Reddit is a social platform that allows users to participate in user-created communities (called subreddits) by creating posts, commenting, and voting. The subreddits can be loosely categorized into two types.

For these two types of subreddits, we want to examine the difference in attitude towards negatively voted comments in these communities to uncover what emotions a user uses to express themselves when they read these comments that diverge from the community's values. Formally, we will be answering the following research question:

In addition to our main research question, we will also be answering the following sub-questions:

We will be using the Reddit text dataset in conjunction with the Reddit API and the NRC Emotion Lexicon to answer these questions.

1. How are these attitudes distributed across the two types of communities?

For the negatively voted comments in specialized and generalized communities, we want to find the distribution of their emotions. This means that for each comment in the dataset, we want to find its emotion vector, and combine these vectors to get the distribution of emotions for each community type.

In addition to these emotions, we will also have a polarity vector for each comment, describing whether the comment is Positive or Negative

Following this process, we get the following distributions of emotions in replies to negatively voted comments for the specialized and generalized subreddits

Emotion Sums

Looking at the emotion sums graphs for the specialized subreddits, we can see that the top 3 emotions are trust, anticipation, and joy, which are considered positive emotions.

If we look at the emotion sums graph for generalized subreddits, we can see that aside from trust, the other two emotions make up a smaller percentage of the total emotions. In the place of anticipation, we have fear and in place of joy, we have anger.

Polarity Sums

Looking at the polarity sums graphs, we observe analogous behaviour.

Specialized subreddits have a significantly higher percentage of replies that use positive language.

Generalized subreddits have only a slightly higher percentage of replies that use positive language, roughly indicating an even split.

From our observations of the emotion and polarity distribution for both specialized and generalized subreddits, we can make the following conclusions:

2. How do the attitudes of the replies relate to the attitudes of the initial comment?

In the previous section, we observed a difference in the emotion of the language used in replies to negatively voted comments.

In this section, we will explore the relationship between a negatively voted comment and their first reply. We will do so by calculating the frequency of the emotion and polarity scores of the replies compared to their corresponding comment.

For example, if a comment has a negative polarity, and its reply has a positive polarity, then we increment the count of negative to positive polarity by 1. We do an analogous process for the emotions, such that each emotion pair has a frequency count. Performing this process for both specialized and generalized subreddits, we get the following results:

Specialized Subreddits

We can see here that trust is the predomniant emotion in replies, regardless of the emotion of the comment. Hence, for our analysis, we can put less weightage on this emotion, and focus on the other emotions.

We can see that for most comments, the replies have a consistently high level of anticipation and joy. However, if we look at comments that contain emotions of anger or fear, the replies contain less anticipation and more fear. This is interesting, as even though the comment is angry, the replies contain language that contains more fear than anger. This shows that even if the comments deviate from the norm of the subreddit, and use negative emotions to express themselves, the replies will most likely respond with emotions of anticipation, joy and sometimes fear.

In the above graph, we can see the for comments that use positive or negative language, the replies to them are more likely to use positive language. In addition, it seems as though for comments that contain negative language, the replies are less likely to use negative language than comments that use positive language. This is interesting, as it contradicts our instinct that a negative comment would have a slightly higher chance of receiving a negative reply, and a positive comment to have slightly lower chance of receiving a negative reply.

Generalized Subreddits

Similar to specialized subreddits, we will chose to overlook trust.

In generalized subreddits, we can see that regardless of the emotion of the comment, the replies are much more likely to contain language of anger and fear. This seems to be the case even for comments that contain positive emotions. This means that even if the commenter uses positive emotions in their comment, as long as the comment deviates from the norm and values of the subreddit (as indicated by its negative score), the replies will respond with anger and fear.

In contrast to specialized subreddits, we can see the for comments that use positive language, the replies to them have more or less even odds of being positive or negative, with a positive reply being slightly more likely. However, for comments that use negative language, the replies still have more or less even odds, with a negative reply being slightly more likely.

From our observations of the emotion and polarity frequency for both specialized and generalized subreddits, we can make the following conclusions:

3. How do the attitudes differ in replies to positively voted comments?

To get the full picture about the distribution of attiudes to negatively voted comments in specialized and generalized subreddits, we must also look at their attitudes towards positively voted comments. This is because doing so will let us determine whether the patterns that we identified in section 1 are unique to negatively voted comments, or if they are also present in positively voted comments.

To keep our processes consistent, we will perform the same combination of emotion vectors, and polarity vectors on replies to positively voted comments. The results are shown below:

Emotion Sums

Looking at the emotion sums for both subreddits we can see that the replies predominantly use positive emotions such as trust, anticipation, and joy.

We can see that for specialized subreddits, the most used negative emotion is sadness compared to generalized subreddits, which have fear as the most used negative emotion.

We can also see that for specialized subreddits, the positive emotions make up a higher percentage of the total emotions as compared to generalized subreddits.

Polarity Sums

For polarity we see that both specialized and generalized subreddits have a greater percentage of positive replies as compared to negative replies, with specialized subreddits having a larger percentage.

It is quite interesting to see that although there is a greater percentage of positive replies in the generalized subreddits, we still have a significant amount of negative replies. This situation would be a trivial detail if we were looking at negatively voted comments. However, since we are looking at positively voted comments, this indicates that there is a significant amount of negative replies to positively voted comments in generalized subreddits.

Based on our analysis of the distribution of attitudes of replies to positively voted comments for specialized and generalized subreddits, we make the following conclusions:

Conclusion

In section 1, we were able to identify that replies to negatively voted comments in specialized communities would use a language that involves emotions of trust, anticipation, and joy, whereas for generalized subreddits, the replies contained emotions of trust, fear, and anger. We also observed the polarity to be significantly more positive in specialized subreddit replies as compared to generalized subreddit replies, where it was a more even split.

In section 2, we investigated the relationship between the emotions contained in the comment and the emotions of the replies. We found that regardless of the emotions or polarity of the comment, the replies were more likely to be positive. We also found that for generalized communities, the emotion and polarity of the comment had only a slight influence on the emotion and polarity of the replies. We observed that regardless of the emotion of the comment, the replies mostly consisted of emotions such as anger and fear. We also observed that the polarity remained relatively even, with it being only slightly more positive for comments with positive polarity and slightly more negative for comments with negative polarity.

In section 3, we conducted a similar analysis to section 1, but on positively voted comments. We did not find any clear distinction between emotion and polarity between specialized and generalized subreddits, which leads us to believe that the distinction exists only in the case of negatively voted comments.

By investigating the 3 sub-questions in the above 3 sections, we are able to conclude that replies to negatively voted comments in a specialized subreddit contain a more positive attitude, using language indicating trust, anticipation, and joy, as compared to replies in generalized subreddits.

References

I make use of the NRC-Emotion-Lexicon-Wordlevel-v0.92, created by Dr. Saif M. Mohammad, Dr. Peter Turney at the National Research Council Canada.