As with much of the deception detection literature, it’s always such a surprise to me how relatively modest the performance gains are. (Here, the predictive model doesn’t actually get above chance performance, for example — of course, neither do humans.) This underscores how difficult a problem deception detection from linguistic cues generally is (or at least, is currently).
For this paper, I appreciated seeing the incorporation of more sophisticated linguistic cues, especially those with more intuitive links to the psychological processes underlying deception (e.g., rhetorical elements representing both what the deceiver chooses to focus on and the chain of argument from one point to the next). I wonder if there’s a way to incorporate theory of mind considerations more concretely, perhaps via pragmatic inference linked to discourse properties (I have visions of a Rational-Speech-Act-style framework being useful somehow).
(1) I wonder if it’s useful to compare and contrast the deception process that underlies fake product reviews with the process underlying fake news. In some sense, they’re both “imaginative writing”, and they’re both about a specific topic that could involve verifiable facts. (This comes to mind especially because of the detection rate of around 90% for the fake product reviews in the data set of Ott et al. 2011, 2013, using just n-grams + some LIWC features).
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. 2011. Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (pp. 309-319). Association for Computational Linguistics.
Ott, M., Cardie, C., & Hancock, J. T. 2013. Negative Deceptive Opinion Spam. In HLT-NAACL (pp. 497-501).
(2) I really appreciated the discussion of the issues surrounding “citizen journalism”. I wonder if an easier (or alternative route) for news verification is considering a set of reports about the same topic in aggregate — i.e., a wisdom of the crowds approach over the content of the reports. The result is aggregated content (note: perhaps cleverly aggregated to be weighted by various linguistic/rhetorical/topic features) that reflects the ground truth better than any individual report, and thus would potentially mitigate the impact of any single fake news report. You might even be able to use the “Bluff the Listener” NPS news data R&al2015 used, though there you only have three stories at a time on the same topic (and two are in fact fake, so your “crowd” of stories is deception-biased).
(3) Something I’m interested in, given the sophistication of the RST discourse features — what are some news examples that a simplistic n-grams approach would miss (either false positives or false negatives)? Once we have those key examples, we can look at the discourse feature profiles of those examples to see if anything pops out. This then tells us what value would be added to a standard baseline n-gram model that also incorporated these discourse features, especially since they have to be manually annotated currently.