# Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic
## 4 Discussion  
In this paper, we performed an analysis of textual and visual features regarding the Instagram posts of the three vaccine-offering companies, during the onset period of the first vaccinations. Our results can be compared with other studies, e.g., [41], as we too found out that the companies post images of people to a large extent and users highly discuss the arrival of the vaccines and engage in opinion sharing with respect to their success. Our research has shown that only three companies have an active account on Instagram, i.e., Pfizer, Astrazeneca, and Johnson & Johnson. Astrazeneca has had an Instagram account longer than the other two, and has had the most posts and the highest rate of posting, but the lowest number of followers. By descriptive statistics, we infer that Johnson & Johnson post less frequently than Pfizer and Astrazeneca, but when they do post, their posts are lengthier. Moreover, their posts contain more hashtags, receive more likes and comments, thus having a greater impact on users. This can be attributed to the fact that Johnson & Johnson is a larger company with a wider range of products, e.g., cosmetics. Their posts additionally include more images and videos. Regarding the images posted, the study has shown that all three companies upload photos of their employees or other persons, classifying the images as clothing items or props, e.g., stethoscope or microscope. Moreover, the image classification outputs from the three models have been organized as a dataset enabling the quantitative and qualitative assessment of results, which is demonstrated and discussed. Astraz Zeneca mostly post synthetic images followed by images of humans. The same applies to Pfizer, with results however following a flatter distribution. Johnson & Johnson on the other hand, prefer posting images of humans, although their number of posts is significantly lower. With respect to the user posts, these are to a large extent written in English, with Spanish, Portuguese, and Italian following. After filtering out non-English language posts and preprocessing, the automatic annotation process has shown that the 'acknowledge' class is the largest, with 'expression' and 'advice' following and a very small size for the'seek' class. Thus, users' post intent was mainly devoted to making generic statements, reporting facts, and sharing their experiences, which in this context meant their experiences after vaccination. Users do not seem to be seeking help or advice about COVID-19 or vaccination process. For the predictive modelling application, results have shown that the best performing algorithms for intent classification, were equally Support Vector Machines and Random Forest, significantly better than the rest of the suite of algorithms examined. Finally, polarity analysis on users' posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts' distributions. Possible future extensions to this work include: investigation of other social media platforms, for example Facebook and Twitter; investigation of other vaccine producing companies, e.g., Moderna and augment the dataset with more recent posts; employment of automatic caption generation to retrieve the textual description of the image scene rather than mere object classification; performance of sentiment analysis on other affects as well, rather than just polarity; carrying out parameter tuning to improve model's performance, as in all of the algorithms used in this work we have mostly opted to stick to the default configurations. Due to the tremendous increase in the data volumes being produced [65; 66; 67; 68] and the big data explosion, an investigation on the use of big data from the social media platforms could be of great importance. Conceptualization, D.A., I.M. and I.K. ; Methodology, D.A., I.M., I.K. and A.S.; Software, D.A. ; Validation, D.A., S.M. and I.K. ; Formal Analysis, D.A., I.M., I.K. and A.S.; Investigation, D.A. and I.M. ; Resources, D.A. and I.M. ; Data Curation, D.A.