Leveraging Joint Interactions for Credibility Analysis in News Communities
Table 6: MSE comparison of models for predicting users’ credibility rating behavior with 10-fold cross-validation. Improvements are statistically significant with P-value < 0.0001.
['[BOLD] Model', '[BOLD] MSE']
Our model (User SVR) achieved the lowest MSE and thus performed best. MSE Comparison: The text-based LFM considers title and text, and performs better than its predecessors. However, the User SVR model considers richer features and interactions, and attains 23% MSE reduction over the best performing LFM baselines. 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.