Linguistic Uncertainty and Engagement in Arabic-Language X (formerly Twitter) Discourse

TL;DR

This study investigates how linguistic uncertainty in Arabic-language tweets about Lebanon influences user engagement, revealing that uncertain tweets tend to receive significantly higher and more conversational interactions.

Contribution

It introduces a lexicon-based classifier for identifying uncertainty in Arabic social media and demonstrates its positive correlation with increased user engagement.

Findings

Uncertain tweets have 51.5% higher mean engagement.

Uncertainty correlates with approximately 25% higher expected engagement.

Engagement increases are strongest for replies, then retweets and likes.

Abstract

Linguistic uncertainty is a common feature of social media discourse, yet its relationship with user engagement remains underexplored, particularly in non-English contexts. Using a dataset of 16,695 Arabic-language tweets about Lebanon posted over a 35-day period, we examine whether tweets expressing linguistic uncertainty receive different levels and forms of engagement compared to certainty-marked tweets. We develop a lexicon-based, context-sensitive classifier to identify uncertainty markers and classify 29.9% of tweets as uncertain. Descriptive analyses indicate that uncertain tweets exhibit 51.5% higher mean total engagement (likes, retweets, and replies). Regression models controlling for tweet length, URL presence, and account verification status confirm a positive association between uncertainty and engagement (\b{eta} = 0.221, SE = 0.044, p < 0.001), corresponding to approximately 25% higher expected engagement. The association is strongest for replies, followed by retweets and likes, suggesting a shift toward more conversational forms of engagement. Results are robust to alternative model specifications and adjustments for within-account correlation. These findings suggest that linguistic uncertainty may function as an interactional cue that encourages participatory engagement in Arabic-language social media discourse. The study contributes computational approaches for modeling linguistic features in large-scale, non-English digital communication.