Online Deception Detection Refueled by Real World Data Collection
Table 5: Cross-domain Experimental Results Using Different Features, Recall/Precision/F1-score
['Features', 'Books', 'Health', 'Electronics', 'Movies', 'Macro Average']
features overall. Especially in the runs trained with three smaller domains, Health, Electronics, Movies, UP features are promising in deriving generalized deception detection classifiers. Next, we add each of the first three types of basic features on top of UP features. It turns out that POS features slightly improve the overall performance, while both Unigrams and LIWC features hurt the overall performance. (With New Features section) shows the cross-domain experimental results when we increasingly add two new types of linguistic features. We can see that the features modeling advertising language can clearly improve the performance of deception detection across the four classifiers trained on each domain, showing that advertising language is commonly seen across four domains. Furthermore, adding the syntactic complexity features can slightly improve both the macro-average recall and precision in deception detection. Long Short-Term Memory (LSTMs) Hochreiter and Schmidhuber LeCun et al. For comparison purposes, we also conduct cross-domain experiments using both LSTMs and CNNs trained on top of word2vec 300 dimensions word embeddings Mikolov et al. One explanation for the lower performance of neural nets on deception detection is that this task is not about understanding semantic meanings of reviews, rather, deception detection is about understanding and recognizing subtle syntactic or stylistic clues and footprints of deceptive writing. Our strong claim is that general rules for deception detection cannot be obtained continuing single-domain studies (in which simple unigram is the best) and we have shown that cross-domain experiments are promising in revealing general rules.