Perception detection using Twitter
Vanja Ljevar, James Goulding, Alexa Spence, Gavin Smith · 2020
Patients' perceptions about their condition have a strong impact on not only adherence to medication, but also on how they view themselves in the light of their condition. Research implies that Twitter is a particularly rich source of perceptions, as patients frequently use internet for information sharing and support. However, Twitter contains a lot of noise in the form of tweets that do not relate to perceptions, but are rather generated to advertise research and corporate news and this kind of information could `pollute' perception analysis. This study examined methods that could be used to extract perception tweets, on the example of tweets related to asthma. We first demonstrated differences between perception and non-perception tweets in terms of their linguistic features, and then focused on filtering perceptions using the classification process. Results demonstrated that there is a significant difference between perceptions and non-perceptions: perception tweets are shorter, have less capital letters, less punctuation signs and less hashtags. These features also performed well in predicting perceptions. However, the bag of words approach had better results in distinguishing between perception and non-perception tweets and the best results were obtained using word-based frequency vectorization and by training a neural network based classifier. Future research could explore the synergy of these approaches.