We Built a Fake News & Click-bait Filter:What Happened Next Will Blow Your Mind!
Table 1: Words most strongly associated with the fake news class.
['[BOLD] Original word', '[BOLD] Translation', '[BOLD] PMI']
Fact-checking lexicon: Using lexicons of sentiment words has been shown to be very successful for the task of sentiment analysis Mohammad and Turney , and we applied the same idea to extract a fact-checking lexicon. In particular, we use point-wise mutual information (PMI) to find terms (words, word bi-grams, and named entities) that are highly correlated with the fake/factual news class. We calculated the PMI scores for uni-grams, bi-grams and on extracted named entities. We can see in the table some words that grab people attention, but are not very informative by themselves, such as mysterious or phenomenon. These words are largely context-independent and are likely to remain stable in their usage across different domains and even over an extended period of time. Thus, they should be useful beyond this task and this dataset.