💬 NLP

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

This study analyzes a dataset of 16,695 Arabic-language tweets about Lebanon to demonstrate that linguistic uncertainty significantly increases user engagement, particularly in the form of replies, suggesting that uncertainty functions as a conversational cue in non-English social media discourse.

Original authors: Mohamed Soufan

Published 2026-03-03

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Mohamed Soufan

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are walking through a giant, noisy digital town square (like X/Twitter) where people are shouting about current events in Lebanon. You notice something interesting: some people shout with absolute confidence ("This is definitely happening!"), while others sound a bit unsure ("I think this might be happening, but I'm not 100% sure").

This paper asks a simple question: Does sounding unsure make people more likely to talk back?

The researcher, Mohamed Soufan, decided to find out by looking at nearly 17,000 tweets about Lebanon over a month. Here is the story of what he found, explained simply.

1. The "Open Door" vs. The "Closed Box"

Think of a tweet that is certain (confident) as a closed box. It says, "Here is the fact. Take it or leave it." When you see a closed box, you might nod, click "like," or share it, but you probably won't stop to argue or ask questions. It feels finished.

Now, think of a tweet with linguistic uncertainty as an open door. It says, "I think this is the case, but what do you think?" or "Is this true?" or "I heard a rumor."

The Metaphor: Uncertainty acts like an invitation. It lowers the barrier to entry. It tells the audience, "Hey, I don't have the final answer, so come help me figure this out."

2. The Big Discovery: The "Reply" Explosion

The researcher found that tweets with these "open door" messages got 51% more total attention (likes, shares, and replies) than the confident "closed box" tweets.

But here is the most fascinating part: It wasn't just about getting more likes.

Likes are like a quick thumbs-up; they are passive.
Retweets are like passing a flyer to a friend; they are broadcast.
Replies are like starting a conversation at a party; they require effort and thinking.

The study found that uncertainty didn't just boost all numbers equally. It caused a massive spike in replies.

The Analogy: If a confident tweet is a billboard that people walk past and glance at, an uncertain tweet is a campfire. People gather around the campfire to talk, debate, and share their own stories. The "Uncertainty" signal turns a broadcast into a conversation.

The researcher calls this the "Uncertainty–Reply Asymmetry." It means that when someone says "I'm not sure," the crowd rushes to reply, not just to say "cool," but to say, "Here is what I know," or "I think you're wrong," or "I heard something else."

3. How Did They Measure This?

The researcher built a digital "detector" (a computer program) that read thousands of Arabic tweets. It looked for specific words that signal doubt, such as:

"Maybe" (Rubaama)
"I think" (A'qad)
"Who is responsible?" (Man huwa al-masool?)
"It is rumored" (Yuqal)

The computer flagged about 30% of the tweets as "uncertain." Then, it compared how many people interacted with those tweets versus the confident ones. Even after checking for other factors (like if the account was verified or if the tweet had a link), the "uncertain" tweets still won the popularity contest for conversation.

4. Why Does This Matter?

Usually, we think being unsure is a bad thing in news. We think it means the information is weak or fake.

The Old View: Uncertainty = Weakness.
The New View (from this paper): Uncertainty = Social Glue.

In a world where information changes fast (like during a political crisis), admitting you don't know everything actually makes people want to engage more. It makes the conversation feel like a group effort to solve a puzzle, rather than a lecture from an expert.

The Bottom Line

If you want to get people to listen and agree, be confident.
But if you want to get people to talk, debate, and participate, it helps to leave a little bit of the door open.

In the digital town square, a little bit of doubt is the best way to start a conversation.

1. Problem Statement

While prior research extensively analyzes how sentiment, emotion, and controversy drive social media engagement, the role of linguistic uncertainty (expressions of doubt, incompleteness, or lack of verified information) remains underexplored, particularly in non-English contexts.

The Gap: Existing studies often treat engagement as a unified metric (aggregated likes, shares, replies) and view uncertainty primarily as a deficit in information quality or a marker of misinformation.
The Hypothesis: The author posits that linguistic uncertainty may function not as a weakness, but as an interactional cue that invites audience participation, potentially shifting engagement from passive consumption (likes/retweets) to active conversational exchange (replies).

2. Methodology

Data Collection

Source: Publicly accessible tweets from X (formerly Twitter).
Topic: Discourse related to Lebanon (query: لبنان OR بيروت).
Language: Arabic (lang:ar).
Timeframe: 35 consecutive days (December 15, 2025 – January 18, 2026), a period of high political/economic volatility.
Sample Size: Initial collection of 17,343 tweets; after removing duplicates and filtering, the final analytical sample was 16,695 tweets.
Composition: 41.2% of the data consisted of original replies, ensuring a mix of broadcast and conversational content.

Linguistic Uncertainty Classification

Approach: A rule-based, lexicon-driven classifier designed specifically for Arabic.
Mechanism: The classifier uses a dictionary of 60 uncertainty markers grouped into six categories:
1. Modal verbs (e.g., maybe, perhaps).
2. Hedges (e.g., I think, seems).
3. Questions (e.g., what, why, how).
4. Contextual "Who" questions (e.g., who is responsible?).
5. Information uncertainty phrases (e.g., not sure, unclear).
6. Rumor markers (e.g., rumor, unconfirmed sources).
Context Sensitivity: Rules were implemented to avoid false positives (e.g., distinguishing the interrogative use of "من" from its prepositional use).
Validation: A stratified random sample of 200 tweets was annotated by a native Lebanese Arabic speaker.
- Performance: 73.5% accuracy, 1.00 recall, 0.47 precision, F1-score of 0.639.
- Error Profile: The classifier tended to over-predict uncertainty (false positives). The author argues this creates a conservative estimate, meaning the observed positive association between uncertainty and engagement is likely not inflated by classification errors.
Resulting Classification: 29.9% (4,997 tweets) were classified as "uncertain," while 70.1% (11,698 tweets) were "certain."

Statistical Analysis

Dependent Variable: Natural logarithm of total engagement (log(1 + Total Engagement)), where engagement = likes + retweets + replies.
Independent Variable: Binary indicator for linguistic uncertainty.
Control Variables: Tweet length (character count), URL presence (binary), and account verification status (binary).
Modeling:
- Primary Model: Ordinary Least Squares (OLS) linear regression.
- Robustness Check: Negative Binomial regression (to account for overdispersion in count data).
- Error Handling: Standard errors were clustered at the author level to account for within-account correlation (7,593 unique accounts).
Tools: Python 3.11 (pandas, numpy, scipy, statsmodels).

3. Key Results

Descriptive Findings

Volume: Uncertain tweets received 51.5% higher mean total engagement than certain tweets (23.67 vs. 15.62).
Breakdown by Type:
- Replies: +81.5% increase (2.16 vs. 1.19).
- Retweets: +65.2% increase (2.66 vs. 1.61).
- Likes: +47.0% increase (18.84 vs. 12.82).
Observation: The relative increase was highest for replies, suggesting uncertainty drives conversational engagement more than passive endorsement.

Regression Analysis

OLS Model: After controlling for length, links, and verification, uncertainty remained a significant positive predictor of engagement.
- Coefficient ( $\beta$ ): 0.221 ( $p < 0.001$ ).
- Interpretation: Uncertain tweets are associated with approximately 25% higher expected total engagement compared to certain tweets, holding other factors constant.
Negative Binomial Model: Confirmed the OLS results with an Incidence Rate Ratio (IRR) of 1.301, indicating a ~30% increase in expected engagement.
Model Fit: $R^2 = 0.049$ , consistent with the high unpredictability of social media engagement dynamics.

The "Uncertainty–Reply Asymmetry"

The study identifies a specific pattern where uncertainty disproportionately boosts replies compared to likes or retweets. This suggests uncertainty acts as an invitation for clarification, speculation, and collective sense-making rather than simple amplification.

4. Key Contributions

Computational Framework for Arabic NLP: Introduces and validates a context-sensitive, lexicon-based classifier for detecting linguistic uncertainty in Arabic, addressing a gap in non-English computational social science.
Reconceptualizing Engagement: Shifts the analytical focus from engagement volume (total count) to engagement composition (the mix of likes, retweets, and replies).
Theoretical Insight: Challenges the view of uncertainty as purely a credibility deficit. Instead, it frames uncertainty as an interactional cue that lowers barriers to participation and fosters conversational dynamics in digital public spheres.
Empirical Evidence: Provides large-scale evidence that linguistic form (uncertainty markers) systematically influences how users engage, not just how much.

5. Significance and Implications

For Computational Social Science: Demonstrates that qualitative linguistic features (epistemic stance) can be operationalized as measurable signals in large-scale digital communication models, even in underrepresented languages like Arabic.
For Information Behavior: Suggests that in high-uncertainty environments (e.g., political instability), users do not merely consume information but actively engage in collective sense-making. Uncertainty markers signal "openness," inviting the audience to fill informational gaps.
Platform Dynamics: Highlights that algorithms and user behaviors may treat uncertainty differently depending on the engagement type. Content designed to spark debate (high replies) may benefit from linguistic hedging, whereas content designed for viral spread (high retweets) might rely on different cues.
Limitations & Future Work: The study is observational (correlational, not causal) and relies on a rule-based classifier. Future research should employ supervised learning for better precision and investigate the specific qualitative content of the replies to understand the nature of the "sense-making" occurring.

Conclusion: The paper establishes that in Arabic-language discourse on X, linguistic uncertainty is a robust predictor of higher engagement, specifically driving a shift toward conversational participation (replies). This "Uncertainty–Reply Asymmetry" reveals that uncertainty functions as a social mechanism to invite audience contribution rather than a signal of low-quality information.