Text Length and the Function of Intentionality: A Case Study of Contrastive Subreddits (2024)

Abstract: Text length is of central concern in natural language processing (NLP) tasks, yet it is very much under-researched. In this paper, we use social media data, specifically Reddit, to explore the function of text length and intentionality by contrasting subreddits of the same topic where one is considered more serious/professional/academic and the other more relaxed/beginner/layperson. We hypothesize that word choices are more deliberate and intentional in the more in-depth and professional subreddits with texts subsequently becoming longer as a function of this intentionality. We argue that this has deep implications for many applied NLP tasks such as emotion and sentiment analysis, fake news and disinformation detection, and other modeling tasks focused on social media and similar platforms where users interact with each other via the medium of text.

## introduction the relationship between the length, intentionality, register, genre, and emotion-associated word distributions in texts is a complex one. the genre often dictates the length for the sake of convention, such as with academic writing. the register (i.e. language as it is used in a specific situation and for a specific purpose) also affects text length, most obviously when there are platform limitations that impose maximum character counts such as sms messages or tweets, or based on the general pace of the platform. both of these effects are linked to and can artificially influence the intentionality and framing of a text. we define intentionality as a careful, self-curated production of text with a deliberate purpose or goal behind the communication. different text genres also force text producers to convey their message more succinctly and thus perhaps with more intentionality. examples of this include works of literature, poetry, and political speeches where the evocation of specific emotions in the reader is a desired effect achieved by carefully choosing the "right" words (see e.g. @xcite . we can see similar evocation tactics on social media too with, for example, "rage bait" 1 posts. despite the nearubiquitous presence of rage bait on social media, only a handful of academic papers have explored the topic, and these studies have been from a disinformation perspective rather than from an nlp or linguistic perspective (see e.g. jennings-roche, 2023; @xcite @xcite @xcite . in this study, we explore the functions of length, intentionality, affect, and register in contrastive pairs of corpora on the same topic. as our data, we use subreddits (topic-specific discussion forums on reddit) where at least two separate subreddits exist for the same topic and one is considered to be more serious or in-depth, and the other more general in nature. we hypothesize that (1) the average length of a text (post or comment) is longer in the more serious, in-depth subreddits, (2) the language in more serious subreddits has more variability and lexical density, and (3) that positive words carry less information than negative words and therefore the more serious subreddits have less positive words than their general subreddit counterpart showing a different aspect of negativity bias @xcite . this study also contributes to the discussion on best practices in how to work with the computational aspects of texts of varying lengths. ## background and related work many studies make off-handed mentions of the different nature of tweets as compared to other social media texts that are not artificially constrained by 1 rage bait is when social media content is perceived as having been carefully constructed to induce a maximal negative emotional response in readers for the sake of engagement. length to the same degree @xcite . some mention the "informal language and expressive content such as emojis and hashtags" @xcite , and others discuss the limited length, selfcontained nature, and the helpfulness of emojis and hashtags for annotators of tweets in contrast with reddit comments that tend not to include emojis and are often highly context and conversation dependent @xcite . for the most part, when social media message length is studied, the focus is on optimization for marketing purposes @xcite or crisis communication strategies @xcite . one interesting prior study looked into "perceived partner responsiveness" and found that longer messages were perceived as more intimate whether length was measured by counting tokens, characters, words or non-word characters @xcite . in this study we focus on word count because we examine word choice beyond length as a metric. perhaps the most famous law in quantitative linguistics, zipf's law @xcite describes the distribution of sorted measures. in terms of corpus linguistics, zipf's law states that when the words in a corpus are ordered by frequency, the value of the nth entry is inversely proportional to n. this frequency can also be used to measure the length of words and more recent studies have extended zip's law to state that information content causes word length to increase @xcite . @xcite studied the distribution of positive and negative words and their frequency as they relate to information density and word frequency overall. using small emotion lexicons (1034 entries for english) they found that word valence and frequency of use are related, and in particular that positive emotional content is more common than negative content. however, because positive words are more common, they point out that the relative rarity of negative words causes them to carry more information. more recently @xcite used readability and other linguistic complexity metrics to show that the negativity bias holds true for reddit data too meaning that negative emotions are associated with more complex texts than positive emotions. they continue on to show that current state-of-theart transformers such as bert have more trouble with the more complex texts and discuss the implications of this for the evaluation of emotion and sentiment analysis models. intentionality is a very understudied concept in nlp. here we use the term to mean a careful selection of words to achieve a desired affective reaction in the reader, i.e., affective rhetorical devices. intentionality has mostly been researched as part of the field of rhetoric (see e.g. @xcite and in social psychology for example, to examine how other people determine whether an action or message was intentional, but some have looked at the rhetoric and affect of political speech: teneva (2021) showed that emotional appeals in political discourse aimed at social solidarity, group identification, and shaping public opinion in the internet news discourse. their conclusion was that political emotions play an important role in modern argumentation. in quantitative linguistics, varieties of language that are defined by their situation of use and communicative function are called registers. while there are many approaches to register analysis (e.g. @xcite , the analysis of registers is generally founded on the idea that certain linguistic features are more or less well-suited to certain situational and functional concerns, and as such, registers tend to prefer and disprefer different sets of linguistic features depending on the situational and functional circumstances of the register. for instance, narrative registers might prefer features such as past-tense verb forms and third-person pronouns. while text length is commonly recognized as a confounding factor for linguistic analyses, text length itself is rarely the object of study in linguistics. however, @xcite analyzes comment length on reddit from the point of view of register. these studies show that the length of a text is not determined at random even in contexts where the author can in principle write a text of any length on reddit and other similar social media platform, when they are not constrained by genre conventions, publisher requirements or limitations of technology. instead, @xcite demonstrates that text length is closely linked with the idea of register. just like different communicative functions and situational concerns prefer linguistic features that are well-suited for the situation, so too do different communicative functions prefer text lengths that are similarly well-suited for the situation. furthermore, liimatta (2022) goes on to show that the associations between text length and register are not all universal: within reddit data, many subreddits can differ in terms of the role that text length plays in them. of particular relevance to the present study are the findings by @xcite ) that longer comments on reddit have higher frequencies of linguistic features associated with higher information density, such as nominalizations, as well as ones associated with a more complex argument structure, such as infinitives and certain modal verbs; whereas the frequencies of features associated with non-edited, casual, "on-line" production tend to be higher in shorter comments, including features such as contractions, subordinator that deletion, first-person singular pronouns, and private verbs. these results suggest that, in general, longer comments tend to be more informationally dense and more carefully edited, as opposed to shorter comments, which tend to be more casual, less informationally dense, and less edited. ## data we used the convokit reddit corpus @xcite to collect data from related but contrastive subreddits. we chose pairs of subreddits that are on the same topic but split into a general and a specific, typically more serious or professional, subreddit. the selection of the subreddits was made based on the authors' knowledge of the topics and the subreddits, with both authors agreeing on each pair of subreddits. the criteria here vary slightly per subreddit where the latter has much more stringent requirements for both posts and comments than the former. we attempted to pick subreddit pairs of similar relative standing within pairs, however, there are many differences between our chosen pairs. all of the subreddits can be considered to be niche or nerdy in some aspects, including the more general ones and therefore they might not be different enough from each other for some of the pairs. we expect the most significant differences to be shown in the pair r/starttrek and r/daystrominstitute because the first in the pair is a fairly popular topic of discussion in the mainstream consciousness. as a tv show, star trek has been one of the most long-lasting and successful franchises on tv since the 1960s and it is also easy for more casual viewers to form opinions and ask questions on the subreddits @xcite . compared to some of the other pairs, r/aviation and r/flying for example, r/aviation is meant for enthusiasts and r/flying for pilots. however, aviation enthusiasts are a more niche group to begin with compared to tv, and therefore it is unlikely that a large percentage of posts would be from the perspective of a more general public. similarly, although r/askhistorians is one of the most strictly moderated subreddits on reddit with stringent guidelines about what top-level posts must contain and r/askhistory was established as a more lax alternative with fewer citation criteria and the like, the questions and discussions remain rather similar. finally, we have the world of warcraft (wow)-related subreddits, that could be said to be somewhat niche despite their immense popularity because casual players, let alone the general public, are unlikely to be actively discussing the topic. we expect r/wow and r/wownoobs to be somewhat sim-ilar simply because r/wow is so large it is going to catch a large majority of posts that would be more suited for r/wownoobs, but we expect r/wow and r/competitivewow to show more differences on the general to in-depth axis similar to the star trek and aviation-related subreddits. the sizes of the corpora are not uniform and therefore the results were normalized by token count where applicable. ## method for type-token ratio calculations, we used the same token count as for normalization and lemmatized type counts. we used spacy for tokenization and lemmatization. no other preprocessing steps were taken. to calculate the distribution of emotionassociated words, we use the nrc emotion intensity lexicon @xcite which divides words into plutchik's 8 core emotion categories of anger, anticipation, disgust, fear, joy, sadness, surprise, and trust and associates a value between 0 and 1 for the intensity of the emotion. our method can therefore detect the difference between, e.g., annoyance, anger, and rage and be judged more akin to valence @xcite . although we measure the prevalence of emotion-associated words in all eight plutchik categories, we focus on joy and anger as the proxy emotions we expect to represent positive and negative in this context. ## results the average post/comment length in the less general subreddits are consistently longer than in the more general ones (see note that the average post length refers to all comments on posts, not just the original post. the difference in length is much larger if going by the original post only. we chose to merge the comments and posts here to show that the entire conversation on the in-depth subreddits is more complex, rather than just the starting post. because a higher type-token ratio (ttr) has been associated with registers with a higher information density (e.g. @xcite , and as such we might expect the less general subreddits to have a higher ttr, we calculated the moving average type-token ratios (mattr) for the subreddits. we chose mattr over ttr since the lengths of the posts between the subreddit pairs were just so drastically different that for example, for r/startrek a typical post would only have about 70 words of which between 55 and 60 were unique making the ttr values very high (.75 to .8), whereas r/daystrominstitute had lower ttr values typically between .35 and .45 because a typical post was between 800 and 1400 words of which 400-500 were unique. however, the mattr values for all the subreddits in question was between .80 and .81 suggesting that mattr was no better than ttr as a measure for information density or intentionality in our texts. in ## analysis and concluding discussion we found that the average length of a text in posts and comments were longer in the more serious, indepth subreddits at almost twice the length on average, with some significant deviations for the two history subreddits where the posts on the more serious subreddit were almost 15 times longer than on the less serious one. this finding supports the earlier results on the relationship between text length and register, which associated longer reddit comments with linguistic features related to information density and more carefully edited content. the world of warcraft subreddits differed here slightly with the post lengths on the general subreddit still about half the length of the more serious one (33.4 vs. 59.7), but with little difference between the competitive and beginner subreddits (57.4 vs. 59.7). this might be because the general subreddit is less specific and less moderated in terms of allowed our finding that the serious subreddits have both longer average comment lengths and features associated with higher information content, such as higher average word length, is also in line with the findings by @xcite correlating longer comment length on reddit with higher frequencies of linguistic features associated with densification of information, such as nominalizations, and shorter comments with features of more casual and personal, less carefully considered language, as well as @xcite that information content causes word length to increase. another difference became apparent when sorting the posts on the subreddits by all-time top posts; the top posts in the general subreddits tended to contain images or videos rather than the long essaylike posts in the more serious subreddits, further highlighting the differences between the general and niche. for the emotions, we did find that the posts in the more general subreddits tended to contain more joyrelated words at higher intensities than their more in-depth counterparts. however, the case for angerrelated words was not as clear-cut. the results indicate that r/daystrominstitute posts tend to contain more anger than r/startrek and the same can be said for r/wow when compared to r/competitivewow, but for the others, it seemed that the general sub-reddit posts contained more emotion-related words at higher intensities in general. in conclusion, we have shown that the length of a post differs based on what we consider intentionality, that is, the in-depth nature of the text with more niche conversation significantly longer than more general conversation on the same topic. we were unable to show a difference in lexical variation due to the short messages in the general subreddits, but the results support the idea that general discussions are more positive than the more complex, longer texts in the in-depth subreddits. ## future work the complex relationships between the length, intentionality, register, genre, and emotion-associated word distributions in texts remains and interesting and useful area of study. for instance, while we have demonstrated an indirect relationship between emotion-associated word distributions and certain types of register variation, insomuch as both of them are linked with text length, it would be fruitful to perform a deeper analysis of the connections between different register dimensions, emotionassociated words, and text length, to better understand this multifaceted problem. in future work we hope to include part-of-speech and syntactic structures as features related to register and intentionality. we also hope to study additional subreddits and better utilize both fine-tuned llms and qualitative analysis.