  Text-based misinformation permeates online discourses, yet evidence of
people's ability to discern truth from such deceptive textual content is
scarce. We analyze a novel TV game show data where conversations in a
high-stake environment between individuals with conflicting objectives result
in lies. We investigate the manifestation of potentially verifiable language
cues of deception in the presence of objective truth, a distinguishing feature
absent in previous text-based deception datasets. We show that there exists a
class of detectors (algorithms) that have similar truth detection performance
compared to human subjects, even when the former accesses only the language
cues while the latter engages in conversations with complete access to all
potential sources of cues (language and audio-visual). Our model, built on a
large language model, employs a bottleneck framework to learn discernible cues
to determine truth, an act of reasoning in which human subjects often perform
poorly, even with incentives. Our model detects novel but accurate language
cues in many cases where humans failed to detect deception, opening up the
possibility of humans collaborating with algorithms and ameliorating their
ability to detect the truth.
