An exploratory study suggests Bloom's taxonomy classification is the most promising NLP-based measure of question creativity, while new answer-based metrics are only loosely related to question complexity.
Transformer based Automatic COVID-19 Fake News Detection System
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abstract
Recent rapid technological advancements in online social networks such as Twitter have led to a great incline in spreading false information and fake news. Misinformation is especially prevalent in the ongoing coronavirus disease (COVID-19) pandemic, leading to individuals accepting bogus and potentially deleterious claims and articles. Quick detection of fake news can reduce the spread of panic and confusion among the public. For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our best approach is based on an ensemble of three transformer models (BERT, ALBERT, and XLNET) to detecting fake news. This model was trained and evaluated in the context of the ConstraintAI 2021 shared task COVID19 Fake News Detection in English. Our system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.
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Applying Text Mining to Analyze Human Question Asking in Creativity Research
An exploratory study suggests Bloom's taxonomy classification is the most promising NLP-based measure of question creativity, while new answer-based metrics are only loosely related to question complexity.