## 5 Results
### Sentiment Difference between Stakeholders (RQ2)  
We conducted the sentiment analysis, which resulted in a 78% prediction accuracy with our samples. We then used the classification result for further analysis. Figure 4 shows the sentiment distributions of posts in the six subreddits. Interestingly, none of the six distributions coincide with the normal distribution. Instead, there are sharp sentiment polarizations in the five subreddits, except for _r/funyonlineclasses_. Users seem to express intense and differentiated emotions towards online classes on Reddit. This result verified the previous finding that sharing beliefs and opinions to find others who share similar ideas is a significant motivation for Reddit users to post content [32]. Figures 5 and 6 display more detailed sentiment distributions of each subreddit, with a single post's sentiment value counted and indicated by a dot and bar. Figure 4: Sentiment distributions of the six subreddits. Figure 3: Word cloud of each topic. Compared with the other five subreddits, _r/funnyonlineclass_ illustrates the most normalized distribution, where most posts display neutral and slightly positive sentiment, which appears reasonable. Surprisingly, among the three subreddits composed of learners of different ages, the sentiment of _r/teenagers_ is the most gentle. The median sentiment value of this subreddit is neutral, even though a significant amount of sentiment is either very positive or negative. Yet, the other two subreddits, _r/college_ and _r/CollegeRant_, show extreme polarization. Notably, _r/college_ demonstrates a similar amount and extent of positive and negative emotions, whereas _r/CollegeRant_ shows a higher number of negative emotions. This result can be explained by this subreddit's topic on the negative aspects of college life. Figure 5: Box plot of posts’ sentiment distributions of six subreddits. Figure 6: KDE plot of posts’ sentiment distributions of six subreddits. On the educators' side, positive sentiment exceeds negative sentiment. Moreover, with a higher median sentiment value and higher density of positive sentiment value, _r/Teachers_ expresses stronger negative emotions than _r/Professors_. After cleaning and tokenizing the sentence in the two representative datasets, the sentiment of different words in the posts was calculated based on negative, neutral, and positive word classification. The negative words and positive words in the two datasets were ranked according to the frequencies. Further analysis was conducted on the learner and educator datasets to understand the two types of stakeholders. Figure 7 shows the kernel density estimate (KDE) plot of both sides for comparison. Overall, learners express more extreme sentiments, while educators tend to have more positive sentiments. The difference in motivations behind social media usage could explain this: educators generally use social media for self-development purposes, such as sharing resources and seeking collaborations with potential colleagues [22]. Figure 8 shows the sentiment of words constituting posts extracted and ranked by frequency. Notably, educators and learners shared a similar set of sentimental words, such as _bad, hate, shit, fail_, and _hell_, widely used for expressing negative feelings, and _best_, _love_, _great_, _kind_, and _free_ to express positive feelings in contrast. However, the term _cheat_ is particularly concerning since it ranks high on both sides. Figure 8: Word sentiment in educators and learners ranked by frequency. Figure 7: KDE plot of sentiment distributions of educators and learners. Posts regarding cheating were extracted, and Appendix A shows several representative examples. Educators observed widespread cheating behavior and this should be taken seriously as a systemic problem. Accordingly, we propose various practical approaches to prohibit cheating and care for learners' mental health. On the learners' side, motivations for cheating are complicated. It may be due to their difficulties studying online and fear of exam failure.