—based , and essence events , are more re—current than isolated uses of deception topics, and thus deserve special consideration in deception detection efforts. This is subject to future testing and targeted elicitations. The deceptive piece below describes how distortions of reality are details of events which are, nevertheless, focal to the message:.

Similarly, the sender who wrote Example 2 reveals the reality—based distortion Facet C of the event as the focal point Facet B :. Thus, each deception essence event, entity, characteristics, etc. In the case below, focal events are true but the entity the man is imaginary:.

Misattributions were three—fold. Events are also often misattributed to known entities e. Out of judgements, generated verbal explanations describing cues by which perceivers said they judged deception. The cues fell into four categories of this data—driven, perceived cue typology: world knowledge, logical contradiction, linguistic evidence and intuitive sense.

Sixteen percent vaguely referenced linguistic evidence, for instance, in regards to a story about cricket:. Five percent openly stated a decision based on intuition, relying on hunches, or impressions outside of empirical evidence. The perceivers tended not to be very descriptive in unpacking their reasons around their sense of deception:.

With the elicitation methods and experiment set—up detailed above, human judges achieved on average overall 50—63 percent success rates, depending on what is considered deceptive on the truth—deception continuum. In other words, the higher the actual deception level of the story, the more likely a story would be confidently assigned as deceptive.

This finding is consistent with the Undeutsch Hypothesis and current theory on qualitative differences in deceptive texts. Such combination makes the trend obvious, implying that even though humans are not as good at detecting overall deception; its extreme degrees are more transparent and thus obvious to judges. It would then make sense to focus further research on defining each category on the truth—deception continuum, and consequently eliciting more refined data. What would most people consider a half—truth as opposed to an absolute lie?

This supports the idea that further methods are needed to combine isolated predictors into more complex constructs. We further reflect on conceptual and methodological challenges in elicitation and analytical methods, and identify potential areas of improvements. The first challenge arises from interpretations of what constitutes deception.

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Half—truths and half—lies inevitably interfere with binary classification by humans. In general, humans performed better when there were fewer ambiguous cases e. It would be beneficial if people could defer to a machine—learning prediction in cases where they lack confidence, thus future research is needed on finding areas in which computational techniques supersede human judgments that lack confidence. Second, our data elicitation task was open-ended, allowing writers to make choices and us to charter their preferences and existing case scenarios.

This approach served an excellent purpose for surveying potential context and classifying deception facets such as theme, centrality, realism, essence, self—distancing. The heterogeneous data, however, lessened linguistic predictive power.

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In addition, since qualitative types of deception do not easily translate to linear deception level scales, we propose further research focuses on the following categories: events, entities or characteristics as a focal point of the story. This may have morphed the original intent of elicitations to an even broader, yet realistic, task.

Understanding and Detecting Deception - 6. Nonverbal Cues - A Look at the Nonverbal Cues of Liars

Two other broader challenges are associated with the experimental design. The relative unimportance of linguistic cues 16 percent is evident from our proposed cue typology. Contrary to the majority of current computational efforts at the lexico—semantic level e. Based on their survey, Park and colleagues found that only two percent of the lies are caught in real—time and are never purely on the basis of verbal and non—verbal behavior of the sender. On the other hand, if verbal cues are still detected objectively — based on data at hand and in real—time with some predictive power — automatic technique would have an upper hand over human efforts, ultimately proving invaluable, especially in CMC environments.

Finally, we offer an insight from the language pragmatics perspective. Human communication, including CMC, involves complex social phenomenon consisting of an interplay of language, shared frames of reference, and culturally specific contextual knowledge in order to convey meanings between the sender and receiver.

A thorough understanding of deception, therefore, must account for the pragmatic use of language. Without this relationship, the author is not subject to the cognitive demands associated with deception in more intimate scenarios. In our experiments, each sender wrote an unverifiable story for an anonymous recipient, with possibly inaccurate expectations about how the message would be received; each responder relied on an imaginary author and a subjective schema of the events being described.

Such elicitation conditions reduce the possibility of shared linguistic conventions and contextual knowledge that deception demands, and may undermine the ability of the data to meet the requirements of deception, or at least separate truth in a significant sense from deception as it occurs in a real—world communication scenario.

Deception detection is a challenging task. With pervasive use of text—based computer—mediated communication, automated deception detection continues to increase in importance in natural language processing, machine—learning communities and broader library and information science and technology.