Content Analysis of Topics and Hashtags about the Coronavirus in Social Media

Coronavirus pandemic caused changes in the daily lifestyle, such as reducing social interactions and creating social distancing. In this research, we pursue two goals. One is algorithmic content analysis of comments/posts in Persian related to the Coronavirus on two social media, namely Tweeter and Instagram. To this end, topic modeling is used as a method for content analysis to cluster the data into abstract topics. The other goal is finding the correlation between topics and hashtags in the comments/posts. To this end, we developed a corpus from these two social media. We found 24 abstract topics by algorithmic content analysis of this corpus and they were manually labeled to be comprehensive. According to the corpus and the statistical information of the extracted topics, it can be speculated that about 25% of the comments/posts in this corpus focused on political and social issues of the virus. 10 fine-grained topics which contained 35% of the comments were related to the Coronavirus itself and its pandemic property. This indicates the importance of the attention that has been paid to social media for informing and disseminating information. Furthermore, the hypothesis of existing correlation between topics and hashtags was studied from statistical point of view by using the Pearson correlation coefficient. For 20 topics, a high correlation score between topics and hashtags was found; but this correlation was not found for 4 topics. The outcome of this research can be used to increase the internal coherence of a text and to make the hashtags predictable.