Classifying sentiment in microblogs: is brevity an advantage?

Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs

Exploring Roles of Emotion in Fake News Detection

Detecting fake news is becoming widely acknowledged as a critical activity with significant implications for social impact. As fake news tends to evoke high-activating emotions from audiences, the role of emotions in identifying fake news is still under-explored. Existing research made efforts in examining effective representations of emotions conveyed in the news content to help discern the veracity of the news. However, the aroused emotions from the audience are usually ignored. This paper first demonstrates effective representations of emotions within both news content and users’ comments. Furthermore, we propose an emotion-aware fake news detection framework that seamlessly incorporates emotion features to enhance the accuracy of identifying fake news. Future work will include thorough experiments to prove that the proposed framework with the emotions expressed in news and users’ comments improves fake news detection performance