# Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic
## 3 Results  
Reverting to textual information, Table 3 displays the mean and standard deviation (in parentheses) values for the associated dataset features for the three companies. Although there is some difference in the number of posts for each company, it can be observed that while Johnson & Johnson is significantly less active in posting than the other two companies, they do post longer messages. Their messages also contain more hashtags, receive more likes and comments, and are richer (or at least as rich) in images and videos. Figure 6: Predicted image classes. Figure 7: Top-10 classes for the three companies. The official companies' posts are a means of gaining insight into how their social media strategy expresses their policies, how it informs and promotes their products and services. Some degree of user perception can be measured with likes and comments, as these are the most user-familiar ways of passive and active interaction. The number of likes does carry an inherent positive sign; however, comments do not necessarily do so. Thus, in order to measure users' perception we subsequently downloaded posts containing the three respective hashtags: #pfizer, #astrazeneca, and #inj. We opted to download these posts for December 2020 and onwards, as it is the month just before and during the first vaccinations took place. We also opted to download posts only from public accounts. The total number of anytime hashtag-containing posts are (as of 27 December 2020); 99.519 for #pfizer, 26.222 for #astrazeneca, and 118.282 for #inj. It is interesting that #inj is significantly adopted by users, although the company Instagram profile is not as active as the other two. This may be justified by the company's more recognizable brand and wider product range. For December 2020, we ended up with 646 public posts for #pfizer, 738 public posts for #astrazeneca, and 70 public posts for #inj. As before, using a similar Python script we have constructed a single dataframe, consisting of all the three hashtag containing user posts text, i.e., 1454 rows. The reason for doing so is partially due to #inj being underrepresented and to the fact that we would like to increase our sample size for training purposes, as discussed later. Having a first look at the user posts we observe that there are different post languages and that the use of emojis is quite frequent, as seen in Figure 8, so although emojis could be utilized, some preprocessing is deemed essential:  
Google's Python module CLD2 (Compact Language Detection) is based on a Naive Bayesian classifier and can identify up to three languages in a document and their associated probabilities. Keeping the highest probability for each post we detected its language, except for two unidentified language posts ('un') and results are shown in Figure 9:  
Detecting the English language for a post does not necessarily imply that it is completely free of non-English words and characters. In our study, this was shown later when we performed text 'cleaning'. Our first preprocessing step was to filter out non-English language posts, so the new dataframe consisted of 675 English language posts: 339 for #pfizer, 291 for #astrazeneca, and 45 for #inj. Our aim here was to classify users' communication intents. Intent classification differs from sentiment and opinion mining, as it focuses on futuristic action rather than the current state of feelings [56]. Four intent classes were specified, motivated by the works of [57,58] and were partially adopted and modified to fit in our context: