What signals beyond surface content indicate a passage caused a user's reaction?
This explores what tells you a passage actually moved a reader — beyond the literal words on the page — and the corpus points to causal structure across sentences, the reader's own position, and the appeal a text makes for attention.
This reads as a question about detection: if you want to know that a passage *caused* a reaction rather than just sat next to one, what do you look at besides the words themselves? The collection's strongest answer is that the signal lives in the relationships between statements, not in the statements. Work on predicting anxiety found that causal reasoning chained *across* sentences — how one claim is used to justify the next — predicts a reader's emotional state far better than any individual word does, because reactions like anxiety come from overgeneralizing through inter-statement logic, not from loaded vocabulary Why do discourse patterns predict anxiety better than single words?. So the first non-surface signal is structural: the shape of the reasoning a passage performs.
A second signal is the layered architecture of comprehension itself. Readers don't process text as a flat stream; they simultaneously track which segment they're in, what the writer intends, and what's currently salient — three layers that constrain each other, and a reaction is often a disruption in one of them rather than a response to content How do readers track segments, purposes, and salience together?. This reframes 'reaction' as something you can locate: a break in intentional or attentional tracking shows up even when the surface sentences look ordinary. Relatedly, human writing carries an internal *appeal to the reader's attention* as a built-in property, and its absence is exactly what readers register as the 'aloofness' of AI text — a reaction triggered by a structural absence, not by anything stated Does AI writing lack the internal appeal to attention that humans use?.
The twist worth knowing is that the same passage causes different reactions depending on who's reading, and that variance is itself a signal rather than noise. Interpretation modeling shows that disagreement on socially loaded sentences reflects real differences in reader social position — the distribution of reactions carries meaning Why do readers interpret the same sentence so differently?. So 'did this passage cause a reaction' isn't answerable in the abstract; the spread of responses across reader positions is the measurement.
Two more channels sit at the edges. Framing and tone can change what a passage does without changing its facts: emotional framing measurably shifts how text is received and even what information gets returned Does emotional tone in prompts change what information LLMs provide?. And reactions can be provoked by stylistic fingerprints the reader feels but can't name — accommodation to the prompt, textbook-perfect argument markers, persona distortions toward confidence and extremity — which are detectable through interpretable linguistic features even when the literal claims are unremarkable Can simple linguistic features detect AI-written arguments?, Does AI writing assistance change how readers perceive the writer?.
The through-line: the things that cause reactions are mostly invisible at the word level — they're causal links between sentences, the intentional and attentional layers underneath, whether the text reaches for you at all, and who you are when you read it. Surface content is the weakest predictor in the whole collection.
Sources 7 notes
Causal explanations across statements—not individual words—are the strongest predictor of anxiety because anxious thinking involves overgeneralization through inter-statement reasoning. A dual model combining both representation levels outperforms either alone.
Discourse processing demands parallel recognition of linguistic segments, intentional structure, and attentional salience—not sequential processing. These three layers constrain each other during comprehension, and failures in any single layer disrupt overall understanding.
Human writing contains an appeal to the reader's attention as a fundamental property of communication itself. AI-generated posts inherit platform visibility but do not perform this internal appeal, producing the reported aloofness readers perceive — a structural absence, not a stylistic defect.
Interpretation Modeling research shows that disagreement on socially embedded sentences reflects valid differences in reader perspective, not annotation failure. Structured human disagreement in NLI benchmarks confirms that interpretation distributions carry meaningful information.
GPT-4 exhibits emotional rebound (negative prompts yield ~86% neutral-positive responses) and a tone floor (positive prompts rarely go negative), causing identical questions to receive different answers depending on emotional framing. This bias is suppressed only on sensitive topics where alignment constraints override tone effects.
General linguistic features combined with argument-quality measures achieved 99% accuracy detecting LLM-generated counter-arguments on r/ChangeMyView, matching heavyweight neural detectors while remaining computationally cheap and transparent. LLMs produce detectable stylistic signatures: accommodation to prompts and textbook-quality argument markers that humans don't replicate.
A study of 2,939 writers and 11,091 readers found AI assistance shifted every tested dimension—29 total—toward extremism, confidence, quality, agreeableness, and perceived privilege. Distortions were statistically significant and directional, not random noise.