Finding Experiential Stress in Tweets by Utilizing both Explicit and Implicit Stress Data

Stress has become a major social issue. In Japan, many people have been tweeting or confessing their complaints and worries on microblogging sites such as Twitter. However, it is difficult to estimate how many people are truly suffering from stress. This is because some people express their complaints and worries without using the word "stress." Moreover, even if the word "stress" is explicitly written, it does not necessarily represent the author's experience of stress, which we call experiential stress.In this study, we built classifiers that find not only the experiential stress appeared in tweets containing the word "stress," but also that appeared in all tweets related to stress. To build such classifiers, first, we collected two datasets from the web in two different ways. One is a dataset of tweets that explicitly contain the word "stress," and the other is a dataset by collecting tweets with the hashtag "#stress." The former one (we call explicit stress data) is easy to collect on a large scale, but the latter one (we call implicit stress data) is not. We then employed the pre-training and fine-tuning framework of BERT. We used the explicit stress data for pre-training and implicit stress data for fine-tuning. We showed that a classifier built in this way can find experiential stress with higher accuracy than a classifier built using only implicit stress data.