Acknowledgements
Table III.17: MSE comparison of models for predicting aggregated article credibility rating with 10-fold cross-validation. Improvements are statistically significant with P-value < 0.0001.
['[BOLD] Model', '[BOLD] Only Title', '[BOLD] Title & Text']
Ch4S3SS4SSSx3 Ch4S4SS1SSSx2 -Ch5S3SS1SSSx4 Ch5S3SS1SSSx7 -Ch5S3SS2SSSx3 Ch5S3SS2SSSx6 Ch5S3SS3SSSx4 Ch5S3SS3SSSx5 Ch5S4SS2SSSx2 -Ch5S4SS3SSSx3 -Ch5T13 The language model, especially the bias and subjectivity features, is less effective using only the article titles due to sparseness. On the other hand, using the entire article text may lead to very noisy features. So including the first few paragraphs of an article is the “sweet spot”. For this, we made an ad-hoc decision and included the first 1000 characters of each article. With this setting, the language features made a substantial contribution to reducing the MSE.